Brainwave Entrainment Beginning Guide

Brainwave Entrainment Beginning Guide Binaural Beats and Isochronic Tones

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Contents Articles Brainwave entrainment

1

Audio–visual entrainment

4

Binaural beats

8

Isochronic tones

14

Electroencephalography

15

Thalamus

33

Delta wave

41

Theta rhythm

46

Alpha wave

52

Beta wave

56

Gamma wave

57

Mu wave

61

Hypothalamus

66

Hippocampus

77

Neural oscillation

97

Sensorimotor rhythm

112

Sleep spindle

113

Biofeedback

115

Dreamachine

134

Mind machine

136

References Article Sources and Contributors

138

Image Sources, Licenses and Contributors

140

Article Licenses License

142

Brainwave entrainment

Brainwave entrainment Brainwave entrainment or "brainwave synchronization," is any practice that aims to cause brainwave frequencies to fall into step with a periodic stimulus having a frequency corresponding to the intended brain-state (for example, to induce sleep), usually attempted with the use of specialized software. It purportedly depends upon a "frequency following" response on the assumption that the human brain has a tendency to change its dominant EEG frequency towards the frequency of a dominant external stimulus. Such a stimulus is often aural, as in the case of binaural or monaural beats and isochronic tones, or else visual, as with a dreamachine, a combination of the two with a mind machine, or even electromagnetic radiation. [1]

History Enthusiasts of brainwave entrainment claim that it has been noted or used in one form or another for centuries (long before the invention of EEG equipment), from shamanistic societies' use of drum beats to Ptolemy noting in 200 AD the effects of flickering sunlight generated by a spinning wheel. In the 1930s and '40s, with then-new EEG equipment and strobe lights, William Grey Walter performed some of the first scientific research on the subject.[2] Later, in the 1960s and '70s, interest in altered states led some artists to become interested in the subject, most notably Brion Gysin who, along with a Cambridge math student, invented the Dreamachine.[3] From the 1970s to date there have been numerous studies and various machines built that combine light and sound. These efforts were aided by continued development of micro-circuitry and other electronic breakthroughs which allowed for ever more sophisticated equipment. One of the more frequently noted scientific results claimed for brainwave entrainment was the discovery of binaural beats, published in Scientific American in 1973 by Gerald Oster.[4] However, Oster's research actually makes no mention of brainwaves. With the development of isochronic tones by Arturo Manns, combined with more sophisticated equipment, these discoveries led to many attempts to use claimed brainwave entrainment techniques in the treatment of numerous psychological and physiological conditions.

Aural entrainment Binaural beats Binaural beats deserve special mention because of the manner in which the desired frequencies are obtained. Brainwave entrainment may be achieved when audio signals are introduced to the brain causing a response directly related to the frequency of the signal introduced, Binaural beats called binaural beats. Two tones close in frequency generate a beat frequency at the difference of the frequencies, which is generally subsonic. For example, a 495 Hz tone and 505 Hz tone will produce a subsonic 10 Hz beat, roughly in the middle of the alpha range. The "carrier frequency" (e.g., the 500 Hz in the example above), is also said by some to affect the quality of the transformative experience. Note that this effect is achieved without either ear hearing the pulse when headphones are used. Instead, the brain produces the pulse by combining the two tones. Each ear hears only a steady tone. Although some have claimed that these frequencies do provide help in treating certain medical conditions,[5] there is not a wide acceptance by the medical community to adopt the practice of brainwave entrainment for emotional/mental disorders. A fixed, constant frequency of synchronization is less helpful than techniques such as classical neurofeedback or learning meditation, which naturally generate brainwave frequencies that differ from person to person and may vary from minute to minute.

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Brainwave entrainment

2

Monaural beats Binaural beats were first discovered in 1839 by H. Dove, a German experimenter. At that time, binaural beats were considered to be a special case of monaural beats. Binaural beats are not the same as monaural beats. Binaural beats are perceived by presenting two Monaural beats different tones at slightly different pitches (or frequencies) separately into each ear. This effect is produced in the brain, not in the ears as with monaural beats. It is produced by the neural output from the ears and created within the olivary body within the brain, in its attempt to "locate" the direction of the sound based on phase.[6] Only monaural beats are the result of the arithmetic (vector) sum of the waveforms of the two tones as they add or subtract from one another, becoming louder and quieter and louder again.[6] Monaural and binaural beats are rarely encountered in nature, but in man-made objects, monaural beats occur frequently. For example, two large engines running at slightly different speeds will send "surges" of vibrations through the deck of a ship or jet plane. The lower pitched tone is called the carrier and the upper tone is called the offset.[6] Monaural beats occur in the open air and external to the ears. For example, when two guitar strings of slightly different frequencies are plucked simultaneously, monaural beats strike the ear as beats and therefore excite the thalamus, an action crucial for entrainment. Binaural beats played through loudspeakers become monaural beats.[6] Binaural tones require headphones to be effective.[7] To hear monaural beats, both tones must be of the same amplitude. However binaural beats can be heard when the tones have different amplitudes. They can even be heard if one of the tones is below the hearing threshold. Noise reduces the perceived volume of monaural beats whereas noise actually increases the loudness of binaural beats.[8]

Isochronic tones "Isochronic tones are evenly spaced tones which turn on and off quickly."[9] Unlike binaural and monaural beats, isochronic tones do not rely on the combination of two tones – the "beat" is created manually by turning a tone on and off. Widely regarded as the most effective tone-based method, isochronic beats produce very strong cortical responses in the brain. Many people who do not respond well to binaural beats often respond very well to isochronic tones. Isochronic tones are most effective using headphones.[7]

Isochronic tones

Music Modulation and Audio Filtering Modulating sound is a way to produce brainwave entrainment using something as complex as a musical track.[7] In effect, this is "embedding" brainwave entrainment into the audio. Any sound can be used, from nature sounds to white noise to a full classical symphony. [7] Modulation works by rhythmically adjusting a component in the sound. [7] For example, volume modulation would be used to increase and decreases the volume to create the rhythmic stimulus necessary for entrainment to occur. [7] The problem with modulation (above) is that it can often distort the audio, particularly when used with music or certain nature sounds like rain. [7] Frequency band selection solves this problem by selectively modulating certain parts of an audio file, instead of the whole of it. [7] The brainwave entrainment is embedded into a lower frequency range only – affecting parts of the bass, but leaving the mid and treble alone.[7] Frequency band selection can be used to affect only one part of a sound file. [7] Multiple

Brainwave entrainment frequency bands can also be selected. [7] Frequency band selection is an important advancement, allowing entrainment to be embedded into any sound file with virtually no negative effect on the existing audio.[7] Because it allows for much higher intensity levels, the effectiveness of the session is actually increased. [7]

Audio–visual entrainment Audio–visual entrainment (AVE), a subset of brainwave entrainment, uses flashes of lights and pulses of tones to guide the brain into various states of brainwave activity. AVE devices are often termed light and sound machines or mind machines. Altering brainwave activity may aid in the treatment of psychological and physiological disorders.

Notes [1] Ochs L (2007). "The Low Energy Neurofeedback System (LENS): Theory, Background, and Introduction". Journal of Neurotherapy: Investigations in Neuromodulation, Neurofeedback and Applied Neuroscience 10 (2-3): 5-39. doi:10.1300/J184v10n02_02. [2] http:/ / www. stanford. edu/ group/ brainwaves/ 2006/ theclinicalguidetosoundandlight. pdf [3] Allen, Mark (2005-01-20). "Décor by Timothy Leary" (http:/ / www. nytimes. com/ 2005/ 01/ 20/ garden/ 20mach. html?ex=1264050000& en=2ead60550b324624& ei=5088& partner=rssnyt). The New York Times. . Retrieved 2010-04-26. [4] http:/ / rawexplorations. com/ sites/ default/ files/ G%20Oster%20-%20Auditory%20Beats%20in%20the%20Brain. pdf "Auditory Beats in the Brain," Gerald Oster, 1973 [5] The Clinical Guide to Light and Sound, Thomas Budzynski, PhD (http:/ / www. stanford. edu/ group/ brainwaves/ 2006/ theclinicalguidetosoundandlight. pdf) [6] Entraining Tones and Binaural Beats, Dave Siever (http:/ / www. mindalive. com/ 1_0/ article 11. pdf) [7] Transparent Corporation (http:/ / www. transparentcorp. com/ products/ mindws/ entrainment_methods. php) [8] Oster, G. (1973). Auditory beats in the brain. Scientific American, X, 94–102. [9] Entraining Tones and Binaural Beats, David Siever (http:/ / www. mindalive. com/ articleeleven. htm)

External links • Auditory Driving as Ritual Technology: A Review and Analysis (http://www.stanford.edu/group/brainwaves/ 2006/AuditoryDrivingRitualTech.pdf) – Overview of entrainment techniques • Brainwave Entrainment to External Rhythmic Stimuli (http://www.stanford.edu/group/brainwaves/2006/ index.html) – Interdisciplinary research and clinical perspectives symposium (Stanford University) • The Clinical Guide to Sound and Light (http://www.stanford.edu/group/brainwaves/2006/ theclinicalguidetosoundandlight.pdf) By Thomas Budzynski, PhD

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Audiovisual entrainment

Audio–visual entrainment Audio–visual entrainment (AVE), a subset of brainwave entrainment, uses flashes of lights and pulses of tones to guide the brain into various states of brainwave activity. AVE devices are often termed light and sound machines or mind machines. Altering brainwave activity is believed to aid in the treatment of psychological and physiological disorders.

Introduction All of our senses (except smell) access the brain's cerebral cortex via the thalamus, and because the thalamus is highly innervated with the cortex, sensory stimulation can easily influence cortical activity. In order to affect brain (neuronal) activity, sensory stimulation must be within the frequency range of roughly 0.5 to 25 hertz (Hz). Touch, photic and auditory stimulation are capable of affecting brain wave activity. A large area of skin must be stimulated to affect brainwaves, which leaves both auditory and photic stimulation as the most effective and easiest means of affecting brain activity. Therefore, mind machines are typically in the form of light and sound devices.[1] Auditory or visual stimulation (AVS) can take a variety of forms, generating different subjective and clinical effects. The simplest form of stimulation is to present a series of random light flashes and/or sound pulses to a subject, such as from watching TV or cars drive by, and investigate the resulting subjective experiences or electroencephalography (EEG) effects. AVE, however, involves organized, repetitive stimulation at a particular frequency for a specific period of time, and the frequency of stimulation is reflected within the EEG. This is called "open loop" stimulation, or free-running entrainment, and is not contingent on monitoring brainwaves in any way. "Close loop" AVE would involve visual and auditory stimulation in response to one's EEG.[2]

History Clinical reports of flicker stimulation appear as far back as the beginning of the 20th century. Pierre Janet, at the Salpêtrière Hospital in France, reported that by having his patients gaze into the flickering light produced from a spinning, spoked wheel in front of a kerosene lantern, they showed a reduction in their anxiety and hysteria.[3] With the development of the EEG, Adrian and Matthews[4] published their results showing that the alpha rhythm could be "driven" above and below the natural frequency with photic stimulation. This discovery prompted several small physiological outcome studies on the "flicker-following response," the brain's electrical response to repetitive stimulation[5][6][7][8][9][10][11] As EEG equipment improved, so did a renewed interest in the brain's evoked response to photic and auditory entrainment and soon, a variety of studies were completed.[12][13][14][15][16][17][18] In 1956, W. Gray Walter published the first results on thousands of test subjects comparing flicker stimulation with the subjective emotional feelings it produced. Test subjects reported all types of visual illusions and in particular, the "whirling spiral" which was significant with alpha production.[19] In the late 1950s, as a result of Kroger's observations as to why US military radar operators often drifted into trance, Kroger teamed up with Sidney Schneider of the Schneider Instrument Company. They produced the world's first electronic clinical photic stimulator - the Schneider Brain Wave Synchronizer.[20] It had powerful hypnotic qualities and soon studies on hypnotic induction were published[21][22][23] A variety of companies developing AVE (light and sound) devices have been established since this time.

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Audiovisual entrainment

Physiology of Audio-Visual Entrainment AVE is believed to achieve its effects through several mechanisms simultaneously. These include: • • • • •

altered EEG activity dissociation/hypnotic induction limbic stabilization improved neurotransmitter production altered cerebral blood flow[24]

AVE consists of constant, repetitive stimuli of the proper frequency and sufficient strength to "excite" the thalamus and neocortex. These stimuli do not transfer energy directly into the cortex. The direct transmission of energy from AVE only goes so far as to excite retinal cells in the eyes and pressure sensitive cilia within the cochlea in the ears. The nerve pathways from the eyes and ears carry the elicited electrical potentials into the thalamus. From there, the entrained electrical activity within the thalamus is "amplified" and distributed throughout other limbic areas and the cerebral cortexes via the cortical thalamic loop. AVE involves the continuous electrical response of the brain in relation to the stimulus frequency plus the mathematical representation (harmonics) of the stimulus wave shape.[25]

Effects of Audio-Visual Entrainment AVE effects on the EEG are found primarily over the sensory-motor strip, frontally, and in the parietal lobe (somatosensory) regions and slightly less within the prefrontal cortex.[26] It is within these areas where motor activation, attention, executive function, and somatosensory (body) awareness is primarily mediated. Auditory entrainment (AE) is the same concept as visual entrainment, with the exception that auditory signals are passed from the cochlea of the ears into the thalamus via the medial geniculate nucleus, whereas visual entrainment passes from the retina into the thalamus via the lateral geniculate nucleus.[27] Eyes-closed AVE at 18.5 Hz has been shown to increase EEG brainwave activity by 49% at the vertex. At the vertex (with the eyes closed) AE has been shown to increase EEG brainwave activity by 21%.[28] Successful entrainment leads to a meditative, peaceful kind of dissociation, where the individual experiences a loss of somatic and cognitive awareness. However, it is possible for visual entrainment to trigger seizures.

Evidence of Sensory Effects of AVE Both Huxley[29] and Walter[30] were among the first to articulate the subjective correlates of photic stimulation. They described subjective experiences of incessantly changing patterns, whose color was a function of the rate of flashing. Between ten and fifteen flashes per second, Walter reported orange and red; above fifteen, green and blue; above eighteen, white and grey. Huxley also described enriched and intensified experiences when subjects were under the effects of mescaline or lysergic acid. In his view, the rhythms of the lamp interacted with the rhythms of the brain's electrical activity to produce a complex interference pattern, which is translated by the brain's perceptual circuits into a conscious pattern of color and movement. Glicksohn also reported on altered states of consciousness from photic driving and its relationship of self-perceived creativity.[31] Other studies have shown that stimulation can produce both transient and lasting changes in the EEG.[32][33] Collura articulated the relationship between the low-frequency and high-frequency components of the steady-state visual evoked potential as reflecting anatomically and physiologically distinct response mechanisms. Additional clinical studies explored the use of photic entrainment to induce hypnotic trance,[34][35] to augment anasthesia during surgery,[36] and to reduce pain, control gagging and accelerate healing in dentistry.[37] More recently, the induction of dissociation was explored, which aided the understanding of dissociative pathology and development of better techniques for relaxing people suffering from trauma and posttraumatic stress disorder.[38][39] Dissociation begins after approximately four to eight minutes from properly applied AVE. A restabilization effect occurs where muscles relax, electro-dermal activity decreases, peripheral blood flow stabilizes, breathing becomes

5

Audiovisual entrainment diaphragmatic and relaxed, and heart rates becomes uniform and smooth.[40] Visual entrainment alone, in the alpha frequency range (7–10 Hz), has been shown to easily induce hypnosis,[41] and it has been shown that nearly 80% of subjects entered into either a light or deep hypnotic trance within six minutes during alpha AVE.[42] AVE provides an excellent medium for achieving an altered state of consciousness.[43]

Treatment Implications of AVE A review of 20 studies on brainwave entrainment found that it is effective in improving cognition and behavioral problems, and alleviating stress and pain.[44] The results of a study on children with attention-deficit disorder found that AVE was more effective than neurofeedback for treating ADD symptoms.[45] A migraine headache study involving seven migraine sufferers found that AVE sessions reduced migraine duration from a pretreatment average of six hours to a posttreatment average of 35 minutes. Measuring 50 of the participants' migraines, 49 migraines decreased in severity and 36 were stopped when using AVE.[46] Another clinical study showed declines in depression, anxiety and suicidal ideation following a treatment program using AVE.[47] A study by Berg and Siever used audio-visual entrainment devices on women suffering with seasonal affective disorder. Both depression and anxiety symptoms were reduced in participants, as compared to a placebo phase. Participants also reported improvements in their social lives, with increased happiness and sociability, decreased appetite, increased energy and weight loss.[48] A study by Cantor and Stevens found significant decreases in depression scores in participants after four weeks of using AVE.[49] A study by Thomas and Siever showed that many people with chronic temporomandibular joint disorder (TMD) brace up when asked to relax. AVE at 10 Hz produced deep masseter muscle relaxation and finger warming within six minutes.[50] Audio entrainment has shown promise as a singular therapeutic modality for treating jaw tension and TMD pain.[51] AVE has been used to reduce jaw pain, patient anxiety and heart rate during dental procedures.[52]

References [1] Siever, D. (2007) Audio-visual entrainment: history, physiology, and clinical studies. Handbook of Neurofeedback: Dynamics and Clinical Applications, Chapter 7 (pp. 155-183) Binghamton, NY: The Haworth Medical Press. [2] Collura, T. & Siever, D. (2009) Audio-visual entrainment in relation to mental health and EEG. In J.R. Evans & A. Abarbanel (Eds.) Quantitative EEG and Neurofeedback (2nd Ed.) (pp. 155-183) San Diego, CA: Academic Press. [3] Pieron, H. (1982) Melanges dedicated to Monsieur Pierre Janet. Acta Psychiatrica Belgica, 1, 7-112. [4] Adrian, E. & Matthews, B.(1934) The Berger rhythm: potential changes from the occipital lobes in man. Brain, 57, 355-384. [5] Bartley, S. (1934) Relation of intensity and duration of brief retinal stimulation by light to the electrical response to the optic cortex of the rabbit. American Journal of Physiology, 108, 397-408. [6] Bartley, S. (1937) Some observations on the organization of the retinal response. American Journal of Physiology, 120, 184-189. [7] Durup, G. & Fessard, A. (1935) L'electroencephalogramme de l'homme (The human electroencephalogram). Annale Psychologie, 36, 1-32. [8] Jasper, H.H. (1936) Cortical excitatory state and synchronism in the control of bioelectric autonomous rhythms. Cold Spring Harbor Symposia in Quantitative Biology, 4 (2), 9-15. [9] Goldman, G., Segal, J., & Segalis, M. (1938). L'action d'une excitation intermittente sur le rhythme de Berger (The effects of intermittent excitation on the Berger rhythms) C.R. Societe de Biologie Paris, 127, 1217-1220. [10] Jung, R. (1939) Das Elektroencephalogram und seine klinische anwendung (The electroencephalogram and its clinical application) Nervenarzt, 12, 569-591. [11] Toman, J. (1941) Flicker potentials and the alpha rhythm in man. Journal of Neurophysiology, 4, 51-61. [12] Barlow, J. (1960) Rhythmic activity induced by photic stimulation in relation to intrinsic alpha activity of the brain in man. Electroencephalography and Clinical Neurophysiology, 12, 317-326. [13] Van Der Tweel, L., & Lunel, H. (1965) Human visual responses to sinusoidally modulated light. Electroencephalography and Clinical Neurophysiology, 18, 587-598. [14] Kinney, J.A., McKay, C., Mensch, A., & Luria, S.(1973) Visual evoked responses elicited by rapid stimulation.Electroencephalography and Clinical Neurophysiology, 34, 7-13. [15] Townsend, R. (1973) A device for generation and presentation of modulated light stimuli. Electroencephalography and Clinical Neurophysiology, 34, 97-99.

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Audiovisual entrainment [16] Donker, D., Njio, L., Storm Van Leewan, W., & Wieneke, G. (1978) Interhemispheric relationships of responses to sine wave modulated light in normal subjects and patients. Electroencephalography and Clinical Neurophysiology, 44, 479-489. [17] Frederick, J., Lubar, J., Rasey, H., Brim, S., & Blackburn, J. (1999) Effects of 18.5 Hz audiovisual stimulation on EEG amplitude at the vertex. Journal of Neurotherapy, 3 (3), 23-27. [18] Chatrian, G., Petersen, M., & Lazarte, J. (1959) Response to clicks from the human brain: some depth electrographic observations. Electroencephalography and Clinical Neurophysiology, 12, 479-489. [19] Walter, W.G. (1956) Color illusions and aberrations during stimulation by flickering light. Nature, 177, 710. [20] Kroger, W.S., & Schneider, S.A. (1959) An electronic aid for hypnotic induction: a preliminary report. International Journal of Clinical and Experimental Hypnosis, 7, 93-98. [21] Lewerenz, C. (1963) A factual report on the brainwave synchronizer. Hypnosis Quarterly, 6(4), 23. [22] Sadove, M.S. (1963) Hypnosis in anaesthesiology. Illinois Medical Journal, 39-42. [23] Margolis, B. (1966) A technique for rapidly inducing hypnosis.CAL (Certified Akers Laboratories), June, 21-24. [24] Siever, D. (2007) Audio-visual entrainment: history, physiology, and clinical studies. Handbook of Neurofeedback: Dynamics and Clinical Applications, Chapter 7 (pp. 155-183) Binghamton, NY: The Haworth Medical Press. [25] Siever, D. (2007) Audio-visual entrainment: history, physiology, and clinical studies. Handbook of Neurofeedback: Dynamics and Clinical Applications, Chapter 7 (pp. 155-183) Binghamton, NY: The Haworth Medical Press. [26] Siever, D. (2007) Audio-visual entrainment: history, physiology, and clinical studies. Handbook of Neurofeedback: Dynamics and Clinical Applications, Chapter 7 (pp. 155-183) Binghamton, NY: The Haworth Medical Press. [27] McClintic, J. (1978). Physiology of the human body. John Whiley & Sons, New York, NY. [28] Frederick, J.A., Timmerman, D.L., Russell, H.L, & Lubr, J.F. (1999) Effects of 18.5 Hz audiovisual stimulation on EEG amplitude at the vertex. Journal of Neurotherapy, 3(3), 23-27. [29] Huxley, A. (1954) The doors of perception/heaven and hell. New York: Harper & Row, 1963 edition. [30] Walter, W.G. (1956) Color illusions and aberrations during stimulation by flickering light. Nature, 177 710. [31] Glicksohn, J. (1986/87) Photic driving and altered states of consciousness: an exploratory study. Imagination, Cognition and Personality, 6(2) New York, 167-182. [32] Collura, T.F. (2001). Application of repetitive visual stimulation to EEG neurofeedback protocols. Journal of Neurotherapy, 6(1), 47-70. [33] Frederick, J.A., Timmerman, D.L., Russell, H.L., & Lubr, J.F. (2005) EEG coherence effects of audio-visual stimulation (AVS) at dominant and twice dominant alpha frequency Journal of Neurotherapy, 8(4), 25-42. [34] Kroger, W.S., & Scheider, S.A. (1959) An electronic aid for hypnotic induction: a preliminary report. International Journal of Clinical and Experimental Hypnosis, 7, 93-98. [35] Lewerenz, C.(1963) A factual report on the brain wave synchronizer. Hypnosis Quartlerly, 6(4), 23. [36] Sadove, M.S. (1963) Hypnosis in anaesthesiology. Illinois Medical Journal, 39-42. [37] Margolis, B. (1966) A technique for rapidly inducing hypnosis. CAL (Certified Akers Laboratories), 21-24. [38] Leonard, K., Telch, M., & Harrington, P.(1999) Dissociation in the laboratory: a comparison of strategies. Behaviour Research and Therapy, 37, 49-61. [39] Leonard, K., Telch, M., & Harrinton, P. (2000) Fear response to dissociation challenge. Anxiety, Stress and Coping, 13, 355-369. [40] Siever, D. (2007) Audio-visual entrainment: history, physiology, and clinical studies. Handbook of Neurofeedback: Dynamics and Clinical Applications, Chapter 7 (pp. 155-183) Binghamton, NY: The Haworth Medical Press. [41] Lewerenz, C.(1963) A factual report on the brain wave synchronizer. Hypnosis Quartlerly, 6(4), 23. [42] Kroger, W.S., & Scheider, S.A. (1959) An electronic aid for hypnotic induction: a preliminary report. International Journal of Clinical and Experimental Hypnosis, 7, 93-98. [43] Glicksohn, J. (1986/87) Photic driving and altered states of consciousness: an exploratory study. Imagination, Cognition and Personality, 6(2) New York, 167-182. [44] Huang, T.L., & Charyton, C. (2008) A comprehensive review of the psychological effects of brainwave entrainment. Alternative Therapies in Health and Medicine, 14(5). [45] Joyce M., & Siever, D.(2000) Audio-visual entrainment program as a treatment for behavior disorders in a school setting. Journal of Neurotherapy, 4(2), 9-15. [46] Anderson, D. (1989) The treatment of migraine with variable frequency photic stimulation. Headache, 29, 154-155. [47] Gagnon, C., & Boersma, F. (1992) The use of repetitive audio-visual entrainment in the management of chronic pain. Medical Hypnoanalysis Journal, 7, 462-468. [48] Berg, K., & Siever, D. (2009) A controlled comparison of audio-visual entrainment for treating SAD. Journal of Neurotherapy, 13(3), 166-175. [49] Cantor, D.S. & Stevens, E. (2009) QEEG correlates of auditory-visual entrainment treatment efficacy of refractory depression. Journal of Neurotherapy, 13(2), 100-108. [50] Thomas, N. & Siever, D. (1989) The effect of repetitive audio/visual stimulation on skeletomotor and vasomotor activity. In Waxman, D., Pederson, D., Wilkie, I., & Meller, P. (Eds.) Hypnosis: 4th European Congress at Oxford, 238-245. London: Whurr Publishers. [51] Manns, A., Miralles, R., & Adrian, H. (1981) The application of audiostimulation and electromyographic biofeedback to bruxism and myofascial pain-dysfunction syndrome. Oral Surgery, 52(3), 247-252.

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[52] Morse, D., & Chow, E. (1993) The effect of the Relaxodont brain wave synchronizer on endodontic anxiety: evaluation by galvanic skin resistance, pulse rate, physical reactions, and questionnaire responses. International Journal of Psychosomatics, 40(1-4), 68-76.

Binaural beats Binaural beats or binaural tones are auditory processing artifacts, or apparent sounds, the perception of which arises in the brain for specific physical stimuli. This effect was discovered in 1839 by Heinrich Wilhelm Dove, and earned greater public awareness in the late 20th Binaural beats century based on claims that binaural beats could help induce relaxation, meditation, creativity and other desirable mental states. The effect on the brainwaves depends on the difference in frequencies of each tone: for example, if 300 Hz was played in one ear and 310 in the other, then the binaural beat would have a frequency of 10 Hz.[1][2] The brain produces a phenomenon resulting in low-frequency pulsations in the amplitude and sound localization of a perceived sound when two tones at slightly different frequencies are presented separately, one to each of a subject's ears, using stereo headphones. A beating tone will be perceived, as if the two tones mixed naturally, out of the brain. The frequencies of the tones must be below 1,000 hertz for the beating to be noticeable.[3] The difference between the two frequencies must be small (less than or equal to 30 Hz) for the effect to occur; otherwise, the two tones will be heard separately and no beat will be perceived. Binaural beats are of interest to neurophysiologists investigating the sense of hearing.[4][5][6][7] Binaural beats reportedly influence the brain in more subtle ways through the entrainment of brainwaves[3][8][9] and have been claimed to reduce anxiety[10] and to provide other health benefits such as control over pain.[11]

Acoustical background For sound localization the human auditory system analyses interaural time differences between both ears inside small frequency ranges, called critical bands. For frequencies below 1000 to 1500 Hz interaural time differences are evaluated from interaural phase differences between both ear signals.[12] The perceived sound is also evaluated from the analysis of both ear signals. If different pure tones (sinusoidal signals with different frequencies) are presented to each ear, there will be time dependent phase and time differences between both ears (see figure). The perceived sound depends on the frequency difference between both ear signals:

Interaural time differences (ITD) of binaural beats

• If the frequency difference between the ear signals is lower than some hertz, the auditory system can follow the changes in the interaural time differences. As a result an auditory event is perceived, which is moving through the head. The perceived direction corresponds to the instantaneous interaural time difference. • For slightly bigger frequency differences between the ear signals (more than 10 Hz) the auditory system can no longer follow the changes in the interaural parameters. A diffuse auditory event appears. The sound corresponds to an overlay of both ear signals, which means amplitude and loudness are changing rapidly (see figure in the chapter above). • For frequency differences between the ear signals of above 30 Hz the cocktail party effect begins to work, and the auditory system is able to analyze the presented ear signals in terms of two different sound sources at two different locations, and two distinct signals are perceived.

Binaural beats Binaural beats can also be experienced without headphones, they appear when playing two different pure tones through loudspeakers. The sound perceived is quite similar: with auditory events which move through the room, at low frequency differences, and diffuse sound at slightly bigger frequency differences. At bigger frequency differences apparent localized sound sources appear.[13] However, it is more effective to use headphones than loudspeakers.

History Heinrich Wilhelm Dove discovered binaural beats in 1839. While research about them continued after that, the subject remained something of a scientific curiosity until 134 years later, with the publishing of Gerald Oster's article "Auditory Beats in the Brain" (Scientific American, 1973). Oster's article identified and assembled the scattered islands of relevant research since Dove, offering fresh insight (and new laboratory findings) to research on binaural beats. In particular,Oster saw binaural beats as a powerful tool for cognitive and neurological research, addressing questions such as how animals locate sounds in their three-dimensional environment, and also the remarkable ability of animals to pick out and focus on specific sounds in a sea of noise (which is known as the "cocktail party effect"). Oster also considered binaural beats to be a potentially useful medical diagnostic tool, not merely for finding and assessing auditory impairments, but also for more general neurological conditions. (Binaural beats involve different neurological pathways than ordinary auditory processing.) For example, Oster found that a number of his subjects that could not perceive binaural beats, suffered from Parkinson's disease. In one particular case, Oster was able to follow the subject through a week-long treatment of Parkinson's disease; at the outset the patient could not perceive binaural beats; but by the end of the week of treatment, the patient was able to hear them. In corroborating an earlier study, Oster also reported gender differences in the perception of beats. Specifically, women seemed to experience two separate peaks in their ability to perceive binaural beats—peaks possibly correlating with specific points in the menstrual cycle, onset of menstruation and during the luteal phase. This data led Oster to wonder if binaural beats could be used as a tool for measuring relative levels of estrogen.[3] The effects of binaural beats on consciousness were first examined by physicist Thomas Warren Campbell and electrical engineer Dennis Mennerich, who under the direction of Robert Monroe sought to reproduce a subjective impression of 4 Hz oscillation that they associated with out-of-body experience.[14] On the strength of their findings, Monroe created the binaural-beat technology self-development industry by forming The Monroe Institute, now a charitable binaural research and education organization.

Unverified claims There have been a number of claims regarding binaural beats, among them that they may simulate the effect of recreational drugs, help people memorize and learn, stop smoking, help dieting, tackle erectile dysfunction and improve athletic performance. Scientific research into binaural beats is very limited. No conclusive studies have been released to support the wilder claims listed above. However, one uncontrolled pilot study[15] of 8 individuals indicates that binaural beats may have a relaxing effect. In absence of positive evidence for a specific effect, however, claimed effects may be attributed to the power of suggestion (the placebo effect). In a blind study (8 participants) of binaural beats' effects on meditation, 7 Hz frequencies were found to enhance meditative focus while 15 Hz frequencies harmed it.[16]

9

Binaural beats

Physiology The sensation of binaural beats is believed to originate in the superior olivary nucleus, a part of the brain stem. They appear to be related to the brain's ability to locate the sources of sounds in three dimensions and to track moving sounds, which also involves inferior colliculus (IC) neurons.[17] Regarding entrainment, the study of rhythmicity provides insights into the understanding of temporal information processing in the human brain. Auditory rhythms rapidly entrain motor responses into stable steady synchronization states below and above conscious perception thresholds. Activated regions include primary sensorimotor and cingulate areas, bilateral opercular premotor areas, bilateral SII, ventral prefrontal cortex, and, subcortically, anterior insula, putamen, and thalamus. Within the cerebellum, vermal regions and anterior hemispheres ipsilateral to the movement became significantly activated. Tracking temporal modulations additionally activated predominantly right prefrontal, anterior cingulate, and intraparietal regions as well as posterior cerebellar hemispheres.[18] A study of aphasic subjects who had a severe stroke versus normal subjects showed that the aphasic subject could not hear the binaural beats whereas the normal subjects could.[19]

Hypothetical effects on brain function Overview Binaural beats may influence functions of the brain in ways besides those related to hearing. This phenomenon is called frequency following response. The concept is that if one receives a stimulus with a frequency in the range of brain waves, the predominant brain wave frequency is said to be likely to move towards the frequency of the stimulus (a process called entrainment).[20] In addition, binaural beats have been credibly documented to relate to both spatial perception & stereo auditory recognition, and, according to the frequency following response, activation of various sites in the brain.[21][22][23][24][25] The stimulus does not have to be aural; it can also be visual[26] or a combination of aural and visual[27] (one such example would be Dreamachine). Perceived human hearing is limited to the range of frequencies from 20 Hz to 20,000 Hz, but the frequencies of human brain waves are below about 40 Hz. To account for this lack of perception, binaural beat frequencies are used. Beat frequencies of 40 Hz have been produced in the brain with binaural sound and measured experimentally.[28] When the perceived beat frequency corresponds to the delta, theta, alpha, beta, or gamma range of brainwave frequencies, the brainwaves entrain to or move towards the beat frequency.[29] For example, if a 315 Hz sine wave is played into the right ear and a 325 Hz one into the left ear, the brain is entrained towards the beat frequency 10 Hz, in the alpha range. Since alpha range is associated with relaxation, this has a relaxing effect or if in the beta range, more alertness. An experiment with binaural sound stimulation using beat frequencies in the Beta range on some participants and Delta/Theta range in other participants, found better vigilance performance and mood in those on the awake alert state of Beta range stimulation.[30][31] Binaural beat stimulation has been used fairly extensively to induce a variety of states of consciousness, and there has been some work done in regards to the effects of these stimuli on relaxation, focus, attention, and states of consciousness.[8] Studies have shown that with repeated training to distinguish close frequency sounds that a plastic reorganization of the brain occurs for the trained frequencies[32] and is capable of asymmetric hemispheric balancing.[33]

10

Binaural beats

11

Brain waves Frequency range > 40 Hz

Name

Usually associated with:

Gamma waves Higher mental activity, including perception, problem solving, fear, and consciousness

13–39 Hz

Beta waves

Active, busy or anxious thinking and active concentration, arousal, cognition, and or paranoia

7–13 Hz

Alpha waves

Relaxation (while awake), pre-sleep and pre-wake drowsiness, REM sleep, Dreams

8–12 Hz

Mu waves

Sensorimotor rhythm Mu_rhythm, Sensorimotor_rhythm

4–7 Hz

Theta waves

deep meditation/relaxation, NREM sleep

< 4 Hz

Delta waves

Deep dreamless sleep, loss of body awareness

(The precise boundaries between ranges vary among definitions, and there is no universally accepted standard.) The dominant frequency determines your current state. For example, if in someone's brain alpha waves are dominating, they are in the alpha state (this happens when one is relaxed but awake). However, other frequencies will also be present, albeit with smaller amplitudes. The brain entraining is more effective if the entraining frequency is close to the user's starting dominant frequency. Therefore, it is suggested to start with a frequency near to one's current dominant frequency (likely to be about 20 Hz or less for a waking person), and then slowly decreasing/increasing it towards the desired frequency. Some people find pure sine waves unpleasant, so a pink noise or another background (e.g. natural sounds such as river noises) can also be mixed with them. In addition to that, as long as the beat is audible, increasing the volume should not necessarily improve the effectiveness, therefore using a low volume is usually suggested. One theory is to reduce the volume so low that the beating should not even be clearly audible, but this does not seem to be the case (see the next paragraph).

Other uses In addition to lowering the brain frequency to relax the listener, there are other controversial, alleged uses for binaural beats. For example, that by using specific frequencies an individual can stimulate certain glands to produce desired hormones. Beta-endorphin has been modulated in studies using alpha-theta brain wave training,[34] and dopamine with binaural beats.[1] Among other alleged uses, there are reducing learning time and sleeping needs (theta waves are thought to improve learning, since children, who have stronger theta waves, and remain in this state for a longer period of time than adults, usually learn faster than adults; and some people find that half an hour in the theta state can reduce sleeping needs up to four hours; similar to another method of achieving a theta state, e.g. meditation;) some use them for lucid dreaming and even for attempting out-of-body experiences, astral projection, telepathy and psychokinesis. However, the role of alpha-wave activity in lucid dreaming is subject to ongoing research).[35][36][37] Alpha-theta brainwave training has also been used successfully for the treatment of addictions.[34][38][39] It has been used for the recovery of repressed memories, but as with other techniques this can lead to false memories.[40] An uncontrolled pilot study of Delta binaural beat technology over 60 days has shown positive effect on self-reported psychologic measures, especially anxiety. There was significant decrease in trait anxiety, an increase in quality of life, and a decrease in insulin-like growth factor-1 and dopamine[1] and has been successfully shown to decrease mild anxiety.[41] A randomised, controlled study concluded that binaural beat audio could lessen hospital acute pre-operative anxiety.[42] Another claimed effect for sound induced brain synchronization is enhanced learning ability. It was proposed in the 1970s that induced alpha brain waves enabled students to assimilate more information with greater long term retention.[43] In more recent times has come more understanding of the role of theta brain waves in behavioural

Binaural beats learning.[44] The presence of theta patterns in the brain has been associated with increased receptivity for learning and decreased filtering by the left hemisphere.[43][45][46] Based on the association between theta activity (4–7 Hz) and working memory performance, biofeedback training suggests that normal healthy individuals can learn to increase a specific component of their EEG activity, and that such enhanced activity may facilitate a working memory task and to a lesser extent focused attention.[47] A small media controversy was spawned in 2010 by an Oklahoma Bureau of Narcotics official comparing binaural beats to illegal narcotics, and warning that interest in websites offering binaural beats could lead to drug use.[48]

References [1] Wahbeh H, Calabrese C, Zwickey H (2007). "Binaural beat technology in humans: a pilot study to assess psychologic and physiologic effects". Journal of alternative and complementary medicine 13 (1): 25–32. doi:10.1089/acm.2006.6196. PMID 17309374. [2] Wahbeh H, Calabrese C, Zwickey H, Zajdel J (2007). "Binaural Beat Technology in Humans: A Pilot Study to Assess Neuropsychologic, Physiologic, And Electroencephalographic Effects". Journal of alternative and complementary medicine 13 (2): 199–206. doi:10.1089/acm.2006.6201. PMID 17388762. [3] Oster G (1973). "Auditory beats in the brain". Sci. Am. 229 (4): 94–102. doi:10.1038/scientificamerican1073-94. PMID 4727697. [4] Fitzpatrick D, et al (2009). "Processing Temporal Modulations in Binaural and Monaural Auditory Stimuli by Neurons in the Inferior Colliculus and Auditory Cortex". JARO 10 (4): 579–593. doi:10.1007/s10162-009-0177-8. PMID 19506952. [5] Gu X, Wright BA, Green DM (1995). "Failure to hear binaural beats below threshold". The Journal of the Acoustical Society of America 97 (1): 701–703. doi:10.1121/1.412294. PMID 7860843. [6] Zeng F-G, et al (2005). "Perceptual Consequences of Disrupted Auditory Nerve Activity". Journal of Neurophysiology 93 (6): 3050–3063. doi:10.1152/jn.00985.2004. PMID 15615831. [7] Jan Schnupp, Israel Nelken and Andrew King (2011). Auditory Neuroscience (https:/ / mustelid. physiol. ox. ac. uk/ drupal). MIT Press. ISBN 0-262-11318-X. . [8] Hutchison, Michael M. (1986). Megabrain: new tools and techniques for brain growth and mind expansion. New York: W. Morrow. ISBN 0-688-04880-3. [9] Turmel, Ron. "Resonant Frequencies and the Human Brain" (http:/ / www. erowid. org/ culture/ references/ other/ 1997_turmel_resproject_1. shtml). The Resonance Project. . Retrieved 10 June 2011. [10] http:/ / pt. wkhealth. com/ pt/ re/ emmednews/ abstract. 00000524-200509000-00006. htm [11] Hemispheric-synchronisation during anaesthesia: a double-blind randomised trial using audiotapes for intra-operative nociception control (http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 10460529), Jan 2000, Kliempt, Ruta, Ogston, Landeck & Martay [12] Blauert, J.: Spatial hearing - the psychophysics of human sound localization; MIT Press; Cambridge, Massachusetts (1983), ch. 2.4 [13] Slatky, Harald (1992): Algorithms for direction specific Processing of Sound Signals - the Realization of a binaural Cocktail-Party-Processor-System (http:/ / www. cocktail-party-processor. de/ english/ algo/ index. html), Dissertation, Ruhr-University Bochum, ch. 3 [14] "My Big TOE" book 1, Thomas Campbell, p79 ISBN 978-0-9725094-0-4 [15] Wahbeh H, Calabrese C, Zwickey H (2007). "Binaural beat technology in humans: a pilot study to assess psychologic and physiologic effects". J Altern Complement Med 13 (1): 25–32. doi:10.1089/acm.2006.6196. PMID 17309374. [16] Lavallee, Christina F.; Koren, Persinger (7). "A Quantitative Electroencephalographic Study of Meditation and Binaural Beat Entrainment" (http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 21480784). Journal of Alternative and Complementary Medicine 17 (4): 351–355. doi:10.1089/acm.2009.0691. PMID 21480784. . Retrieved 10 March 2012. [17] Spitzer MW, Semple MN (1998). "Transformation of binaural response properties in the ascending auditory pathway: influence of time-varying interaural phase disparity". J. Neurophysiol. 80 (6): 3062–76. PMID 9862906. [18] Thaut MH (2003). "Neural basis of rhythmic timing networks in the human brain". Ann. N. Y. Acad. Sci. 999 (1): 364–73. doi:10.1196/annals.1284.044. PMID 14681157. [19] Barr DF, Mullin TA, Herbert PS. (1977). "Application of binaural beat phenomenon with aphasic patients". Arch Otolaryngol. 103 (4): 192–194. PMID 849195. [20] Gerken GM, Moushegian G, Stillman RD, Rupert AL (1975). "Human frequency-following responses to monaural and binaural stimuli". Electroencephalography and clinical neurophysiology 38 (4): 379–86. doi:10.1016/0013-4694(75)90262-X. PMID 46818. [21] Dobie RA, Norton SJ (1980). "Binaural interaction in human auditory evoked potentials". Electroencephalography and clinical neurophysiology 49 (3-4): 303–13. doi:10.1016/0013-4694(80)90224-2. PMID 6158406. [22] Moushegian G, Rupert AL, Stillman RD (1978). "Evaluation of frequency-following potentials in man: masking and clinical studies". Electroencephalography and clinical neurophysiology 45 (6): 711–18. doi:10.1016/0013-4694(78)90139-6. PMID 84739. [23] Smith JC, Marsh JT, Greenberg S, Brown WS (1978). "Human auditory frequency-following responses to a missing fundamental". Science 201 (4356): 639–41. doi:10.1126/science.675250. PMID 675250. [24] Smith JC, Marsh JT, Brown WS (1975). "Far-field recorded frequency-following responses: evidence for the locus of brainstem sources". Electroencephalography and clinical neurophysiology 39 (5): 465–72. doi:10.1016/0013-4694(75)90047-4. PMID 52439.

12

Binaural beats [25] Yamada O, Yamane H, Kodera K (1977). "Simultaneous recordings of the brain stem response and the frequency-following response to low-frequency tone". Electroencephalography and clinical neurophysiology 43 (3): 362–70. doi:10.1016/0013-4694(77)90259-0. PMID 70337. [26] Cvetkovic D, Simpson D, Cosic I (2006). "Influence of sinusoidally modulated visual stimuli at extremely low frequency range on the human EEG activity". Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 1: 1311–4. doi:10.1109/IEMBS.2006.259565. PMID 17945633. [27] "[Abstract (http:/ / www. actapress. com/ Abstract. aspx?paperId=16352) The Induced Rhythmic Oscillations of Neural Activity in the Human Brain"]. . Retrieved 2007-11-14. [28] Schwarz DW, Taylor P (2005). "Human auditory steady state responses to binaural and monaural beats". Clinical Neurophysiology 116 (3): 658–68. doi:10.1016/j.clinph.2004.09.014. PMID 15721080. [29] Rogers LJ, Walter DO (1981). "Methods for finding single generators, with application to auditory driving of the human EEG by complex stimuli". J. Neurosci. Methods 4 (3): 257–65. doi:10.1016/0165-0270(81)90037-6. PMID 7300432. [30] Lane JD, Kasian SJ, Owens JE, Marsh GR (1998). "Binaural auditory beats affect vigilance performance and mood". Physiol. Behav. 63 (2): 249–52. doi:10.1016/S0031-9384(97)00436-8. PMID 9423966. [31] Beatty J, Greenberg A, Deibler WP, O'Hanlon JF (1974). "Operant control of occipital theta rhythm affects performance in a radar monitoring task". Science 183 (4127): 871–3. doi:10.1126/science.183.4127.871. PMID 4810845. [32] Menning H, Roberts LE, Pantev C (2000). "Plastic changes in the auditory cortex induced by intensive frequency discrimination training". Neuroreport 11 (4): 817–22. doi:10.1097/00001756-200003200-00032. PMID 10757526. [33] Gottselig JM, Brandeis D, Hofer-Tinguely G, Borbély AA, Achermann P (2004). "Human central auditory plasticity associated with tone sequence learning". Learn. Mem. 11 (2): 162–71. doi:10.1101/lm.63304. PMC 379686. PMID 15054131. [34] Peniston EG, Kulkosky PJ (1989). "Alpha-theta brainwave training and beta-endorphin levels in alcoholics". Alcohol. Clin. Exp. Res. 13 (2): 271–9. doi:10.1111/j.1530-0277.1989.tb00325.x. PMID 2524976. [35] Ogilvie RD, Hunt HT, Tyson PD, Lucescu ML, Jeakins DB (1982). "Lucid dreaming and alpha activity: a preliminary report". Perceptual and motor skills 55 (3 Pt 1): 795–808. PMID 7162915. [36] Korabel'nikova EA, Golubev VL (2001). "[Dreams and interhemispheric asymmetry]" (in Russian). Zhurnal nevrologii i psikhiatrii imeni S.S. Korsakova / Ministerstvo zdravookhraneniia i meditsinskoĭ promyshlennosti Rossiĭskoĭ Federatsii, Vserossiĭskoe obshchestvo nevrologov Vserossiĭskoe obshchestvo psikhiatrov 101 (12): 51–4. PMID 11811128. [37] Spoormaker VI, van den Bout J (2006). "Lucid dreaming treatment for nightmares: a pilot study". Psychotherapy and psychosomatics 75 (6): 389–94. doi:10.1159/000095446. PMID 17053341. [38] Saxby E, Peniston EG (1995). "Alpha-theta brainwave neurofeedback training: an effective treatment for male and female alcoholics with depressive symptoms". Journal of clinical psychology 51 (5): 685–93. doi:10.1002/1097-4679(199509)51:5<685::AID-JCLP2270510514>3.0.CO;2-K. PMID 8801245. [39] Watson CG, Herder J, Passini FT (1978). "Alpha biofeedback therapy in alcoholics: an 18-month follow-up". Journal of clinical psychology 34 (3): 765–9. doi:10.1002/1097-4679(197807)34:3<765::AID-JCLP2270340339>3.0.CO;2-5. PMID 690224. [40] Loftus EF, Davis D (2006). "Recovered memories". Annual review of clinical psychology 2 (1): 469–98. doi:10.1146/annurev.clinpsy.2.022305.095315. PMID 17716079. [41] Le Scouarnec RP, Poirier RM, Owens JE, Gauthier J, Taylor AG, Foresman PA. (2001). "Use of binaural beat tapes for treatment of anxiety: a pilot study of tape preference and outcomes". Altern Ther Health Med. (Clinique Psych in Montreal, Quebec.) 7 (1): 58–63. PMID 11191043. [42] Padmanabhan R, Hildreth AJ, Laws D (2005). "A prospective, randomised, controlled study examining binaural beat audio and pre-operative anxiety in patients undergoing general anaesthesia for day case surgery". Anaesthesia 60 (9): 874–7. doi:10.1111/j.1365-2044.2005.04287.x. PMID 16115248. [43] Harris, Bill (2002). Thresholds of the Mind. Centerpointe Press. Appendix 1, pp151–178. ISBN 0-9721780-0-7. [44] Berry SD, Seager MA (2001). "Hippocampal theta oscillations and classical conditioning". Neurobiol Learn Mem 76 (3): 298–313. doi:10.1006/nlme.2001.4025. PMID 11726239. [45] Seager MA, Johnson LD, Chabot ES, Asaka Y, Berry SD (2002). "Oscillatory brain states and learning: Impact of hippocampal theta-contingent training". Proc. Natl. Acad. Sci. U.S.A. 99 (3): 1616–20. doi:10.1073/pnas.032662099. PMC 122239. PMID 11818559. [46] Griffin AL, Asaka Y, Darling RD, Berry SD (2004). "Theta-contingent trial presentation accelerates learning rate and enhances hippocampal plasticity during trace eyeblink conditioning". Behav. Neurosci. 118 (2): 403–11. doi:10.1037/0735-7044.118.2.403. PMID 15113267. [47] Vernon D, Egner T, Cooper N, et al. (2003). "The effect of training distinct neurofeedback protocols on aspects of cognitive performance". International journal of psychophysiology : official journal of the International Organization of Psychophysiology 47 (1): 75–85. doi:10.1016/S0167-8760(02)00091-0. PMID 12543448. [48] "Report: Teens Using Digital Drugs to Get High" (http:/ / www. wired. com/ threatlevel/ 2010/ 07/ digital-drugs/ ). Wired. 14 July 2010. . Retrieved 22 November 2012.

13

Binaural beats

14

External links • Binaural beats (http://www.cerebrowave.com/resources/binaural-beats/)

Isochronic tones Isochronic tones are regular beats of a single tone used for brainwave entrainment. Similar to monaural beats, the interference pattern that produces the beat is outside the brain so headphones are not required for entrainment to be effective. They differ from monaural beats, which are constant sine wave pulses rather than entirely separate pulses of a single tone. As the contrast between noise and silence is more pronounced than the constant pulses of monaural beats, the stimulus is stronger and has a greater effect on brain entrainment.[1]

Isochronic tones

Isochronic tones work by emitting sound at regular intervals. This excites the thalamus and causes the brain to duplicate the frequency of the Isochronic tones, changing its thought patterns.

Notes [1] Entraining Tones and Binaural Beats, David Siever (http:/ / www. mindalive. com/ articleeleven. htm)

Electroencephalography

15

Electroencephalography EEG Intervention

An EEG recording at Dalhousie University [1]

ICD-9-CM

89.14

MeSH

D004569

OPS-301 code:

1-207

[2]

[3]

Electroencephalography (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations resulting from ionic current flows within the neurons of the brain.[4] In clinical contexts, EEG refers to the recording of the brain's spontaneous electrical activity over a short period of time, usually 20–40 minutes, as recorded from multiple electrodes placed on the scalp. Diagnostic applications generally focus on the spectral content of EEG, that is, the type of neural oscillations that can be observed in EEG signals. In neurology, the main diagnostic application of EEG is in the case of epilepsy, as epileptic activity can create clear abnormalities on a standard EEG study.[5] A secondary clinical use of EEG is in the diagnosis of coma, encephalopathies, and brain death. A third clinical Epileptic spike and wave discharges monitored use of EEG is for studies of sleep and sleep disorders where recordings with EEG are typically done for one full night, sometimes more. EEG used to be a first-line method for the diagnosis of tumors, stroke and other focal brain disorders,[6] but this use has decreased with the advent of anatomical imaging techniques with high (<1 mm) spatial resolution such as MRI and CT. Despite limited spatial resolution, EEG continues to be a valuable tool for research and diagnosis, especially when millisecond-range temporal resolution (not possible with CT or MRI) is required. Derivatives of the EEG technique include evoked potentials (EP), which involves averaging the EEG activity time-locked to the presentation of a stimulus of some sort (visual, somatosensory, or auditory). Event-related

Electroencephalography potentials (ERPs) refer to averaged EEG responses that are time-locked to more complex processing of stimuli; this technique is used in cognitive science, cognitive psychology, and psychophysiological research.

Source of EEG activity The brain's electrical charge is maintained by billions of neurons. Neurons are electrically charged (or "polarized") by membrane transport proteins that pump ions across their membranes. Neurons are constantly exchanging ions with the extracellular milieu, for example to maintain resting potential and to propagate action potentials. Ions of similar charge repel each other, and when many ions are pushed out of many neurons at the same time, they can push their neighbours, who push their neighbours, and so on, in a wave. This process is known as volume conduction. When the wave of ions reaches the electrodes on the scalp, they can push or pull electrons on the metal on the electrodes. Since metal conducts the push and pull of electrons easily, the difference in push or pull voltages between any two electrodes can be measured by a voltmeter. Recording these voltages over time gives us the EEG.[7] The electric potential generated by single neuron is far too small to be picked up by EEG or MEG.[8] EEG activity therefore always reflects the summation of the synchronous activity of thousands or millions of neurons that have similar spatial orientation. If the cells do not have similar spatial orientation, their ions do not line up and create waves to be detected. Pyramidal neurons of the cortex are thought to produce the most EEG signal because they are well-aligned and fire together. Because voltage fields fall off with the square of distance, activity from deep sources is more difficult to detect than currents near the skull.[9] Scalp EEG activity shows oscillations at a variety of frequencies. Several of these oscillations have characteristic frequency ranges, spatial distributions and are associated with different states of brain functioning (e.g., waking and the various sleep stages). These oscillations represent synchronized activity over a network of neurons. The neuronal networks underlying some of these oscillations are understood (e.g., the thalamocortical resonance underlying sleep spindles), while many others are not (e.g., the system that generates the posterior basic rhythm). Research that measures both EEG and neuron spiking finds the relationship between the two is complex with the power of surface EEG in only two bands (gamma and delta) relating to neuron spike activity.[10]

Clinical use A routine clinical EEG recording typically lasts 20–30 minutes (plus preparation time) and usually involves recording from scalp electrodes. Routine EEG is typically used in the following clinical circumstances: • to distinguish epileptic seizures from other types of spells, such as psychogenic non-epileptic seizures, syncope (fainting), sub-cortical movement disorders and migraine variants. • to differentiate "organic" encephalopathy or delirium from primary psychiatric syndromes such as catatonia • to serve as an adjunct test of brain death • to prognosticate, in certain instances, in patients with coma • to determine whether to wean anti-epileptic medications At times, a routine EEG is not sufficient, particularly when it is necessary to record a patient while he/she is having a seizure. In this case, the patient may be admitted to the hospital for days or even weeks, while EEG is constantly being recorded (along with time-synchronized video and audio recording). A recording of an actual seizure (i.e., an ictal recording, rather than an inter-ictal recording of a possibly epileptic patient at some period between seizures) can give significantly better information about whether or not a spell is an epileptic seizure and the focus in the brain from which the seizure activity emanates. Epilepsy monitoring is typically done: • to distinguish epileptic seizures from other types of spells, such as psychogenic non-epileptic seizures, syncope (fainting), sub-cortical movement disorders and migraine variants. • to characterize seizures for the purposes of treatment

16

Electroencephalography

17

• to localize the region of brain from which a seizure originates for work-up of possible seizure surgery Additionally, EEG may be used to monitor certain procedures: • to monitor the depth of anesthesia • as an indirect indicator of cerebral perfusion in carotid endarterectomy • to monitor amobarbital effect during the Wada test EEG can also be used in intensive care units for brain function monitoring: • to monitor for non-convulsive seizures/non-convulsive status epilepticus • to monitor the effect of sedative/anesthesia in patients in medically induced coma (for treatment of refractory seizures or increased intracranial pressure) • to monitor for secondary brain damage in conditions such as subarachnoid hemorrhage (currently a research method) If a patient with epilepsy is being considered for resective surgery, it is often necessary to localize the focus (source) of the epileptic brain activity with a resolution greater than what is provided by scalp EEG. This is because the cerebrospinal fluid, skull and scalp smear the electrical potentials recorded by scalp EEG. In these cases, neurosurgeons typically implant strips and grids of electrodes (or penetrating depth electrodes) under the dura mater, through either a craniotomy or a burr hole. The recording of these signals is referred to as electrocorticography (ECoG), subdural EEG (sdEEG) or intracranial EEG (icEEG)--all terms for the same thing. The signal recorded from ECoG is on a different scale of activity than the brain activity recorded from scalp EEG. Low voltage, high frequency components that cannot be seen easily (or at all) in scalp EEG can be seen clearly in ECoG. Further, smaller electrodes (which cover a smaller parcel of brain surface) allow even lower voltage, faster components of brain activity to be seen. Some clinical sites record from penetrating microelectrodes.[4]

Research use EEG, and the related study of ERPs are used extensively in neuroscience, cognitive science, cognitive psychology, and psychophysiological research. Many EEG techniques used in research are not standardized sufficiently for clinical use.

Relative advantages Several other methods to study brain function exist, including functional magnetic resonance imaging (fMRI), positron emission tomography, magnetoencephalography, Nuclear magnetic resonance spectroscopy, Electrocorticography, and Single-photon emission computed tomography. Despite the relatively poor spatial sensitivity of EEG, it possesses multiple advantages over these techniques:

The first human EEG recording obtained by Hans Berger in 1924. The upper tracing is EEG, and the lower is a 10 Hz timing signal.

• Hardware costs are significantly lower than those of all other techniques [11] • EEG sensors can be used in more places than fMRI, SPECT, PET, MRS, or MEG, as these techniques require bulky and immobile equipment. For example, MEG requires equipment consisting of liquid helium-cooled detectors that can be used only in magnetically shielded rooms, altogether costing upwards of several million dollars;[12] and fMRI requires the use of a 1-ton magnet in, again, a shielded room. • EEG has very high temporal resolution, on the order of milliseconds rather than seconds. EEG is commonly recorded at sampling rates between 250 and 2000 Hz in clinical and research settings, but modern EEG data collection systems are capable of recording at sampling rates above 20,000 Hz if desired. MEG is the only other noninvasive cognitive neuroscience technique that approaches this level of temporal resolution.[12] • EEG is relatively tolerant of subject movement, unlike all other neuroimaging techniques. There even exist methods for minimizing, and even eliminating movement artefacts in EEG data [13]

Electroencephalography • EEG is silent, which allows for better study of the responses to auditory stimuli • EEG does not aggravate claustrophobia, unlike fMRI, PET, MRS, SPECT, and sometimes MEG [14] • EEG does not involve exposure to high-intensity (>1 Tesla) magnetic fields, as in some of the other techniques, especially MRI and MRS. These can cause a variety of undesirable issues with the data, and also prohibit use of these techniques with participants that have metal implants in their body, such as metal-containing pacemakers [15]

• EEG does not involve exposure to radioligands, unlike positron emission tomography.[16] • ERP studies can be conducted with relatively simple paradigms, compared with IE block-design fMRI studies • Extremely uninvasive, unlike Electrocorticography, which actually requires electrodes to be placed on the surface of the brain. EEG also has some characteristics that compare favorably with behavioral testing: • • • •

EEG can detect covert processing (i.e., processing that does not require a response) [17] EEG can be used in subjects who are incapable of making a motor response [18] Some ERP components can be detected even when the subject is not attending to the stimuli Unlike other means of studying reaction time, ERPs can elucidate stages of processing (rather than just the final end result) [19]

Relative disadvantages • Low spatial resolution on the scalp. fMRI, for example, can directly display areas of the brain that are active, while EEG requires intense interpretation just to hypothesize what areas are activated by a particular response.[20] • EEG determines neural activity that occurs below the upper layers of the brain (the cortex) poorly. • Unlike PET and MRS, cannot identify specific locations in the brain at which various neurotransmitters, drugs, etc. can be found.[16] • Often takes a long time to connect a subject to EEG, as it requires precise placement of dozens of electrodes around the head and the use of various gels, saline solutions, and/or pastes to keep them in place. While the length of time differs dependent on the specific EEG device used, as a general rule it takes considerably less time to prepare a subject for MEG, fMRI, MRS, and SPECT. • Signal-to-noise ratio is poor, so sophisticated data analysis and relatively large numbers of subjects are needed to extract useful information from EEG [21]

Combining EEG with other neuroimaging techniques Simultaneous EEG recordings and fMRI scans have been obtained successfully,[22][23] though successful simultaneous recording requires that several technical difficulties be overcome, such as the presence of ballistocardiographic artifact, MRI pulse artifact and the induction of electrical currents in EEG wires that move within the strong magnetic fields of the MRI. While challenging, these have been successfully overcome in a number of studies.[24] Similarly, simultaneous recordings with MEG and EEG have also been conducted, which has several advantages over using either technique alone: • EEG requires accurate information about certain aspects of the skull that can only be estimated, such as skull radius, and conductivities of various skull locations. MEG does not have this issue, and a simultaneous analysis allows this to be corrected for. • MEG and EEG both detect activity below the surface of the cortex very poorly, and like EEG, the level of error increases with the depth below the surface of the cortex one attempts to examine. However, the errors are very different between the techniques, and combining them thus allows for correction of some of this noise. • MEG has access to virtually no sources of brain activity below a few centimetres under the cortex. EEG, on the other hand, can receive signals from greater depth, albeit with a high degree of noise. Combining the two makes it

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Electroencephalography

19

easier to determine what in the EEG signal comes from the surface (since MEG is very accurate in examining signals from the surface of the brain), and what comes from deeper in the brain, thus allowing for analysis of deeper brain signals than either EEG or MEG on its own.[25] EEG has also been combined with positron emission tomography. This provides the advantage of allowing researchers to see what EEG signals are associated with different drug actions in the brain.[26]

Method In conventional scalp EEG, the recording is obtained by placing electrodes on the scalp with a conductive gel or paste, usually after preparing the scalp area by light abrasion to reduce impedance due to dead skin cells. Many systems typically use electrodes, each of which is attached to an individual wire. Some systems use caps or nets into which electrodes are embedded; this is particularly common when high-density arrays of electrodes are needed. Computer Electroencephalograph

Electrode locations and names are specified by the International 10–20 Neurovisor-BMM 40 system[27] for most clinical and research applications (except when high-density arrays are used). This system ensures that the naming of electrodes is consistent across laboratories. In most clinical applications, 19 recording electrodes (plus ground and system reference) are used.[28] A smaller number of electrodes are typically used when recording EEG from neonates. Additional electrodes can be added to the standard set-up when a clinical or research application demands increased spatial resolution for a particular area of the brain. High-density arrays (typically via cap or net) can contain up to 256 electrodes more-or-less evenly spaced around the scalp. Each electrode is connected to one input of a differential amplifier (one amplifier per pair of electrodes); a common system reference electrode is connected to the other input of each differential amplifier. These amplifiers amplify the voltage between the active electrode and the reference (typically 1,000–100,000 times, or 60–100 dB of voltage gain). In analog EEG, the signal is then filtered (next paragraph), and the EEG signal is output as the deflection of pens as paper passes underneath. Most EEG systems these days, however, are digital, and the amplified signal is digitized via an analog-to-digital converter, after being passed through an anti-aliasing filter. Analog-to-digital sampling typically occurs at 256–512 Hz in clinical scalp EEG; sampling rates of up to 20 kHz are used in some research applications. During the recording, a series of activation procedures may be used. These procedures may induce normal or abnormal EEG activity that might not otherwise be seen. These procedures include hyperventilation, photic stimulation (with a strobe light), eye closure, mental activity, sleep and sleep deprivation. During (inpatient) epilepsy monitoring, a patient's typical seizure medications may be withdrawn. The digital EEG signal is stored electronically and can be filtered for display. Typical settings for the high-pass filter and a low-pass filter are 0.5-1 Hz and 35–70 Hz, respectively. The high-pass filter typically filters out slow artifact, such as electrogalvanic signals and movement artifact, whereas the low-pass filter filters out high-frequency artifacts, such as electromyographic signals. An additional notch filter is typically used to remove artifact caused by electrical power lines (60 Hz in the United States and 50 Hz in many other countries).[4] As part of an evaluation for epilepsy surgery, it may be necessary to insert electrodes near the surface of the brain, under the surface of the dura mater. This is accomplished via burr hole or craniotomy. This is referred to variously as "electrocorticography (ECoG)", "intracranial EEG (I-EEG)" or "subdural EEG (SD-EEG)". Depth electrodes may also be placed into brain structures, such as the amygdala or hippocampus, structures, which are common epileptic foci and may not be "seen" clearly by scalp EEG. The electrocorticographic signal is processed in the same manner as digital scalp EEG (above), with a couple of caveats. ECoG is typically recorded at higher sampling rates than scalp EEG because of the

Electroencephalography requirements of Nyquist theorem—the subdural signal is composed of a higher predominance of higher frequency components. Also, many of the artifacts that affect scalp EEG do not impact ECoG, and therefore display filtering is often not needed. A typical adult human EEG signal is about 10µV to 100 µV in amplitude when measured from the scalp[29] and is about 10–20 mV when measured from subdural electrodes. Since an EEG voltage signal represents a difference between the voltages at two electrodes, the display of the EEG for the reading encephalographer may be set up in one of several ways. The representation of the EEG channels is referred to as a montage. Bipolar montage Each channel (i.e., waveform) represents the difference between two adjacent electrodes. The entire montage consists of a series of these channels. For example, the channel "Fp1-F3" represents the difference in voltage between the Fp1 electrode and the F3 electrode. The next channel in the montage, "F3-C3," represents the voltage difference between F3 and C3, and so on through the entire array of electrodes. Referential montage Each channel represents the difference between a certain electrode and a designated reference electrode. There is no standard position for this reference; it is, however, at a different position than the "recording" electrodes. Midline positions are often used because they do not amplify the signal in one hemisphere vs. the other. Another popular reference is "linked ears," which is a physical or mathematical average of electrodes attached to both earlobes or mastoids. Average reference montage The outputs of all of the amplifiers are summed and averaged, and this averaged signal is used as the common reference for each channel. Laplacian montage Each channel represents the difference between an electrode and a weighted average of the surrounding electrodes.[30] When analog (paper) EEGs are used, the technologist switches between montages during the recording in order to highlight or better characterize certain features of the EEG. With digital EEG, all signals are typically digitized and stored in a particular (usually referential) montage; since any montage can be constructed mathematically from any other, the EEG can be viewed by the electroencephalographer in any display montage that is desired. The EEG is read by a clinical neurophysiologist or neurologist (depending on local custom and law regarding medical specialities), optimally one who has specific training in the interpretation of EEGs for clinical purposes. This is done by visual inspection of the waveforms, called graphoelements. The use of computer signal processing of the EEG—so-called quantitative EEG—is somewhat controversial when used for clinical purposes (although there are many research uses).

Limitations EEG has several limitations. Most important is its poor spatial resolution. EEG is most sensitive to a particular set of post-synaptic potentials: those generated in superficial layers of the cortex, on the crests of gyri directly abutting the skull and radial to the skull. Dendrites, which are deeper in the cortex, inside sulci, in midline or deep structures (such as the cingulate gyrus or hippocampus), or producing currents that are tangential to the skull, have far less contribution to the EEG signal. The meninges, cerebrospinal fluid and skull "smear" the EEG signal, obscuring its intracranial source. It is mathematically impossible to reconstruct a unique intracranial current source for a given EEG signal,[4] as some currents produce potentials that cancel each other out. This is referred to as the inverse problem. However, much

20

Electroencephalography work has been done to produce remarkably good estimates of, at least, a localized electric dipole that represents the recorded currents.

EEG vs fMRI, fNIRS and PET EEG has several strong points as a tool for exploring brain activity. EEG's can detect changes over milliseconds, which is excellent considering an action potential takes approximately 0.5-130 milliseconds to propagate across a single neuron, depending on the type of neuron.[31] Other methods of looking at brain activity, such as PET and fMRI have time resolution between seconds and minutes. EEG measures the brain's electrical activity directly, while other methods record changes in blood flow (e.g., SPECT, fMRI) or metabolic activity (e.g., PET, NIRS), which are indirect markers of brain electrical activity. EEG can be used simultaneously with fMRI so that high-temporal-resolution data can be recorded at the same time as high-spatial-resolution data, however, since the data derived from each occurs over a different time course, the data sets do not necessarily represent exactly the same brain activity. There are technical difficulties associated with combining these two modalities, including the need to remove the MRI gradient artifact present during MRI acquisition and the ballistocardiographic artifact (resulting from the pulsatile motion of blood and tissue) from the EEG. Furthermore, currents can be induced in moving EEG electrode wires due to the magnetic field of the MRI. EEG can be used simultaneously with NIRS without major technical difficulties. There is no influence of these modalities on each other and a combined measurement can give useful information about electrical activity as well as local hemodynamics.

EEG vs MEG EEG reflects correlated synaptic activity caused by post-synaptic potentials of cortical neurons. The ionic currents involved in the generation of fast action potentials may not contribute greatly to the averaged field potentials representing the EEG .[8][32] More specifically, the scalp electrical potentials that produce EEG are generally thought to be caused by the extracellular ionic currents caused by dendritic electrical activity, whereas the fields producing magnetoencephalographic signals[33] are associated with intracellular ionic currents .[34] EEG can be recorded at the same time as MEG so that data from these complementary high-time-resolution techniques can be combined.

Normal activity The EEG is typically described in terms of (1) rhythmic activity and (2) transients. The rhythmic activity is divided into bands by frequency. To some degree, these frequency bands One second of EEG signal are a matter of nomenclature (i.e., any rhythmic activity between 6–12 Hz can be described as "alpha"), but these designations arose because rhythmic activity within a certain frequency range was noted to have a certain distribution over the scalp or a certain biological significance. Frequency bands are usually extracted using spectral methods (for instance Welch) as implemented for instance in freely available EEG software such as EEGLAB or the the neurophysiological biomarker toolbox [35]. Most of the cerebral signal observed in the scalp EEG falls in the range of 1–20 Hz (activity below or above this range is likely to be artifactual, under standard clinical recording techniques).

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Electroencephalography

22

Comparison table Comparison of EEG bands Type

Frequency (Hz)

Location

Normally

Delta

up to 4

frontally in adults, posteriorly in children; high-amplitude waves

• • •

adult slow-wave sleep in babies Has been found during some [36] continuous-attention tasks

• • • •

subcortical lesions diffuse lesions metabolic encephalopathy hydrocephalus deep midline lesions

Theta

4–8

Found in locations not related to task at hand

• •

young children drowsiness or arousal in older children and adults idling Associated with inhibition of elicited responses (has been found to spike in situations where a person is actively trying to repress a response or [36] action).

• • • •

focal subcortical lesions metabolic encephalopathy deep midline disorders some instances of hydrocephalus

• •

Alpha

Beta

8 – 13

posterior regions of head, both sides, higher in amplitude on non-dominant side. Central sites (c3-c4) at rest

• • •

relaxed/reflecting closing the eyes Also associated with inhibition control, seemingly with the purpose of timing inhibitory activity in different locations across the brain.

•

coma

>13 – 30

both sides, symmetrical distribution, most evident frontally; low-amplitude waves

• •

alert/wo active, busy, or anxious thinking, active concentration

•

benzodiazepines

Somatosensory cortex

•

Displays during cross-modal sensory processing (perception that combines two different senses, such as sound and [37][38] sight) Also is shown during short-term memory matching of recognized objects, sounds, or tactile sensations

•

A decrease in gamma-band activity may be associated with cognitive decline, especially when related to the theta band; however, this has not been proven for use as a clinical diagnostic measurement

•

Mu suppression could indicate that motor mirror neurons are working. Deficits in Mu suppression, and thus in mirror neurons, [40] might play a role in autism.

Gamma 30 – 100+

•

Mu

Pathologically

8 – 13

Sensorimotor cortex

•

[39]

Shows rest-state motor neurons.

It should be noted that while these are the universally recognized ranges, they are not concrete definitions of the range of brain-waves. While researchers tend to follow these guidelines, many scholars use their own specific boundaries depending on the range they choose to focus on. Additionally, some researchers define the bands using decimal values rather than rounding to whole numbers (for example, one researcher may define the lower Beta band cut-off as 12.1, while another may use the value 13), while still others sometimes divide the bands into subbands. Generally, this is only done for the sake of analysis.

Electroencephalography

Wave patterns • Delta is the frequency range up to 4 Hz. It tends to be the highest in amplitude and the slowest waves. It is seen normally in adults in slow wave sleep. It is also seen normally delta waves. in babies. It may occur focally with subcortical lesions and in general distribution with diffuse lesions, metabolic encephalopathy hydrocephalus or deep midline lesions. It is usually most prominent frontally in adults (e.g. FIRDA - Frontal Intermittent Rhythmic Delta) and posteriorly in children (e.g. OIRDA - Occipital Intermittent Rhythmic Delta). • Theta is the frequency range from 4 Hz to 7 Hz. Theta is seen normally in young children. It may be seen in drowsiness or arousal in older children and adults; it can also theta waves. be seen in meditation.[41] Excess theta for age represents abnormal activity. It can be seen as a focal disturbance in focal subcortical lesions; it can be seen in generalized distribution in diffuse disorder or metabolic encephalopathy or deep midline disorders or some instances of hydrocephalus. On the contrary this range has been associated with reports of relaxed, meditative, and creative states. • Alpha is the frequency range from 8 Hz to 12 Hz. Hans Berger named the first rhythmic EEG activity he saw as the "alpha wave". This was the "posterior basic rhythm" (also alpha waves. called the "posterior dominant rhythm" or the "posterior alpha rhythm"), seen in the posterior regions of the head on both sides, higher in amplitude on the dominant side. It emerges with closing of the eyes and with relaxation, and attenuates with eye opening or mental exertion. The posterior basic rhythm is actually slower than 8 Hz in young children (therefore technically in the theta range). In addition to the posterior basic rhythm, there are other normal alpha rhythms such as the mu rhythm (alpha activity in the contralateral sensory and motor cortical areas that emerges when sensorimotor rhythm aka mu rhythm. the hands and arms are idle; and the "third rhythm" (alpha activity in the temporal or frontal lobes).[42][43] Alpha can be abnormal; for example, an EEG that has diffuse alpha occurring in coma and is not responsive to external stimuli is referred to as "alpha coma".

23

Electroencephalography

24

• Beta is the frequency range from 12 Hz to about 30 Hz. It is seen usually on both sides in symmetrical distribution and is most evident frontally. Beta activity beta waves. is closely linked to motor behavior and is generally attenuated during active movements.[44] Low amplitude beta with multiple and varying frequencies is often associated with active, busy or anxious thinking and active concentration. Rhythmic beta with a dominant set of frequencies is associated with various pathologies and drug effects, especially benzodiazepines. It may be absent or reduced in areas of cortical damage. It is the dominant rhythm in patients who are alert or anxious or who have their eyes open. • Gamma is the frequency range approximately 30–100 Hz. Gamma rhythms are thought to represent binding of different populations of neurons together into a network for the purpose of carrying out a certain cognitive or motor function.[4]

gamma waves.

• Mu ranges 8–13 Hz., and partly overlaps with other frequencies. It reflects the synchronous firing of motor neurons in rest state. Mu suppression is thought to reflect motor mirror neuron systems, because when an action is observed, the pattern extinguishes, possibly because of the normal neuronal system and the mirror neuron system "go out of sync", and interfere with each other.[40] "Ultra-slow" or "near-DC" (Direct current) activity is recorded using DC amplifiers in some research contexts. It is not typically recorded in a clinical context because the signal at these frequencies is susceptible to a number of artifacts. Some features of the EEG are transient rather than rhythmic. Spikes and sharp waves may represent seizure activity or interictal activity in individuals with epilepsy or a predisposition toward epilepsy. Other transient features are normal: vertex waves and sleep spindles are seen in normal sleep. Note that there are types of activity that are statistically uncommon, but not associated with dysfunction or disease. These are often referred to as "normal variants." The mu rhythm is an example of a normal variant. The normal Electroencephalography (EEG) varies by age. The neonatal EEG is quite different from the adult EEG. The EEG in childhood generally has slower frequency oscillations than the adult EEG. The normal EEG also varies depending on state. The EEG is used along with other measurements (EOG, EMG) to define sleep stages in polysomnography. Stage I sleep (equivalent to drowsiness in some systems) appears on the EEG as drop-out of the posterior basic rhythm. There can be an increase in theta frequencies. Santamaria and Chiappa cataloged a number of the variety of patterns associated with drowsiness. Stage II sleep is characterized by sleep spindles—transient runs of rhythmic activity in the 12–14 Hz range (sometimes referred to as the "sigma" band) that have a frontal-central maximum. Most of the activity in Stage II is in the 3–6 Hz range. Stage III and IV sleep are defined by the presence of delta frequencies and are often referred to collectively as "slow-wave sleep." Stages I-IV comprise non-REM (or "NREM") sleep. The EEG in REM (rapid eye movement) sleep appears somewhat similar to the awake EEG. EEG under general anesthesia depends on the type of anesthetic employed. With halogenated anesthetics, such as halothane or intravenous agents, such as propofol, a rapid (alpha or low beta), nonreactive EEG pattern is seen over most of the scalp, especially anteriorly; in some older terminology this was known as a WAR (widespread anterior rapid) pattern, contrasted with a WAIS (widespread slow) pattern associated with high doses of opiates. Anesthetic

Electroencephalography effects on EEG signals are beginning to be understood at the level of drug actions on different kinds of synapses and the circuits that allow synchronized neuronal activity (see: http://www.stanford.edu/group/maciverlab/).

Artifacts Biological artifacts Electrical signals detected along the scalp by an EEG, but that originate from non-cerebral origin are called artifacts. EEG data is almost always contaminated by such artifacts. The amplitude of artifacts can be quite large relative to the size of amplitude of the cortical signals of interest. This is one of the reasons why it takes considerable experience to correctly interpret EEGs clinically. Some of the most common types of biological artifacts include: • • • •

Eye-induced artifacts (includes eye blinks, eye movements and extra-ocular muscle activity) ECG (cardiac) artifacts EMG (muscle activation)-induced artifacts Glossokinetic artifacts

The most prominent eye-induced artifacts are caused by the potential difference between the cornea and retina, which is quite large compared to cerebral potentials. When the eyes and eyelids are completely still, this corneo-retinal dipole does not affect EEG. However, blinks occur several times per minute, the eyes movements occur several times per second. Eyelid movements, occurring mostly during blinking or vertical eye movements, elicit a large potential seen mostly in the difference between the Electrooculography (EOG) channels above and below the eyes. An established explanation of this potential regards the eyelids as sliding electrodes that short-circuit the positively charged cornea to the extra-ocular skin.[45][46] Rotation of the eyeballs, and consequently of the corneo-retinal dipole, increases the potential in electrodes towards which the eyes are rotated, and decrease the potentials in the opposing electrodes.[47] Eye movements called saccades also generate transient electromyographic potentials, known as saccadic spike potentials (SPs).[48] The spectrum of these SPs overlaps the gamma-band (see Gamma wave), and seriously confounds analysis of induced gamma-band responses,[49] requiring tailored artifact correction approaches.[48] Purposeful or reflexive eye blinking also generates electromyographic potentials, but more importantly there is reflexive movement of the eyeball during blinking that gives a characteristic artifactual appearance of the EEG (see Bell's phenomenon). Eyelid fluttering artifacts of a characteristic type were previously called Kappa rhythm (or Kappa waves). It is usually seen in the prefrontal leads, that is, just over the eyes. Sometimes they are seen with mental activity. They are usually in the Theta (4–7 Hz) or Alpha (8–13 Hz) range. They were named because they were believed to originate from the brain. Later study revealed they were generated by rapid fluttering of the eyelids, sometimes so minute that it was difficult to see. They are in fact noise in the EEG reading, and should not technically be called a rhythm or wave. Therefore, current usage in electroencephalography refers to the phenomenon as an eyelid fluttering artifact, rather than a Kappa rhythm (or wave).[50] Some of these artifacts can be useful in various applications. The EOG signals, for instance, can be used to detect[48] and track eye-movements, which are very important in polysomnography, and is also in conventional EEG for assessing possible changes in alertness, drowsiness or sleep. EKG artifacts are quite common and can be mistaken for spike activity. Because of this, modern EEG acquisition commonly includes a one-channel EKG from the extremities. This also allows the EEG to identify cardiac arrhythmias that are an important differential diagnosis to syncope or other episodic/attack disorders. Glossokinetic artifacts are caused by the potential difference between the base and the tip of the tongue. Minor tongue movements can contaminate the EEG, especially in parkinsonian and tremor disorders.

25

Electroencephalography

Environmental artifacts In addition to artifacts generated by the body, many artifacts originate from outside the body. Movement by the patient, or even just settling of the electrodes, may cause electrode pops, spikes originating from a momentary change in the impedance of a given electrode. Poor grounding of the EEG electrodes can cause significant 50 or 60 Hz artifact, depending on the local power system's frequency. A third source of possible interference can be the presence of an IV drip; such devices can cause rhythmic, fast, low-voltage bursts, which may be confused for spikes.

Artifact correction Recently, independent component analysis techniques have been used to correct or remove EEG contaminates.[48][51][52][53][54] These techniques attempt to "unmix" the EEG signals into some number of underlying components. There are many source separation algorithms, often assuming various behaviors or natures of EEG. Regardless, the principle behind any particular method usually allow "remixing" only those components that would result in "clean" EEG by nullifying (zeroing) the weight of unwanted components. Fully automated artifact rejection methods, which use ICA, have also been developed.[55]

Abnormal activity Abnormal activity can broadly be separated into epileptiform and non-epileptiform activity. It can also be separated into focal or diffuse. Focal epileptiform discharges represent fast, synchronous potentials in a large number of neurons in a somewhat discrete area of the brain. These can occur as interictal activity, between seizures, and represent an area of cortical irritability that may be predisposed to producing epileptic seizures. Interictal discharges are not wholly reliable for determining whether a patient has epilepsy nor where his/her seizure might originate. (See focal epilepsy.) Generalized epileptiform discharges often have an anterior maximum, but these are seen synchronously throughout the entire brain. They are strongly suggestive of a generalized epilepsy. Focal non-epileptiform abnormal activity may occur over areas of the brain where there is focal damage of the cortex or white matter. It often consists of an increase in slow frequency rhythms and/or a loss of normal higher frequency rhythms. It may also appear as focal or unilateral decrease in amplitude of the EEG signal. Diffuse non-epileptiform abnormal activity may manifest as diffuse abnormally slow rhythms or bilateral slowing of normal rhythms, such as the PBR. Intracortical Encephalogram electrodes and sub-dural electrodes can be used in tandem to discriminate and discretize artifact from epileptiform and other severe neurological events. More advanced measures of abnormal EEG signals have also recently received attention as possible biomarkers for different disorders such as Alzheimer's disease.[56]

26

Electroencephalography

History A timeline of the history of EEG is given by Swartz.[57] Richard Caton (1842–1926), a physician practicing in Liverpool, presented his findings about electrical phenomena of the exposed cerebral hemispheres of rabbits and monkeys in the British Medical Journal in 1875. In 1890, Polish physiologist Adolf Beck published an investigation of spontaneous electrical activity of the brain of rabbits and dogs that included rhythmic oscillations altered by light. In 1912, Russian physiologist, Vladimir Vladimirovich Pravdich-Neminsky published the first animal EEG and the evoked potential of the mammalian (dog).[58] In 1914, Napoleon Cybulski and Jelenska-Macieszyna photographed EEG-recordings of experimentally induced seizures. German physiologist and psychiatrist Hans Berger (1873–1941) recorded the first human EEG in 1924.[59] Expanding on work previously conducted on animals by Richard Caton and others, Berger also invented the electroencephalogram (giving the device its name), an invention described Hans Berger "as one of the most surprising, remarkable, and momentous developments in the history of clinical neurology".[60] His discoveries were first confirmed by British scientists Edgar Douglas Adrian and B. H. C. Matthews in 1934 and developed by them. In 1934, Fisher and Lowenback first demonstrated epileptiform spikes. In 1935 Gibbs, Davis and Lennox described interictal spike waves and the 3 cycles/s pattern of clinical absence seizures, which began the field of clinical electroencephalography. Subsequently, in 1936 Gibbs and Jasper reported the interictal spike as the focal signature of epilepsy. The same year, the first EEG laboratory opened at Massachusetts General Hospital. Franklin Offner (1911–1999), professor of biophysics at Northwestern University developed a prototype of the EEG that incorporated a piezoelectric inkwriter called a Crystograph (the whole device was typically known as the Offner Dynograph). In 1947, The American EEG Society was founded and the first International EEG congress was held. In 1953 Aserinsky and Kleitman describe REM sleep. In the 1950s, William Grey Walter developed an adjunct to EEG called EEG topography, which allowed for the mapping of electrical activity across the surface of the brain. This enjoyed a brief period of popularity in the 1980s and seemed especially promising for psychiatry. It was never accepted by neurologists and remains primarily a research tool.

Various uses The EEG has been used for many purposes besides the conventional uses of clinical diagnosis and conventional cognitive neuroscience. An early use was during World War II by the U.S. Army Air Corps to screen out pilots in danger of having seizures;[61] long-term EEG recordings in epilepsy patients are still used today for seizure prediction. Neurofeedback remains an important extension, and in its most advanced form is also attempted as the basis of brain computer interfaces. The EEG is also used quite extensively in the field of neuromarketing. Honda is attempting to develop a system to enable an operator to control its Asimo robot using EEG, a technology it eventually hopes to incorporate into its automobiles.[62] EEGs have been used as evidence in trials in the Indian state of Maharastra.[63][64]

27

Electroencephalography

EEG and Remote Communication The United States Army Research Office budgeted $4 million in 2009 to researchers at the University of California, Irvine to develop EEG processing techniques to identify correlates of imagined speech and intended direction to enable soldiers on the battlefield to communicate via computer-mediated reconstruction of team members' EEG signals, in the form of understandable signals such as words.[65]

Low-cost EEG Devices Inexpensive EEG devices exist for the low-cost research and consumer markets. Recently, a few companies have miniaturized medical grade EEG technology to create versions accessible to the wider public. Some of these companies have even built commercial EEG devices retailing for less than $100 USD. • In 2004 OpenEEG released its ModularEEG as open source hardware. Compatible open source software includes a game for balancing a ball. • In 2007 NeuroSky released the first affordable consumer based EEG along with the game NeuroBoy. This was also the first large scale EEG device to use dry sensor technology.[66] • In 2008 OCZ Technology developed device for use in video games relying primarily on electromyography. • In 2008 the Final Fantasy developer Square Enix announced that it was partnering with NeuroSky to create a game, Judecca.[67][68] • In 2009 Mattel partnered with NeuroSky to release the Mindflex, a game that used an EEG to steer a ball through an obstacle course. By far the best selling consumer based EEG to date.[67][69] • In 2009 Uncle Milton Industries partnered with NeuroSky to release the StarWars Force Trainer, a game designed to create the illusion of possessing The Force.[67][70] • In 2009 Emotiv released the EPOC, a 14 channel EEG device. The EPOC is the first commercial BCI to not use dry sensor technology, requiring users to apply a saline solution to their head.[71] • In 2010, NeuroSky added a blink and electromyography function to the MindSet.[72] • In 2011, NeuroSky released the MindWave. An EEG device designed for educational purposes and games.[73] The MindWave won the Guinness Book of World Records award for “Heaviest machine moved using a brain control interface”.[74] • In 2012, a Japanese gadget project, neurowear, released Necomimi: a headset with motorized cat ears. The headset is a NeuroSky MindWave unit with two motors on the headband where a cat's ears might be. Slipcovers shaped like cat ears sit over the motors so that as the device registers emotional states the ears move to relate. For example, when relaxed, the ears fall to the sides and perk up when excited again.

28

Electroencephalography

29

Images

Person wearing electrodes for EEG

Portable recording device for EEG

EEG electroencephalophone used during a music performance in which bathers from around the world were networked together as part of a collective musical performance, using their brainwaves to control sound, lighting, and the bath environment

References [1] [2] [3] [4]

http:/ / icd9cm. chrisendres. com/ index. php?srchtype=procs& srchtext=89. 14& Submit=Search& action=search http:/ / www. nlm. nih. gov/ cgi/ mesh/ 2011/ MB_cgi?field=uid& term=D004569 http:/ / ops. icd-code. de/ ops/ code/ 1-207. html Niedermeyer E. and da Silva F.L. (2004). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincot Williams & Wilkins. ISBN 0-7817-5126-8. [5] Atlas of EEG & Seizure Semiology. B. Abou-Khalil; Musilus, K.E.; Elsevier, 2006. [6] "EEG" (http:/ / www. nlm. nih. gov/ medlineplus/ ency/ article/ 003931. htm). . [7] Tatum, W. O., Husain, A. M., Benbadis, S. R. (2008) "Handbook of EEG Interpretation" Demos Medical Publishing. [8] Nunez PL, Srinivasan R (1981). Electric fields of the brain: The neurophysics of EEG (http:/ / books. google. com/ books?id=gu5qAAAAMAAJ). Oxford University Press. . [9] Klein, S.; Thorne, B. M. (3 October 2006). Biological psychology. New York, N.Y.: Worth. ISBN 0-7167-9922-7. [10] Whittingstall, K; Logothetis, NK. (2009). "Frequency-band coupling in surface EEG reflects spiking activity in monkey visual cortex". Neuron 64 (2): 281–9. doi:10.1016/j.neuron.2009.08.016. PMID 19874794. [11] Vespa, P., Nenov, V., & Nuwer, M. (1999). Continuous EEG monitoring in the intensive care unit: early findings and clinical efficacy. J Clin Neurophysiol, 16: 1–13. [12] Hamalainen, M., Riitta, H., Ilmoniemi, R., Knuutila, J., & Lounasmaa, O. (1993). Magnetoencephalography—theory, instrumentation, and applications to noninvasive studies of the working human brain. Reviews of Modern Physics, 65(2), 414-497. [13] O'Regan, S., Faul, S., & Marnane, W. (2010). "Automatic detection of EEG artefacts arising from head movements." Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, Buenos Aires, Argentina, August 31 - September 4, 2010, 6353-6356. [14] Murphy, K., & Brunberg, J. (1997). "Adult claustrophobia, anxiety and sedation in MRI." Magnetic Resonance Imaging, 15(1), 51-54. [15] Schenck, J. (1996). "The role of magnetic susceptibility in magnetic resonance imaging: MRI magnetic compatibility of the first and second kinds." Med. Phys., 23(6), 815-850 [16] Yasuno et al. (2008). "The PET Radioligand [11C]MePPEP Binds Reversibly and with High Specific Signal to Cannabinoid CB1 Receptors in Nonhuman Primate Brain". Neuropsychopharmacology 33: 259–269. [17] Mulholland, T. (1973). "Objective EEG methods for studying covert shifts of visual attention." In F. J. McGuigan and J. Schoonover (Eds.), The Psychophysiology of thinking. Academic Press: New York, 109-151. [18] Hinterberger, T., Kübler, A., Kaiser, J., Neumann, N., & Birbaumer, N. (2003). A brain-computer interface (BCI) for the locked-in: comparison of different EEG classifications for the thought translation device. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 114(3), 416-25.

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New York, NY: Worth. p. 17. ISBN 0-7167-0110-3. [32] Creutzfeldt OD, Watanabe S, Lux HD (1966). "Relations between EEG phenomena and potentials of single cortical cells. I. Evoked responses after thalamic and epicortical stimulation". Electroencephalogr Clin Neurophysiol 20 (1): 1–18. doi:10.1016/0013-4694(66)90136-2. PMID 4161317. [33] Hamalainen M, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV (1993). "Magnetoencphalography - Theory, instrumentation, and applications to noninvasive studies of the working human brain". Reviews of Modern Physics 65 (2): 413–497. Bibcode 1993RvMP...65..413H. doi:10.1103/RevModPhys.65.413. [34] Buzsaki G (2006). Rhythms of the brain. Oxford University Press. ISBN 0-19-530106-4. [35] http:/ / www. nbtwiki. net/ [36] Kirmizialsan, E.; Bayraktaroglu, Z.; Gurvit, H.; Keskin, Y.; Emre, M.; Demiralp, T. (2006). "Comparative analysis of event-related potentials during Go/NoGo and CPT: Decomposition of electrophysiological markers of response inhibition and sustained attention". Brain Research 1104 (1): 114–128. doi:10.1016/j.brainres.2006.03.010. PMID 16824492. [37] Kisley, M. A.; Cornwell, Z. M. (2006). "Gamma and beta neural activity evoked during a sensory gating paradigm: Effects of auditory, somatosensory and cross-modal stimulation". Clinical Neurophysiology 117 (11): 2549–2563. doi:10.1016/j.clinph.2006.08.003. PMC 1773003. PMID 17008125. [38] Kanayama, N.; Sato, A.; Ohira, H. (2007). "Crossmodal effect with rubber hand illusion and gamma-band activity". Psychophysiology 44 (3): 392–402. doi:10.1111/j.1469-8986.2007.00511.x. PMID 17371495. [39] Gastaut, H. (1952). "Etude electrocorticographique de al reactivite des rhytmes rolandiques". Rev. Neurol 87 (2): 176–182. PMID 13014777. [40] Oberman, LM; Hubbard, EM; McCleery, JP; Altschuler, EL; Ramachandran, VS; Pineda, JA (2005). "EEG Evidence for mirror neuron dysfunction in autism spectrum disorders". Cognitive Brain Research 24 (2): 190–198. doi:10.1016/j.cogbrainres.2005.01.014. PMID 15993757. [41] Cahn, B.R.; Polich, J. (2006). "Meditation states and traits: EEG, ERP, and neuroimaging studies". Psychological Bulletin 132 (2): 180–211. doi:10.1037/0033-2909.132.2.180. PMID 16536641. [42] Niedermeyer, E (1997). "Alpha rhythms as physiological and abnormal phenomena". Int J Psychophysiol 26 (1–3): 31–49. doi:10.1016/S0167-8760(97)00754-X. PMID 9202993. [43] Feshchenko, VA; Reinsel, RA; Veselis, RA (2001). "Multiplicity of the alpha rhythm in normal humans". J Clin Neurophysiol 18 (4): 331–44. doi:10.1097/00004691-200107000-00005. PMID 11673699. [44] Pfurtscheller G, Lopes da Silva FH (1999). "Event-related EEG/MEG synchronization and desynchronization: basic principles". Clin Neurophysiol 110 (11): 1842–1857. doi:10.1016/S1388-2457(99)00141-8. PMID 10576479. [45] Barry, W; Jones, GM (1965). "INFLUENCE OF EYE LID MOVEMENT UPON ELECTRO-OCULOGRAPHIC RECORDING OF VERTICAL EYE MOVEMENTS". Aerospace medicine 36: 855–858. PMID 14332336. [46] Iwasaki, M.; Kellinghaus, C.; Alexopoulos, A.V.; Burgess, R.C.; Kumar, A.N.; Han, Y.H.; Lüders, H.O.; Leigh, R.J. (2005). "Effects of eyelid closure, blinks, and eye movements on the electroencephalogram". Clinical Neurophysiology 116 (4): 878–885.

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"Removing electroencephalographic artifacts by blind source separation". Psychophysiology 37 (2): 163–178. doi:10.1017/S0048577200980259. PMID 10731767. [52] Jung, T.P.; Makeig, S.; Westerfield, M.; Townsend, J.; Courchesne, E.; Sejnowski, T.J. (2000b). "Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects". Clinical Neurophysiology 111 (10): 1745–1758. doi:10.1016/S1388-2457(00)00386-2. PMID 11018488. [53] Joyce, Carrie A.; Gorodnitsky, Irina F.; Kutas, Marta (2004). "Automatic removal of eye movement and blink artifacts from EEG data using blind component separation". Psychophysiology 41 (2): 313–325. doi:10.1111/j.1469-8986.2003.00141.x. PMID 15032997. [54] Shackman, AJ; McMenamin, BW; Maxwell, JS; Greischar, LL; Davidson, RJ (2010). "Identifying robust and sensitive frequency bands for interrogating neural oscillations". NeuroImage 51 (4): 1319–1333. doi:10.1016/j.neuroimage.2010.03.037. PMC 2871966. PMID 20304076. [55] Nolan, H.; Whelan, R.; Reilly, R.B. (2010). "FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection". Journal of Neuroscience Methods 192 (1): 152–162. doi:10.1016/j.jneumeth.2010.07.015. PMID 20654646. [56] Montez T, Poil S-S, Jones BF, Manshanden I, Verbunt JPA, van Dijk BW, Brussaard AB, van Ooyen A, Stam CJ, Scheltens P, Linkenkaer-Hansen K (2009). "Altered temporal correlations in parietal alpha and prefrontal theta oscillations in early-stage Alzheimer disease" (http:/ / www. pnas. org/ content/ 106/ 5/ 1614. abstract). PNAS 106 (5): 1614–1619. Bibcode 2009PNAS..106.1614M. doi:10.1073/pnas.0811699106. PMC 2635782. PMID 19164579. . [57] Article must be purchased. Swartz, B.E; Goldensohn, ES (1998). "Timeline of the history of EEG and associated fields" (http:/ / www. sciencedirect. com/ science?_ob=MImg& _imagekey=B6SYX-4FV4S6H-1-1& _cdi=4846& _user=10& _orig=browse& _coverDate=02/ 28/ 1998& _sk=998939997& view=c& wchp=dGLbVzz-zSkWb& md5=47fbbe7e51a806779716fba415b96ab7& ie=/ sdarticle. pdf) (PDF). Electroencephalography and clinical Neurophysiology 106 (2): 173–176. doi:10.1016/S0013-4694(97)00113-2. PMID 9741779. . [58] Pravdich-Neminsky, VV. (1913). "Ein Versuch der Registrierung der elektrischen Gehirnerscheinungen". Zbl Physiol 27: 951–960. [59] Haas, L F (2003). "Hans Berger (1873-1941), Richard Caton (1842-1926) and electroencephalography". Journal of Neurology, Neurosurgery & Psychiatry 74 (1): 9. doi:10.1136/jnnp.74.1.9. PMC 1738204. PMID 12486257. [60] Millet, David (2002). "The Origins of EEG". (http:/ / www. bri. ucla. edu/ nha/ ishn/ ab24-2002. htm) International Society for the History of the Neurosciences (ISHN). [61] Keiper, Adam. "The Age of Neuroelectronics" (http:/ / www. thenewatlantis. com/ publications/ the-age-of-neuroelectronics). The New Atlantis. . [62] (http:/ / search. japantimes. co. jp/ cgi-bin/ nb20090401a2. html) 1 Apr 2009, Japan Times [63] This brain test maps the truth (http:/ / timesofindia. indiatimes. com/ Cities/ This_brain_test_maps_the_truth/ articleshow/ 3257032. cms) 21 Jul 2008, 0348 hrs IST, Nitasha Natu,TNN [64] "Puranik, D.A., Joseph, S.K., Daundkar, B.B., Garad, M.V. (2009). Brain Signature profiling in India. Its status as an aid in investigation and as corroborative evidence – as seen from judgments. Proceedings of XX All India Forensic Science Conference, 815 – 822, November 15 – 17, Jaipur." (http:/ / www. axxonet. com/ cms-filesystem-action/ publications/ beos_in_india. pdf). . [65] MURI: Synthetic Telepathy (http:/ / cnslab. ss. uci. edu/ muri/ index. html). Cnslab.ss.uci.edu. Retrieved 2011-07-19. 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[72] "News - NeuroSky Upgrades SDK, Allows For Eye Blink, Brainwave-Powered Games" (http:/ / www. gamasutra. com/ view/ news/ 29190/ NeuroSky_Upgrades_SDK_Allows_For_Eye_Blink_BrainwavePowered_Games. php). Gamasutra. 2010-06-30. . Retrieved 2010-12-02. [73] Fiolet, Eliane. "NeuroSky MindWave Brings Brain-Computer Interface to Education" (http:/ / venturebeat. com/ 2010/ 06/ 22/ neurosky-raises-11-8m-for-brainwave-controlled-games/ ). www.ubergizmo.com. Ubergizmo. . [74] "NeuroSky MindWave Sets Guinness World Record for "Largest Object Moved Using a Brain-Computer Interface"" (http:/ / neurogadget. com/ 2011/ 04/ 12/ neurosky-mindwave-sets-guinness-world-record-for-“largest-object-moved-using-a-brain-computer-interface”/ 1820). NeuroGadget.com. NeuroGadget. .

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Electroencephalography

External links • A tutorial on simulating and estimating EEG sources in Matlab (http://www.nbtwiki.net/doku. php?id=tutorial:tutorial_dipoles) • A tutorial on analysis of ongoing, evoked, and induced neuronal activity: Power spectra, wavelet analysis, and coherence (http://www.nbtwiki.net/doku.php?id=tutorial:power_spectra_wavelet_analysis_and_coherence) • Scholarpedia EEG (http://www.scholarpedia.org/article/Electroencephalogram) • FASTER (http://www.mee.tcd.ie/neuraleng/Research.Faster) A fully automated, unsupervised method for processing of high density EEG data. FASTER has been peer-reviewed, it is free and the software is open source. The FASTER software is available here. (https://sourceforge.net/projects/faster) • Video demonstration of placement of electrodes (http://www.youtube.com/watch?v=IwGIF5aCnqg& feature=digest) • OpenEEG (http://openeeg.sourceforge.net/doc/) The OpenEEG project makes hardware plans and software for do-it-yourself EEG devices in an Open Source manner. The hardware is aimed toward amateurs who would like to experiment with EEG. • (http://www.caet.org) Canadian association of EEG techs (CAET)

32

Thalamus

33

Thalamus Brain: Thalamus

thalamus marked (MRI cross-section)

anterolateral view Latin

thalamus dorsalis

Gray's

subject #189 808

Part of

Diencephalon

[1]

Components See List of thalamic nuclei Artery

Posterior cerebral artery and branches

NeuroNames hier-283 [2] MeSH

Thalamus

[3]

NeuroLex ID birnlex_954 [4]

The thalamus (from Greek θάλαμος, "inner chamber")[5] is a midline symmetrical structure within the brains of vertebrates including humans, situated between the cerebral cortex and midbrain. Its function includes relaying sensory and motor signals to the cerebral cortex,[6][7] along with the regulation of consciousness, sleep, and alertness. The thalamus surrounds the third ventricle. It is the main product of the embryonic diencephalon.

Thalamus

34

Anatomy The thalamus is perched on top of the brainstem, near the center of the brain, with nerve fibers projecting out to the cerebral cortex in all directions. The medial surface of the thalamus constitutes the upper part of the lateral wall of the third ventricle, and is connected to the corresponding surface of the opposite thalamus by a flattened gray band, the Interthalamic adhesion.

Morphology Both parts of this structure of the brain in the human are each about the size and shape of a walnut.[6] These are about three centimetres in length, at the widest part 2.5 centimetres across and about 2 centimetres in height (the nut relative to an unshelled nut with the nut-shell join in the horizontal plane).

The two thalami in a 360o rotation

The two halves of the thalamus are prominent bulb-shaped masses, about 5.7 cm in length, located obliquely (about 30°) and symmetrically on each side of the third ventricle.

Blood supply The thalamus derives its blood supply from four arteries including the polar artery (posterior communicating artery), paramedian thalamic-subthalamic arteries, inferolateral (thalamogeniculate) arteries, and posterior (medial and lateral) choroidal arteries.[8] These are all derived from the vertebrobasilar arterial system except the polar artery. Some people have the artery of Percheron, which is a rare anatomic variation in which a single arterial trunk arises from the posterior cerebral artery to supply both thalami.

Thalamic nuclei The thalamus is part of a nuclear complex structured of four parts, the hypothalamus, epithalamus, ventral thalamus, and dorsal thalamus.[9] Derivatives of the diencephalon also include the dorsally-located epithalamus (essentially the habenula and annexes) and the perithalamus (prethalamus formerly described as ventral thalamus) containing the zona incerta and the "reticulate nucleus" (not the reticular, term of confusion). Due to their different ontogenetic origins, the epithalamus and the perithalamus are formally distinguished from the thalamus proper. Nuclei of the thalamus The thalamus comprises a system of lamellae (made up of myelinated fibers) separating different thalamic subparts. Other areas are defined by distinct clusters of neurons, such as the periventricular gray, the intralaminar elements, the "nucleus limitans", and others.[10] These latter structures, different in structure from the major part of the thalamus, have been grouped together into the allothalamus as opposed to the isothalamus.[11] This distinction simplifies the global description of the thalamus.

Thalamus

35

Connections The thalamus is manifoldly connected to the hippocampus via the mammillo-thalamic tract, this tract comprises the mammilary body and fornix.[12] The spinothalamic tract is a sensory pathway originating in the spinal cord. It transmits information to the thalamus about pain, temperature, itch and crude touch. There are two main parts: the lateral spinothalamic tract, which transmits pain and temperature, and the anterior (or ventral) spinothalamic tract, which transmits crude touch and pressure.

The thalamus is connected to the spinal cord via the spinothalamic tract.

Function The thalamus has multiple functions. It may be thought of as a kind of switchboard of information. It is generally believed to act as a relay between a variety of subcortical areas and the cerebral cortex. In particular, every sensory system (with the exception of the olfactory system) includes a thalamic nucleus that receives sensory signals and sends them to the associated primary cortical area. For the visual system, for example, inputs from the retina are sent to the lateral geniculate nucleus of the thalamus, which in turn projects to the primary visual cortex (area V1) in the occipital lobe. The thalamus is believed to both process sensory information as well as relay it—each of the primary sensory relay areas receives strong "back projections" from the cerebral cortex. Similarly the medial geniculate nucleus acts as a key auditory relay between the inferior colliculus of the midbrain and the primary auditory cortex, and the ventral posterior nucleus is a key somatosensory relay, which sends touch and proprioceptive information to the primary somatosensory cortex. The thalamus also plays an important role in regulating states of sleep and wakefulness.[13] Thalamic nuclei have strong reciprocal connections with the cerebral cortex, forming thalamo-cortico-thalamic circuits that are believed to be involved with consciousness. The thalamus plays a major role in regulating arousal, the level of awareness, and activity. Damage to the thalamus can lead to permanent coma. The role of the thalamus in the more anterior pallidal and nigral territories in the basal ganglia system disturbances is recognized but still poorly understood. The contribution of the thalamus to vestibular or to tectal functions is almost ignored. The thalamus has been thought of as a "relay" that simply forwards signals to the cerebral cortex. Newer research suggests that thalamic function is more selective.[14] Many different functions are linked to various regions of the thalamus. This is the case for many of the sensory systems (except for the olfactory system), such as the auditory, somatic, visceral, gustatory and visual systems where localized lesions provoke specific sensory deficits. A major role of the thalamus is devoted to "motor" systems. The thalamus is functionally connected to the hippocampus[15] as part of the extended hippocampal system at the thalamic anterior nuclei[16] with respect to spatial memory and spatial sensory datum they are crucial for human episodic memory and rodent event memory.[17][18] There is support for the hypothesis that thalamic regions connection to particular parts of the mesio-temporal lobe provide differentiation of the functioning of recollective and familiarity memory.[12] The neuronal information processes necessary for motor control were proposed as a network involving the thalamus as a subcortical motor centre.[19] Through investigations of the anatomy of the brains of primates[20] the nature of the interconnected tissues of the cerebellum to the multiple motor cortices suggested that the thalamus fulfills a key function in providing the specific channels from the basal ganglia and cerebellum to the cortical motor areas.[21][22] In an investigation of the saccade and antisaccade[23] motor response in three monkeys, the thalamic regions were found to be involved in the generation of antisaccade eye-movement.[24]

Thalamus

36

Human brain frontal (coronal) section

Development The thalamic complex is composed of the perithalamus (or prethalamus, previously also known as ventral thalamus), the mid-diencephalic organiser (which forms later the zona limitans intrathalamica (ZLI) ) and the thalamus (dorsal thalamus).[25][26] The development of the thalamus can be subdivide into three steps[27] The thalamus is the largest structure deriving from the embryonic diencephalon, the posterior part of the forebrain situated between the midbrain and the cerebrum.

Early brain development After neurulation the anlage of the prethalamus and the thalamus is induced within the neural tube. Data from different vertebrate model organisms support a model in which the interaction between two transcription factors, Fez and Otx, are of decisive importance. Fez is expressed in the prethalamus, and functional experiments show that Fez is required for prethalamus formation.[28][29] Posteriorly, Otx1 and Otx2 abut the expression domain of Fez and are required for proper development of the thalamus.[30][31]

The formation of the mid-diencephalic organiser (MDO) At the interface between the expression domains of Fez and Otx, the mid-diencephalic organizer (MDO, also called the ZLI organiser) is induced within the thalamic anlage. The MDO is the central signalling organizer in the thalamus. A lack of the organizer leads to the absence of the thalamus. The MDO matures from ventral to dorsal during development. Members of the SHH family and of the Wnt family are the main principal signals emitted by the MDO. Besides its importance as signalling center, the organizer matures into the morphological structure of the zona limitans intrathalamica (ZLI).

Thalamus

Maturation and parcellation of the thalamus After its induction, the MDO starts to orchestrate the development of the thalamic anlage by release of signalling molecules such as Shh.[32] In mice, the function of signaling at the MDO has not been addressed directly due to a complete absence of the diencephalon in Shh mutants.[33] Studies in chicks have shown that Shh is both necessary and sufficient for thalamic gene induction.[34] In zebrafish, it was shown that the expression of two Shh genes, shh-a and shh-b (formerly described as twhh) mark the MDO territory, and that Shh signaling is sufficient for the molecular differentiation of both the prethalamus and the thalamus but is not required for their maintenance and Shh signaling from the MDO/alar plate is sufficient for the maturation of prethalamic and thalamic territory while ventral Shh signals are dispensable.[35] The exposure to Shh leads to differentiation of thalamic neurons. SHH signaling from the MDO induces a posterior-to-anterior wave of expression the proneural gene Neurogenin1 in the major (caudal) part of the thalamus, and Ascl1 (formerly Mash1) in the remaining narrow stripe of rostral thalamic cells immediately adjacent to the MDO, and in the prethalamus.[36][37] This zonation of proneural gene expression leads to the differentiation of glutamatergic relay neurons from the Neurogenin1+ precursors and of GABAergic inhibitory neurons from the Ascl1+ precursors. In fish, selection of these alternative neurotransmitter fates is controlled by the dynamic expression of Her6 the homolog of HES1. Expression of this hairy-like bHLH transcription factor, which represses Neurogenin but is required for Ascl1, is progressively lost from the caudal thalamus but maintained in the prethalamus and in the stripe of rostral thalamic cells. In addition, studies on chick and mice have shown that blocking the Shh pathway leads to absence of the rostral thalamus and substantial decrease of the caudal thalamus. The rostral thalamus will give rise to the reticular nucleus mainly whereby the caudal thalamus will form the relay thalamus and will be further subdivided in the thalamic nuclei.[27] In humans, a common genetic variation in the promotor region of the serotonin transporter (the SERT-long and -short allele: 5-HTTLPR) has been shown to affect the development of several regions of the thalamus in adults. People who inherit two short alleles (SERT-ss) have more neurons and a larger volume in the pulvinar and possibly the limbic regions of the thalamus. Enlargement of the thalamus provides an anatomical basis for why people who inherit two SERT-ss alleles are more vulnerable to major depression, posttraumatic stress disorder, and suicide.[38]

Pathology A cerebrovascular accident (stroke) can lead to the thalamic syndrome,[39] which involves a one-sided burning or aching sensation often accompanied by mood swings. Bilateral ischemia of the area supplied by the paramedian artery can cause serious problems including akinetic mutism, and be accompanied by oculomotor problems. A related concept is thalamocortical dysrhythmia. The occlusion of the artery of Percheron can lead to a bilateral thalamus infarction. Korsakoff's syndrome stems from damage to the mammillary body, the mammillothalamic fasciculus or the thalamus. Fatal familial insomnia is a hereditary prion disease in which degeneration of the thalamus occurs, causing the patient to gradually lose his ability to sleep and progressing to a state of total insomnia, which invariably leads to death.

37

Thalamus

38

Grays (Images) Images are circa 1858.[40]

Coronal section of lateral and third ventricles.

The left optic nerve and the optic tracts.

Dissection showing the ventricles of the brain.

Mesal aspect of a brain sectioned in the median sagittal plane.

Section of brain showing upper surface of temporal lobe.

Schematic representation of the chief ganglionic categories (I to V).

Scheme showing the course of the fibers of the lemniscus; medial lemniscus in blue, lateral in red.

Horizontal section of right cerebral hemisphere.

Deep dissection of brain-stem. Lateral view.

Thalamus

39

Deep dissection of brain-stem. Ventral view.

Coronal section of brain immediately in front of pons.

Coronal section of brain through intermediate mass of third ventricle.

Thalamus

References [1] [2] [3] [4] [5]

http:/ / education. yahoo. com/ reference/ gray/ subjects/ subject?id=189#p808 http:/ / braininfo. rprc. washington. edu/ Scripts/ hiercentraldirectory. aspx?ID=283 http:/ / www. nlm. nih. gov/ cgi/ mesh/ 2007/ MB_cgi?mode=& term=Thalamus http:/ / www. neurolex. org/ wiki/ birnlex_954 Harper - index (http:/ / www. etymonline. com/ index. php?allowed_in_frame=0& search=thalamus& searchmode=none) & University of Washington Faculty Web Server (http:/ / faculty. washington. edu/ chudler/ neuroroot. html) & Search engine search page (http:/ / www. google. co. uk/ #hl=en& cp=18& gs_id=1y& xhr=t& q=etymology+ thalamus& pf=p& sclient=psy-ab& rlz=1W1GPCK_enGB446& source=hp& pbx=1& oq=etymology+ thalamus& aq=0v& aqi=g-v1& aql=& gs_sm=& gs_upl=& bav=on. 2,or. r_gc. r_pw. r_cp. ,cf. osb& fp=c5c3f35eb03724& biw=1600& bih=694) + Perseus Project tufts.edu (http:/ / www. perseus. tufts. edu/ hopper/ searchresults?q=thalamus) Retrieved 2012-02-09 [6] Sherman, S. (2006). "Thalamus". Scholarpedia 1 (9): 1583. doi:10.4249/scholarpedia.1583. [7] S. M. Sherman & Ray Guillery -ISBN 0-12-305460-5 → Elsevier B.V (http:/ / www. elsevier. com/ wps/ find/ bookdescription. cws_home/ 673351/ description#description) [Retrieved 2012-02-10] [8] Percheron, G. (1982). "The arterial supply of the thalamus". In Schaltenbrand; Walker, A. E.. Stereotaxy of the human brain. Stuttgart: Thieme. pp. 218–32. [9] Herrero, María-Trinidad; Barcia, Carlos; Navarro, Juana (2002). "Functional anatomy of thalamus and basal ganglia". Child's Nervous System 18 (8): 386. doi:10.1007/s00381-002-0604-1. [10] Jones Edward G.(2007) "The Thalamus" Cambridge Uni. Press [11] Percheron, G. (2003). "Thalamus". In Paxinos, G.; May, J.. The human nervous system (2nd ed.). Amsterdam: Elsevier. pp. 592–675. [12] Carlesimo, GA; Lombardi, MG; Caltagirone, C (2011). "Vascular thalamic amnesia: A reappraisal". Neuropsychologia 49 (5): 777–89. doi:10.1016/j.neuropsychologia.2011.01.026. PMID 21255590. [13] Steriade, Mircea; Llinás, Rodolfo R. (1988). "The Functional States of the Thalamus and the Associated Neuronal Interplay" (http:/ / physrev. physiology. org/ content/ 68/ 3/ 649. extract). Physiological Reviews 68 (3): 649–742. PMID 2839857. .

Thalamus [14] Leonard, Abigail W. (August 17, 2006). "Your Brain Boots Up Like a Computer" (http:/ / www. livescience. com/ 980-brain-boots-computer. html). LiveScience. . [15] Stein, Thor; Moritz, Chad; Quigley, Michelle; Cordes, Dietmar; Haughton, Victor; Meyerand, Elizabeth (2000). "Functional Connectivity in the Thalamus and Hippocampus Studied with Functional MR Imaging" (http:/ / www. ajnr. org/ cgi/ pmidlookup?view=long& pmid=11003270). American Journal of Neuroradiology 21 (8): 1397–401. PMID 11003270. . [16] Aggleton, John P.; Brown, Malcolm W. (1999). "Episodic memory, amnesia, and the hippocampal–anterior thalamic axis". Behavioral and Brain Sciences 22 (3): 425–44; discussion 444–89. doi:10.1017/S0140525X99002034. PMID 11301518. [17] Aggleton, John P.; O'Mara, Shane M.; Vann, Seralynne D.; Wright, Nick F.; Tsanov, Marian; Erichsen, Jonathan T. (2010). "Hippocampal-anterior thalamic pathways for memory: Uncovering a network of direct and indirect actions". European Journal of Neuroscience 31 (12): 2292–307. doi:10.1111/j.1460-9568.2010.07251.x. PMC 2936113. PMID 20550571. [18] Burgess, Neil; Maguire, Eleanor A; O'Keefe, John (2002). "The Human Hippocampus and Spatial and Episodic Memory". Neuron 35 (4): 625–41. doi:10.1016/S0896-6273(02)00830-9. PMID 12194864. [19] Evarts, E V; Thach, W T (1969). "Motor Mechanisms of the CNS: Cerebrocerebellar Interrelations". Annual Review of Physiology 31: 451–98. doi:10.1146/annurev.ph.31.030169.002315. PMID 4885774. [20] Orioli, PJ; Strick, PL (1989). "Cerebellar connections with the motor cortex and the arcuate premotor area: An analysis employing retrograde transneuronal transport of WGA-HRP". The Journal of comparative neurology 288 (4): 612–26. doi:10.1002/cne.902880408. PMID 2478593. [21] Asanuma et al. 1983; et al [22] Kurata, K (2005). "Activity properties and location of neurons in the motor thalamus that project to the cortical motor areas in monkeys". Journal of neurophysiology 94 (1): 550–66. doi:10.1152/jn.01034.2004. PMID 15703228. [23] http:/ / www. optomotorik. de/ blicken/ anti-rev. htm [24] Kunimatsu, J; Tanaka, M (2010). "Roles of the primate motor thalamus in the generation of antisaccades". The Journal of neuroscience 30 (14): 5108–17. doi:10.1523/JNEUROSCI.0406-10.2010. PMID 20371831. [25] Kuhlenbeck, Hartwig (1937). "The ontogenetic development of the diencephalic centers in a bird's brain (chick) and comparison with the reptilian and mammalian diencephalon". The Journal of Comparative Neurology 66: 23. doi:10.1002/cne.900660103. [26] Shimamura, K; Hartigan, DJ; Martinez, S; Puelles, L; Rubenstein, JL (1995). "Longitudinal organization of the anterior neural plate and neural tube". Development 121 (12): 3923–33. PMID 8575293. [27] Scholpp, Steffen; Lumsden, Andrew (2010). "Building a bridal chamber: Development of the thalamus". Trends in Neurosciences 33 (8): 373–80. doi:10.1016/j.tins.2010.05.003. PMC 2954313. PMID 20541814. [28] Hirata, T.; Nakazawa, M; Muraoka, O; Nakayama, R; Suda, Y; Hibi, M (2006). "Zinc-finger genes Fez and Fez-like function in the establishment of diencephalon subdivisions". Development 133 (20): 3993–4004. doi:10.1242/dev.02585. PMID 16971467. [29] Jeong, J.-Y.; Einhorn, Z.; Mathur, P.; Chen, L.; Lee, S.; Kawakami, K.; Guo, S. (2007). "Patterning the zebrafish diencephalon by the conserved zinc-finger protein Fezl". Development 134 (1): 127–36. doi:10.1242/dev.02705. PMID 17164418. [30] Acampora, D; Avantaggiato, V; Tuorto, F; Simeone, A (1997). "Genetic control of brain morphogenesis through Otx gene dosage requirement". Development 124 (18): 3639–50. PMID 9342056. [31] Scholpp, S.; Foucher, I.; Staudt, N.; Peukert, D.; Lumsden, A.; Houart, C. (2007). "Otx1l, Otx2 and Irx1b establish and position the ZLI in the diencephalon". Development 134 (17): 3167–76. doi:10.1242/dev.001461. PMID 17670791. [32] Puelles, L; Rubenstein, JL (2003). "Forebrain gene expression domains and the evolving prosomeric model". Trends in neurosciences 26 (9): 469–76. doi:10.1016/S0166-2236(03)00234-0. PMID 12948657. [33] Ishibashi, M; McMahon, AP (2002). "A sonic hedgehog-dependent signaling relay regulates growth of diencephalic and mesencephalic primordia in the early mouse embryo". Development 129 (20): 4807–19. PMID 12361972. [34] Kiecker, C; Lumsden, A (2004). "Hedgehog signaling from the ZLI regulates diencephalic regional identity". Nature Neuroscience 7 (11): 1242–9. doi:10.1038/nn1338. PMID 15494730. [35] Scholpp, S.; Wolf, O; Brand, M; Lumsden, A (2006). "Hedgehog signalling from the zona limitans intrathalamica orchestrates patterning of the zebrafish diencephalon". Development 133 (5): 855–64. doi:10.1242/dev.02248. PMID 16452095. [36] Scholpp, S.; Delogu, A.; Gilthorpe, J.; Peukert, D.; Schindler, S.; Lumsden, A. (2009). "Her6 regulates the neurogenetic gradient and neuronal identity in the thalamus". Proceedings of the National Academy of Sciences 106 (47): 19895–900. doi:10.1073/pnas.0910894106. PMC 2775703. PMID 19903880. [37] Vue, Tou Yia; Bluske, Krista; Alishahi, Amin; Yang, Lin Lin; Koyano-Nakagawa, Naoko; Novitch, Bennett; Nakagawa, Yasushi (2009). "Sonic Hedgehog Signaling Controls Thalamic Progenitor Identity and Nuclei Specification in Mice". Journal of Neuroscience 29 (14): 4484–97. doi:10.1523/JNEUROSCI.0656-09.2009. PMC 2718849. PMID 19357274. [38] Young, Keith A.; Holcomb, Leigh A.; Bonkale, Willy L.; Hicks, Paul B.; Yazdani, Umar; German, Dwight C. (2007). "5HTTLPR Polymorphism and Enlargement of the Pulvinar: Unlocking the Backdoor to the Limbic System". Biological Psychiatry 61 (6): 813–8. doi:10.1016/j.biopsych.2006.08.047. PMID 17083920. [39] Dejerine, J.; Roussy, G. (1906). "Le syndrome thalamique". Revue Neurologique 14: 521–32. [40] Gray, H. & Carter, H. V. (1858), Anatomy Descriptive and Surgical, London: John W. Parker and Son, Retrieved (16 October 2011) [2012-02-10] → (http:/ / www. archive. org/ stream/ anatomydescript09graygoog#page/ n7/ mode/ 2up)

40

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External links • BrainMaps at UCDavis thalamus (http://brainmaps.org/index.php?q=thalamus)

Delta wave A delta wave is a high amplitude brain wave with a frequency of oscillation between 0–4 hertz. Delta waves, like other brain waves, are recorded with an electroencephalogram[1] (EEG) and are Delta waves, an EEG (electroencephalograph) one second sample usually associated with the deepest stages of sleep (3 and 4 NREM), also known as slow-wave sleep (SWS), and aid in characterizing the depth of sleep.

Background and history "Delta waves" were first described in the early 1900s by W. Grey Walter, who improved upon Dr. Hans Berger's electroencephalograph machine (EEG) to detect alpha and delta waves.

Classification and features Delta waves, like all brain waves, are detected by electroencephalography (EEG). Delta waves were originally defined as having a frequency between This is a screen shot of a patient during Slow Wave Sleep (stage 3). The high amplitude 1-4 hertz, although more recent EEG is highlighted in red. This screen shot represents a 30 second epoch (30 seconds of classifications put the boundaries at data). between 0.5 and 2 hertz. They are the slowest, but highest amplitude brainwaves. Delta waves begin to appear in stage 3 sleep, but by stage 4 nearly all spectral activity is dominated by delta waves. Stage 3 sleep is defined as having less than 50% delta wave activity, while stage 4 sleep has more than 50% delta wave activity. These stages have recently been combined and are now collectively referred to as stage N3 slow-wave sleep.[2] During N3 SWS, delta waves account for 20% or more of the EEG record during this stage.[3] Delta waves occur in all mammals, and potentially all animals as well. Delta waves are often associated with another EEG phenomenon, the K-complex. K-Complexes have been shown to immediately precede delta waves in slow wave sleep.[4]

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Delta wave

Neurophysiology Sex differences Females have been shown to have more delta wave activity, and this is true across most mammal species. This discrepancy does not become apparent until early adulthood (in the 30's or 40's, in humans), with men showing greater age-related reductions in delta wave activity than their female counterparts.[5] It has been suggested that this discrepancy may be due to larger skull size in males, but this theory has been refuted by intracranial data from female cats, which still show more delta activity.

Brain localization and biochemistry Delta waves can arise either in the thalamus or in the cortex. When associated with the thalamus, they likely arise in coordination with the reticular formation.[6][7] In the cortex, the suprachiasmatic nuclei have been shown to regulate delta waves, as lesions to this area have been shown to cause disruptions in delta wave activity. In addition, delta waves show a lateralization, with right hemisphere dominance during sleep.[8] Delta waves have been shown to be mediated in part by T-type calcium channels.[9] During delta wave sleep, neurons are globally inhibited by gamma-aminobutyric acid (GABA).[10] Delta activity stimulates the release of several hormones, including growth hormone releasing hormone GHRH and prolactin (PRL). GHRH is released from the hypothalamus, which in turn stimulates release of growth hormone from the pituitary. Like growth hormone, the secretion of prolactin - which is closely related to growth hormone (GH) - is also regulated by the pituitary. Thyroid stimulating hormone (TSH) activity is decreased in response to delta-wave signaling.[11]

Development Infants have been shown to spend a great deal of time in slow-wave sleep, and thus have more delta wave activity. In fact, delta-waves are the predominant wave forms of infants. Analysis of the waking EEG of a newborn infant indicates that delta wave activity is predominant in that age, and still appears in a waking EEG of five-year-olds.[12] Delta wave activity during slow-wave sleep declines during adolescence, with a drop of around 25% reported between the ages of 11 and 14 years.[13] Delta waves have been shown to decrease across the lifespan, with most of the decline seen in the mid-forties. By the age of about 75, stage four sleep and delta waves may be entirely absent.[14] In addition to a decrease in the incidence of delta waves during slow-wave sleep in the elderly, the incidence of temporal delta wave activity is common seen in older adults, and incidences also increase with age.[15]

Disruptions and disorders Regional delta wave activity not associated with NREM sleep was first described by W. Grey Walter, who studied cerebral hemisphere tumors. Disruptions in delta wave activity and slow wave sleep are seen in a wide array of disorders. In some cases there may be increases or decreases in delta wave activity, while others may manifest as disruptions in delta wave activity, such as alpha waves presenting in the EEG spectrum. Delta wave disruptions may present as a result of physiological damage, changes in nutrient metabolism, chemical alteration, or may also be idiopathic. Disruptions in delta activity is seen in adults during states of intoxication or delirium and in those diagnosed with various neurological disorders such as dementia or schizophrenia.[16]

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Delta wave

Temporal Low-voltage Irregular Delta Wave (TLID) Temporal low-voltage irregular delta wave activity has been commonly detected in patients with ischemic brain diseases. In addition, small ischemic lesions have been shown to be closely correlated with TLID, and are indicative of early-stage cerebrovasular damage.[17]

Parasomnias Parasomnias are often associated with disruptions in slow wave sleep. Sleep walking and sleep talking most often occur during periods of high-delta wave activity. Sleep walkers have also been shown to have more Hypersynchronous Delta Activity (HSD activity) compared to total time spent in stages 2, 3, and 4 sleep relative to healthy controls. Hypersynchronous Delta Activity (HSD) are continuous, high-voltage (> 150 uV) delta waves seen in sleep EEGs.[18] Parasomnias which occur deep in NREM sleep also include sleep terrors and confusional arousals.

Sleep deprivation Total sleep deprivation has been shown to increase delta wave activity during sleep recovery,[19] and has also been shown to increase hypersynchronous delta activity (HSD).[18]

Parkinson's disease Sleep disturbances, as well as dementia, are common features of Parkinson's disease, and patients with PD show disrupted brain wave activity. The drug rotigotine, developed for PD, has been shown to increase delta power and slow-wave sleep in those with Parkinson's disease. Interestingly, delta-wave inducing peptide injected into the substantia nigra of the rat model has been shown to increase parkinsonian symptoms.[20]

Schizophrenia People suffering schizophrenia have shown disrupted EEG patterns, and there is a close association of reduced delta waves during deep sleep and negative symptoms associated with schizophrenia. During slow wave sleep (stages 3 and 4), schizophrenics have been shown to have reduced delta wave activity, although delta waves have also been shown to be increased during waking hours in more severe forms of schizophrenia.[21] A recent study has shown that the right frontal and central delta wave dominance, seen in healthy individuals, is absent in patients with schizophrenia. In addition, the negative correlation between delta wave activity and age is also not observed in those with schizophrenia.[22]

Diabetes and insulin resistance Disruptions in slow wave (delta) sleep have been shown to increase risk for development of Type II diabetes, potentially due to disruptions in the growth hormone secreted by the pituitary. In addition, hypoglycemia occurring during sleep may also disrupt delta-wave activity.[23] Low-voltage irregular delta waves (TLID) have also been found in the left temporal lobe of diabetic patients, at a rate of 56% (compared to 14% in healthy controls).[24][25]

Fibromyalgia Patients suffering from fibromyalgia often report unrefreshing sleep. A study conducted in 1975 by Moldovsky et al. showed that the delta wave activity of these patients in stages 3 and 4 sleep were often interrupted by alpha waves. They later showed that depriving the body of delta wave sleep activity also induced musculoskeletal pain and fatigue.[26]

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Alcoholism Alcohol has been shown to decrease slow wave sleep and delta power, while increasing stage 1 and REM incidence in both men and women. In long-term alcohol abuse, the influences of alcohol on sleep architecture and reductions in delta activity have been shown to persist even after long periods of abstinence.[27]

Temporal lobe epilepsy Slow waves, including delta waves, are associated with seizure-like activity within the brain. W. Grey Walter was the first person to use delta waves from an EEG to locate brain tumors and lesions causing temporal lobe epilepsy.[28] Neurofeedback has been suggested as a treatment for temporal lobe epilepsy, and theoretically acts to reduce inappropriate delta wave intrusion, although there has been limited clinical research in this area.[29]

Other disorders Other disorders frequently associated with disrupted delta-wave activity include: • depression • anxiety • obsessive-compulsive disorder • attention deficit disorder (ADD)/ attention deficit hyperactivity disorder (ADHD)[30] • juvenile chronic arthritis[31]

Consciousness and dreaming Initially, dreaming was thought to only occur in rapid eye movement sleep, though it is now known that dreaming may also occur during slow-wave sleep. Delta waves and delta wave activity are marked by an unconscious state, and the loss of physical awareness as well as the "iteration of information". Delta wave activity has also been purported to aid in the formation of declarative and explicit memory formation. [10]

Pharmacology While most drugs that affect sleep do so by stimulating sleep onset, or disrupting REM sleep, a number of chemicals and drugs have been shown to alter delta wave activity. • Delta sleep-inducing peptide, as the name suggests, induces delta wave EEG activity. • Alcohol reduces SWS delta wave activity, thereby restricting the release of growth hormone (GH) by the pituitary.[32] • The muramyl peptide, muramyl dipeptide (MDP, N-acetylmuramyl-L-alanyl-D-isoglutamine) has been shown to increase delta wave activity during slow wave sleep.[33] • The drug Gabapentin, a drug used to control epileptic seizures, increases delta-wave activity and slow wave sleep in adults.[34] • While hypnotic drugs increase slow wave sleep, they do not increase delta wave activity, and instead increase spindle activity during slow wave sleep.[35] • Gamma-hydroxy butyrate (GHB) increases delta slow-wave sleep as well as sleep-related growth hormone (GH).[35]

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Effects of diet Diets very low in carbohydrates, such as a ketogenic diet, have been shown to increase the amount of delta activity and slow wave sleep in healthy individuals.[36]

References [1] Walker, Peter (1999). Chambers dictionary of science and technology. Edinburgh: Chambers. p. 312. ISBN 0-550-14110-3. [2] "Glossary. A resource from the Division of Sleep Medicine at Harvard Medical School, Produced in partnership with WGBH Educational Foundation". Harvard University. 2008. Retrieved 2009-03-11. "The 1968 categorization of the combined Sleep Stages 3 – 4 was reclassified in 2007 as Stage N3." [3] Iber C, Ancoli-Israel S, Chesson A, and Quan SF for the American Academy of Sleep Medicine. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications, 1st ed.: Westchester, Illinois: American Academy of Sleep Medicine, 2007. [4] De Gennaro, L., Ferrara, M., & Bertini, M. (2000). The spontaneous K-complex during stage 2 sleep: is it the 'forerunner' of delta waves? [Article]. Neuroscience Letters, 291(1), 41-43. [5] Ehlers, C. L., and D. J. Kupfer. "Slow-wave Sleep: Do Young Adult Men and Women Age Differently?" J Sleep Res. 6.3 (1997): 211-15. Print. [6] Gross, Richard E. (1992). Psychology: the science of mind and behaviour. London: Hodder & Stoughton. pp. 112–113. ISBN 0-340-56136-X. [7] Maquet, P., Degueldre, C., Delfiore, G., Aerts, J., Peters, J. M., Luxen, A., et al. (1997). Functional neuroanatomy of human slow wave sleep. Journal of Neuroscience, 17(8), 2807-2812. [8] Mistlberger, R. E., Bergmann, B. M., & Rechtschaffen, A. (1987). RELATIONSHIPS AMONG WAKE EPISODE LENGTHS, CONTIGUOUS SLEEP EPISODE LENGTHS, AND ELECTROENCEPHALOGRAPHIC DELTA WAVES IN RATS WITH SUPRACHIASMATIC NUCLEI LESIONS. [Article]. Sleep, 10(1), 12-24. [9] Lee, J., Kim, D., Shin, H. Lack of delta waves and sleep disturbances during non-rapid eye movement sleep in mice lacking a1g-subunit of T-type calcium channels. PNAS;101(52): 18195-18199. [10] Hobson, J. , & Pace-Schott, E. (2002). The Cognitive Neuroscience of Sleep: Neuronal Systems, Consciousness and Learning. Nature Reviews Neuroscience, 3(9), 679-693. [11] Brandenberger, G. (2003). The Ulradien Rhythm of Sleep: Diverse Relations with Pituitary and Adrenal Hormones. Revue Neurologique, 159(11), S5-S10. [12] Taylor, Eric; Rutter, Michael (2002). Child and adolescent psychiatry. Oxford: Blackwell Science. p. 162. ISBN 0-632-05361-5. [13] "Brain Wave Changes In Adolescence Signal Reorganization Of The Brain" (http:/ / www. sciencedaily. com/ releases/ 2006/ 12/ 061207160458. htm). ScienceDaily. 2006-12-08. . Retrieved 2008-03-24. [14] Colrain, I. M., Crowley, K. E., Nicholas, C. L., Afifi, L., Baker, F. C., Padilla, M., et al. (2010). Sleep evoked delta frequency responses show a linear decline in amplitude across the adult lifespan. [Article]. Neurobiology of Aging, 31(5), 874-883. [15] Inui, Koji, Eishi Motomura, Hiroyuki Kaige, and Sen Nomura. "Temporal Slow Waves and Cerebrovascular Diseases - Inui - 2008 Psychiatry and Clinical Neurosciences." Psychiatry and Clinical Neurosciences 55.5 (2001): 525-31. Wiley Online Library. Web. 29 Nov. 2010. [16] Hales, Robert E.; Yudofsky, Stuart C. (2007). The American Psychiatric Publishing Textbook of Neuropsychiatry and Behavioral Neurosciences, Fifth Edition (American Psychiatric Press Textbook of Neuropsychiatry). American Psychiatric Publishing, Inc. ISBN 1-58562-239-7. [17] Inui, Koji, Hozumi Kawamoto, Masahiko Kawakita, Kazuhisa Wako, Hiromichi Nakashima, Masanori Kamihara, and Junichi Nomura. "Temporal Delta Wave and Ischemic Lesions on MRI." Psychiatry and Clinical Neurosciences 48.4 (1994): 891-98. Print. [18] Pilon M; Zadra A; Joncas S et al. Hypersynchronous delta waves and somnambulism: brain topography and effect of sleep deprivation. SLEEP 2006;29(1): 77-84. [19] Feinberg, I., T. Baker, R. Leder, and J. D. March. "Response of Delta (0-3 Hz) EEG and Eye Movement Density to a Night with 100 Minutes of Sleep." Sleep 11.5 (1988): 473-87. Print. [20] Kryzhanovskii, G. N., A. A. Shandra, L. S. Godlevskii, and I. I. Mikhaleva. "Appearance of Parkinsonian Syndrome after Administration of Delta Sleep-inducing Peptide into the Rat Substantia Nigra." Biull Eksp Biol Med. 109.2 (1990): 119-21. Print. [21] Alfimova, M. V., & Uvarova, L. G. (2007). Changes in the EEG spectral power during perception of neutral and emotionally salient words in schizophrenic patients, their relatives and healthy individuals from general population. [Article]. Zhurnal Vysshei Nervnoi Deyatelnosti Imeni I P Pavlova, 57(4), 426-436. [22] Sekimoto, M., et al., Cortical regional differences of delta waves during all-night sleep in schizophrenia, Schizophr. Res. (2010), doi:10.1016/j.schres.2010.11.003 [23] Abdelkarim, T. H., Westin, T., Romaker, A., & Girish, M. (2002). Presence of delta waves in REM sleep during polysomnography as a sign of acute hypoglycemic encephalopathy. [Meeting Abstract]. Sleep, 25, 531. [24] Appearance of Parkinsonian Syndrome after Administration of Delta Sleep-inducing Peptide into the Rat Substantia Nigra." Biull Eksp Biol Med. 109.2 (1990): 119-21. Print.

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Delta wave [25] Inui, K., H. Sannan, H. Ota, Y. Uji, S. Nomura, H. Kaige, I. Kitayama, and J. Nomura. "EEG Findings in Diabetic Patients with and without Retinopathy." Acta Neurologica Scandinavica 97.2 (1998): 107-09. Print. [26] Nezu, Arthur M. ., Christine Maguth. Nezu, Pamela A. . Geller, and Irving B. . Weiner. Handbook of Psychology. New York: Wiley, 2003. Print. [27] Colrain, I. M., S. Turlington, and F. C. Baker. "Impact of Alcoholism on Sleep Architecture and EEG Power Spectra in Men and Women." Sleep. 32.10 (2009): 1341-352. Print. [28] Walter WG. The location of cerebral tumors by electroencephalography. Lancet 1936;2: 305–8. [29] "Biofeedback for Epileptic Seizures; EEG Neurofeedback for Epilepsy" (http:/ / www. epilepsyhealth. com/ biofeedback. html). Epilepsyhealth.com. . Retrieved 2011-02-14. [30] EEG-defined subtypes of children with attention-deficit/hyperactivity disorder. Adam R Clarke, Robert J Barry, Rory McCarthy, Mark Selikowitz. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 1 November 2001 (volume 112 issue 11 Pages 2098-2105) [31] Lopes, M.C., Guilleminault, C., Rosa, A., Passarelli, C., Roizenblatt, S., Tufik, S. Delta sleep instability in children with chronic arthritis. Brazilian Journal of Medical and Biological Research. 2008;41(10): 938-43. [32] Lands, William. "Alcohol, Slow Wave Sleep, and the Somatotropic Axis." Alcohol 18.2 (1999): 109-22. [33] Davenne, D. M. "Enhancement of Quiet Sleep in Rabbit Neonates by Muramyl Dipeptide." Am J Physiol. 253.4 (1987): 646-54. Print. [34] Foldvary-Schaefer, N., I. De Leon Sanchez, M. Karafa, D. Dinner, and H. H. Morris. "Gabapentin Increases Slow-wave Sleep in Normal Adults." Epilepsia 43.12 (2002): 1493-497. Print. [35] D'haenen, H. A. H., Johan A. Den Boer, and Paul Willner. Biological Psychiatry. Chichester: Wiley, 2002. Print. [36] Afaghi, A. , O'Connor, H. , & Chow, C. (2008). Acute Effects of the Very Low Carbohydrate Diet on Sleep Indices. Nutritional Neuroscience, 11(4), 146-154.

Theta rhythm The theta rhythm is an oscillatory pattern in EEG signals recorded either from inside the brain or from electrodes glued to the scalp. Two types of theta rhythm have been Example of an EEG theta wave described. The "hippocampal theta rhythm" is a strong oscillation that can be observed in the hippocampus and other brain structures in numerous species of mammals including rodents, rabbits, dogs, cats, bats, and marsupials. "Cortical theta rhythms" are low-frequency components of scalp EEG, usually recorded from humans. In rats, the most frequently studied species, theta rhythmicity is easily observed in the hippocampus, but can also be detected in numerous other cortical and subcortical brain structures. Hippocampal theta, with a frequency range of 6–10 Hz, appears when a rat is engaged in active motor behavior such as walking or exploratory sniffing, and also during REM sleep. Theta waves with a lower frequency range, usually around 6–7 Hz, are sometimes observed when a rat is motionless but alert. When a rat is eating, grooming, or sleeping, the hippocampal EEG usually shows a non-rhythmic pattern known as Large Irregular Activity or LIA. The hippocampal theta rhythm depends critically on projections from the medial septal area, which in turn receives input from the hypothalamus and several brainstem areas. Hippocampal theta rhythms in other species differ in some respects from those in rats. In cats and rabbits, the frequency range is lower (around 4–6 Hz), and theta is less strongly associated with movement than in rats. In bats, theta appears in short bursts associated with echolocation. In humans and other primates, hippocampal theta is difficult to observe at all. The function of the hippocampal theta rhythm is not clearly understood. Green and Arduini, in the first major study of this phenomenon, noted that hippocampal theta usually occurs together with desynchronized EEG in the neocortex, and proposed that it is related to arousal. Vanderwolf and his colleagues, noting the strong relationship between theta and motor behavior, have argued that it is related to sensorimotor processing. Another school, led by John O'Keefe, have suggested that theta is part of the mechanism animals use to keep track of their location within

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Theta rhythm the environment. The most popular theories, however, link the theta rhythm to mechanisms of learning and memory.(Hasselmo, 2005) Cortical theta rhythms observed in human scalp EEG are a different phenomenon, with no clear relationship to the hippocampus. In human EEG studies, the term theta refers to frequency components in the 4–7 Hz range, regardless of their source. Cortical theta is observed frequently in young children. In older children and adults, it tends to appear during drowsy, meditative, or sleeping states, but not during the deepest stages of sleep. Several types of brain pathology can give rise to abnormally strong or persistent cortical theta waves.

Terminology Because of a historical accident, the term "theta rhythm" is used to refer to two different phenomena, "hippocampal theta" and "human cortical theta". Both of these are oscillatory EEG patterns, but they may have little in common beyond the name "theta" . In the oldest EEG literature dating back to the 1920s, Greek letters such as alpha, beta, theta, and gamma were used to classify EEG waves falling into specific frequency ranges, with "theta" generally meaning a range of about 4–7 cycles per second (Hz). In the 1930s–1950s, a very strong rhythmic oscillation pattern was discovered in the hippocampus of cats and rabbits (Green & Arduini, 1954). In these species, the hippocampal oscillations fell mostly into the 4–6 Hz frequency range, so they were referred to as "theta" oscillations. Later, hippocampal oscillations of the same type were observed in rats; however, the frequency of rat hippocampal EEG oscillations averaged about 8 Hz and rarely fell below 6 Hz. Thus the rat hippocampal EEG oscillation should not, strictly speaking, have been called a "theta rhythm". However the term "theta" had already become so strongly associated with hippocampal oscillations that it continued to be used even for rats. Over the years this association has come to be stronger than the original association with a specific frequency range, but the original meaning also persists. Thus, "theta" can mean either of two things: 1. A specific type of regular oscillation seen in the hippocampus and several other brain regions connected to it. 2. EEG oscillations in the 4–7 Hz frequency range, regardless of where in the brain they occur or what their functional significance is. The first meaning is usually intended in literature that deals with rats or mice, while the second meaning is usually intended in studies of human EEG recorded using electrodes glued to the scalp. In general, it is not safe to assume that observations of "theta" in the human EEG have any relationship to the "hippocampal theta rhythm". Scalp EEG is generated almost entirely by the cerebral cortex, and even if it falls into a certain frequency range, this cannot be taken to indicate that it has any functional dependence on the hippocampus.

Hippocampal Due to the density of its neural layers, the hippocampus generates some of the largest EEG signals of any brain structure. In some situations the EEG is dominated by regular waves at 4–10 Hz, often continuing for many seconds. This EEG pattern is known as the hippocampal theta rhythm. It has also been called Rhythmic Slow Activity (RSA), to contrast it with the Large Irregular Activity (LIA) that usually dominates the hippocampal EEG when theta is not present. In rats, hippocampal theta is seen mainly in two conditions: first, when an animal is running, walking, or in some other way actively interacting with its surroundings; second, during REM sleep (Vanderwolf, 1969). The frequency of the theta waves increases as a function of running speed, starting at about 6.5 Hz on the low end, and increasing to about 9 Hz at the fastest running speeds, although higher frequencies are sometimes seen for brief high-velocity movements such as jumps across wide gaps. In larger species of animals, theta frequencies are generally lower. The behavioral dependency also seems to vary by species: in cats and rabbits, theta is often observed during states of motionless alertness. This has been reported for rats as well, but only when they are fearful (Sainsbury et al., 1987).

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Theta rhythm Theta is not just confined to the hippocampus. In rats, it can be observed in many parts of the brain, including nearly all that interact strongly with the hippocampus. The generation of the rhythm is dependent on the medial septal area: this area projects to all of the regions that show theta rhythmicity, and destruction of it eliminates theta throughout the brain (Stewart & Fox, 1990).

Type 1 and type 2 In 1975 Kramis, Bland, and Vanderwolf proposed that in rats there are two distinct types of hippocampal theta rhythm, with different behavioral and pharmacological properties (Kramis et al., 1975). Type 1 ("atropine resistant") theta, according to them, appears during locomotion and other types of "voluntary" behavior and during REM sleep, has a frequency usually around 8 Hz, and is unaffected by the anticholinergic drug atropine. Type 2 ("atropine sensitive") theta appears during immobility and during anesthesia induced by urethane, has a frequency in the 6–7 Hz range, and is eliminated by administration of atropine. Many later investigations have supported the general concept that hippocampal theta can be divided into two types, although there has been dispute about the precise properties of each type. Type 2 theta is comparatively rare in unanesthetized rats: it may be seen briefly when an animal is preparing to make a movement but hasn't yet executed it, but has only been reported for extended periods in animals that are in a state of frozen immobility because of the nearby presence of a predator such as a cat or ferret (Sainsbury et al., 1987).

Relationship with behavior Vanderwolf (1969) made a strong argument that the presence of theta in the hippocampal EEG can be predicted on the basis of what an animal is doing, rather than why the animal is doing it. Active movements such as running, jumping, bar-pressing, or exploratory sniffing are reliably associated with theta; inactive states such as eating or grooming are associated with LIA. Later studies showed that theta frequently begins several hundred milliseconds before the onset of movement, and that it is associated with the intention to move rather than with feedback produced by movement (Whishaw & Vanderwolf, 1973). The faster an animal runs, the higher the theta frequency. In rats, the slowest movements give rise to frequencies around 6.5 Hz, the fastest to frequencies around 9 Hz, although faster oscillations can be observed briefly during very vigorous movements such as large jumps. There is also a distinction between sleep states: REM (dreaming) sleep is associated with theta; slow-wave sleep is associated with LIA.

Mechanisms Numerous studies have shown that the medial septal area plays a central role in generating hippocampal theta (Stewart & Fox, 1990). Lesioning the medial septal area, or inactivating it with drugs, eliminates both type 1 and type 2 theta. Under certain conditions, theta-like oscillations can be induced in hippocampal or entorhinal cells in the absence of septal input, but this does not occur in intact, undrugged adult rats. The critical septal region includes the medial septal nucleus and the vertical limb of the diagonal band of Broca. The lateral septal nucleus, a major recipient of hippocampal output, probably does not play an essential role in generating theta. The medial septal area projects to a large number of brain regions that show theta modulation, including all parts of the hippocampus as well as the entorhinal cortex, perirhinal cortex, retrosplenial cortex, medial mamillary and supramamillary nuclei of the hypothalamus, anterior nuclei of the thalamus, amygdala, inferior colliculus, and several brainstem nuclei (Buzsáki, 2002). Some of the projections from the medial septal area are cholinergic; the rest are GABAergic. It is commonly argued that cholinergic receptors do not respond rapidly enough to be involved in generating theta waves, and therefore that GABAergic signals must play the central role. A major research problem has been to discover the "pacemaker" for the theta rhythm, that is, the mechanism that determines the oscillation frequency. The answer is not yet entirely clear, but there is some evidence that type 1 and type 2 theta depend on different pacemakers. For type 2 theta, the supramamillary nucleus of the hypothalamus

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Theta rhythm appears to exert control (Kirk, 1998). For type 1 theta, the picture is still unclear, but the most widely accepted hypothesis proposes that the frequency is determined by a feedback loop involving the medial septal area and hippocampus (Wang, 2002). Several types of hippocampal and entorhinal neurons are capable of generating theta-frequency membrane potential oscillations when stimulated. Typically these are sodium-dependent voltage-sensitive oscillations in membrane potential at near-action potential voltages (Alonso & Llinás, 1989). Specifically, it appears that in neurons of the CA1 and dentate gyrus, these oscillations result from an interplay of dendritic excitation via a persistent sodium current (INaP) with perisomatic inhibition (Buzsáki, 2002). Generators As a rule, EEG signals are generated by synchronized synaptic input to the dendrites of neurons arranged in a layer. The hippocampus contains multiple layers of very densely packed neurons—the dentate gyrus and the CA3/CA1/subicular layer—and therefore has the potential to generate strong EEG signals. Basic EEG theory says that when a layer of neurons generates an EEG signal, the signal always phase-reverses at some level. Thus, theta waves recorded from site above and below a generating layer have opposite signs. There are other complications as well: the hippocampal layers are strongly curved, and theta-modulated inputs impinge on them from multiple pathways, with varying phase relationships. The outcome of all these factors is that the phase and amplitude of theta change in a very complex way as a function of position within the hippocampus. The largest theta waves, however, are generally recorded from the vicinity of the fissure that separates the CA1 molecular layer from the dentate gyrus molecular layer. In rats, these signals frequently exceed 1 millivolt in amplitude. Theta waves recorded from above the hippocampus are smaller, and polarity-reversed with respect to the fissure signals. The strongest theta waves are generated by the CA1 layer, and the most significant input driving them comes from the entorhinal cortex, via the direct EC→CA1 pathway. Another important driving force comes from the CA3→CA1 projection, which is out of phase with the entorhinal input, leading to a gradual phase shift as a function of depth within CA1 (Brankack, et al. 1993). The dentate gyrus also generates theta waves, which are difficult to separate from the CA1 waves because they are considerably smaller in amplitude, but there is some evidence that dentate gyrus theta is usually about 90 degrees out of phase from CA1 theta. Direct projections from the septal area to hippocampal interneurons also play a role in generating theta waves, but their influence is much smaller than that of the entorhinal inputs (which are, however, themselves controlled by the septum).

Humans and other primates In animals, EEG signals are usually recorded using electrodes implanted in the brain; the majority of theta studies have involved electrodes implanted in the hippocampus. In humans, because invasive studies are not ethically permissible except in some neurological patients, by far the largest number of EEG studies have been conducted using electrodes glued to the scalp. The signals picked up by scalp electrodes are comparatively small and diffuse, and arise almost entirely from the cerebral cortex—the hippocampus is too small and too deeply buried to generate recognizable scalp EEG signals. Human EEG recordings show clear theta rhythmicity in some situations, but because of the technical difficulties, it has been difficult to tell whether these signals have any relationship with the hippocampal theta signals recorded from other species. In contrast to the situation in rats, where long periods of theta oscillations are easily observed using electrodes implanted at many sites, theta has been difficult to pin down in primates, even when intracortical electrodes have been available. Green and Arduini (1954), in their pioneering study of theta rhythms, reported only brief bursts of irregular theta in monkeys. Other investigators have reported similar results, although Stewart and Fox (1991) described a clear 7–9 Hz theta rhythm in the hippocampus of urethane-anesthetized macaques and squirrel monkeys, resembling the type 2 theta observed in urethane-anesthetized rats.

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Theta rhythm Most of the available information on human hippocampal theta comes from a few small studies of epileptic patients with intracranially implanted electrodes used as part of a treatment plan. In the largest and most systematic of these studies, Cantero et al. (2003) found that oscillations in the 4–7 Hz frequency range could be recorded from both the hippocampus and neocortex. The hippocampal oscillations were associated with REM sleep and the transition from sleep to waking, and came in brief bursts, usually less than a second long. Cortical theta oscillations were observed during the transition from sleep and during quiet wakefulness; however, the authors were unable to find any correlation between hippocampal and cortical theta waves, and concluded that the two processes are probably controlled by independent mechanisms.

Research findings in theta-wave activity Theta-frequency EEG activity is also manifested during some short term memory tasks (Vertes, 2005). Studies suggest that they reflect the "on-line" state of the hippocampus; one of readiness to process incoming signals (Buzsáki, 2002). Conversely, theta oscillations have been correlated to various voluntary behaviors (exploration, spatial navigation, etc.) and alert states (piloerection, etc.) in rats (Vanderwolf, 1969), suggesting that it may reflect the integration of sensory information with motor output (for review, see Bland & Oddie, 2001). A large body of evidence indicates that theta rhythm is likely involved in spatial learning and navigation (Buzsáki, 2005). Theta rhythms are very strong in rodent hippocampi and entorhinal cortex during learning and memory retrieval, and are believed to be vital to the induction of long-term potentiation, a potential cellular mechanism of learning and memory. Based on evidence from electrophysiological studies showing that both synaptic plasticity and strength of inputs to hippocampal region CA1 vary systematically with ongoing theta oscillations (Hyman et al., 2003; Brankack et al., 1993), it has been suggested that the theta rhythm functions to separate periods of encoding of current sensory stimuli and retrieval of episodic memory cued by current stimuli so as to avoid interference that would occur if encoding and retrieval were simultaneous.

History Although there were a few earlier hints, the first clear description of regular slow oscillations in the hippocampal EEG came from a paper written in German by Jung and Kornmüller (1938) They were not able to follow up on these initial observations, and it was not until 1954 that further information became available, in a very thorough study by John D. Green and Arnaldo Arduini that mapped out the basic properties of hippocampal oscillations in cats, rabbits, and monkeys (Green & Arduini, 1954). Their findings provoked widespread interest, in part because they related hippocampal activity to arousal, which was at that time the hottest topic in neuroscience. Green and Arduini described an inverse relationship between hippocampal and cortical activity patterns, with hippocampal rhythmicity occurring alongside desynchronized activity in the cortex, whereas an irregular hippocampal activity pattern was correlated with the appearance of large slow waves in the cortical EEG. Over the following decade came an outpouring of experiments examining the pharmacology and physiology of theta. By 1965, Charles Stumpf was able to write a lengthy review of "Drug action on the electrical activity of the hippocampus" citing hundreds of publications (Stumpf, 1965), and in 1964 John Green, who served as the leader of the field during this period, was able to write an extensive and detailed review of hippocampal electrophysiology (Green, 1964). A major contribution came from a group of investigators working in Vienna, including Stumpf and Wolfgang Petsche, who established the critical role of the medial septum in controlling hippocampal electrical activity, and worked out some of the pathways by which it exerts its influence.

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References • Alonso, A; Llinás R (1989). "Subthreshold Na+-dependent theta-like rhythmicity in entorhinal cortex layer II stellate cells". Nature 342 (6246): 175–177. doi:10.1038/342175a0. PMID 2812013. • Bland, BH; Oddie SD (2001). "Theta band oscillation and synchrony in the hippocampal formation and associated structures: the case for its role in sensorimotor integration". Behav Brain Res 127 (1–2): 119–36. doi:10.1016/S0166-4328(01)00358-8. PMID 11718888. • Brankack, J; Stewart M, Fox SE (1993). "Current source density analysis of the hippocampal theta rhythm: Associated sustained potentials and candidate synaptic generators". Brain Res 615 (2): 310–327. doi:10.1016/0006-8993(93)90043-M. PMID 8364740. • Buzsáki, G (2002). "Theta oscillations in the hippocampus". Neuron 33 (3): 325–40. doi:10.1016/S0896-6273(02)00586-X. PMID 11832222. • Buzsáki, G (2005). "Theta rhythm of navigation: link between path integration and landmark navigation, episodic and semantic memory". Hippocampus 15 (7): 827–40. doi:10.1002/hipo.20113. PMID 16149082. • Cantero JL, Atienza M, Stickgold R, Kahana MJ, Madsen JR, Kocsis B (2003). "Sleep-dependent theta oscillations in the human hippocampus and neocortex" [1]. J Neurosci 23 (34): 10897–903. PMID 14645485. • Green, JD; Arduini A (1954). "Hippocampal activity in arousal". J Neurophysiol 17 (6): 533–57. PMID 13212425. • Green, JD; Arduini, AA (1964). "The hippocampus". Physiol Rev 44 (6): 561–608. PMID 13212425. • Hasselmo, ME (2005). "What is the Function of Hippocampal Theta Rhythm?–– Linking Behavioral Data to Phasic Properties of Field Potential and Unit Recording Data". Hippocampus 15 (7): 936–49. doi:10.1002/hipo.20116. PMID 16158423. • Hasselmo, ME; Eichenbaum H (2005). "Hippocampal mechanisms for the context-dependent retrieval of episodes". Neural Networks 18 (9): 1172–90. doi:10.1016/j.neunet.2005.08.007. PMC 2253492. PMID 16263240. • Hyman, JM; Wyble BP, Goyal V, Rossi CA, Hasselmo ME (December 17, 2003). "Stimulation in hippocampal region CA1 in behaving rats yields LTP when delivered to the peak of theta and LTD when delivered to the trough" [2]. J Neurosci 23 (37): 11725–31. PMID 14684874. • Kirk IJ (1998). "Frequency modulation of hippocampal theta by the supramammillary nucleus, and other hypothalamo-hippocampal interactions: mechanisms and functional implications". Neurosci Biobehav Rev 22 (2): 291–302. doi:10.1016/S0149-7634(97)00015-8. PMID 9579319. • Kramis R, Vanderwolf CH, Bland BH (1975). "Two types of hippocampal rhythmical slow activity in both the rabbit and the rat: relations to behavior and effects of atropine, diethyl ether, urethane, and pentobarbital". Exp Neurol 49 (1 Pt 1): 58–85. doi:10.1016/0014-4886(75)90195-8. PMID 1183532. • Jung, R; Kornmüller AE (1938). "Eine Methodik der ableitung lokalisierter Potentialschwankungen aus subcorticalen Hirngebieten". Arch Psychiat Nervenkr 109: 1–30. doi:10.1007/BF02157817. • Sainsbury, RS; Heynen A, Montoya CP (1987). "Behavioral correlates of hippocampal type 2 theta in the rat". Physiol Behav 39 (4): 513–519. doi:10.1016/0031-9384(87)90382-9. PMID 3575499. • Stewart M, Fox SE (1990). "Do septal neurons pace the hippocampal theta rhythm?". Trends Neurosci 13 (5): 163–8. doi:10.1016/0166-2236(90)90040-H. PMID 1693232. • Stewart M, Fox SE (1991). "Hippocampal theta activity in monkeys". Brain Res 538 (1): 59–63. doi:10.1016/0006-8993(91)90376-7. PMID 2018932. • Stumpf, C (1965). "Drug action on the electrical activity of the hippocampus". Int Rev Neurobiol 8: 77–138. doi:10.1016/S0074-7742(08)60756-4. PMID 4954552.

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• Vanderwolf, CH (1969). "Hippocampal electrical activity and voluntary movement in the rat". EEG Clin Neurophysiol 26 (4): 407–418. doi:10.1016/0013-4694(69)90092-3. • Vertes, RP (2005). "Hippocampal theta rhythm: a tag for short-term memory". Hippocampus 15 (7): 923–35. doi:10.1002/hipo.20118. PMID 16149083. • Wang XJ (2002). "Pacemaker neurons for the theta rhythm and their synchronization in the septohippocampal reciprocal loop" [3]. J Neurophysiol 87 (2): 889–900. PMID 11826054. • Whishaw IQ, Vanderwolf CH (1973). "Hippocampal EEG and behavior: changes in amplitude and frequency of RSA (theta rhythm) associated with spontaneous and learned movement patterns in rats and cats". Behav Biol 8 (4): 461–84. doi:10.1016/S0091-6773(73)80041-0. PMID 4350255.

External links • Brain slice models of theta EEG activity [4]

References [1] http:/ / www. jneurosci. org/ cgi/ content/ full/ 23/ 34/ 10897 [2] http:/ / www. jneurosci. org/ cgi/ content/ abstract/ 23/ 37/ 11725 [3] http:/ / jn. physiology. org/ cgi/ content/ full/ 87/ 2/ 889 [4] http:/ / www. stanford. edu/ group/ maciverlab/ theta. html

Alpha wave Alpha waves are neural oscillations in the frequency range of 8–12 Hz arising from synchronous and coherent (in phase/constructive) electrical activity of thalamic pacemaker cells in humans. They are also called Berger's wave in memory of the founder of EEG.

Alpha waves

Alpha waves are one type of brain waves detected either by electroencephalography (EEG) or magnetoencephalography (MEG) and predominantly originate from the occipital lobe during wakeful relaxation with closed eyes. Alpha waves are reduced with open eyes, drowsiness and sleep. Historically, they were thought to represent the activity of the visual cortex in an idle state. More recent papers have argued that they inhibit areas of the cortex not in use, or alternatively that they play an active role in network coordination and communication.[1] Occipital alpha waves during periods of eyes closed are the strongest EEG brain signals. An alpha-like variant called mu (μ) can be found over the motor cortex (central scalp) that is reduced with movement, or the intention to move. Alpha waves do not start to appear until three years of age.[2]

Alpha wave

History of alpha waves Alpha waves were discovered by German neurologist Hans Berger, most famous for his invention of the EEG. Alpha waves were among the first waves documented by Berger, along with beta waves, and he displayed an interest in "alpha blockage", the process by which alpha waves decrease and beta waves increase upon a subject opening their eyes. This distinction earned the alpha wave the alternate title of "Berger’s Wave". Berger took a cue from Eastern European physiologist Pravdich-Neminski, who used a string galvanometer to create a photograph of the electrical activity of a dog's brain. Using similar techniques, Berger confirmed the existence of electrical activity in the human brain. He first did this by presenting a stimulus to hospital patients with skull damage and measuring the electrical activity in their brains. Later he ceased the stimulus method and began measuring the natural rhythmic electrical cycles in the brain. The first natural rhythm he documented was what would become known as the alpha wave. Despite his brilliance, Berger was very thorough and meticulous in his data-gathering, and did not feel confident enough to publish his discoveries until at least five years after he had made them. In 1931, he published his first findings on alpha waves in the journal Archiv für Psychiatrie. He was originally met with derision for his EEG technique and his subsequent alpha and brain wave discoveries. His technique and findings did not gain widespread acceptance in the psychological community until 1937, when he gained the approval of the famous physiologist Lord Adrian, who took a particular interest in alpha waves.[3] Alpha waves again gained recognition in the early 1960s and 1970s with the creation of a biofeedback theory relating to brain waves (see below). Such biofeedback, referred to as a kind of neurofeedback, relating to alpha waves is the conscious elicitation of alpha brainwaves by a subject. Two different researchers in the United States explored this concept through unrelated experiments. Dr. Joe Kamiya, of the University of Chicago, discovered that some individuals had the conscious ability to recognize when they were creating alpha waves, and could increase their alpha activity. These individuals were motivated through a reward system from Kamiya. The second progenitor of biofeedback is Dr. Barry Sterman, from the University of California, Los Angeles. He was working with monitoring brain waves in cats and found that, when the cats were trained to withhold motor movement, they released SMR, or mu, waves, a wave similar to alpha waves. Using a reward system, he further trained these cats to enter this state more easily. Later, he was approached by the United States Air Force to test the effects of a jet fuel that was known to cause seizures in humans. Sterman tested the effects of this fuel on the previously-trained cats, and discovered that they had a higher resistance to seizures than non-trained cats. Alpha wave biofeedback has gained interest for having some successes in humans for seizure suppression and for treatment of depression.[4]

Types of alpha waves Some researchers posit that there are at least three forms of alpha waves, which may all have different functions in the wake-sleep cycle. Alpha waves are present at different stages of the wake-sleep cycle. The most widely-researched is during the relaxed mental state, where the subject is at rest with eyes closed, but is not tired or asleep. This alpha activity is centered in the occipital lobe, and is presumed to originate there, although there has been recent speculation that it instead has a thalamic origin.[5] This wave begins appearing at around four months, and is initially a frequency of 4 waves per second. The mature alpha wave, at 10 waves per second, is firmly established by age 3.[6] The second occurrence of alpha wave activity is during REM sleep. As opposed to the awake form of alpha activity, this form is located in a frontal-central location in the brain. The purpose of alpha activity during REM sleep has yet to be fully understood. Currently, there are arguments that alpha patterns are a normal part of REM sleep, and for the notion that it indicates a semi-arousal period. It has been suggested that this alpha activity is inversely related to REM sleep pressure.

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Alpha wave The third occurrence of alpha wave activity is the alpha-delta or slow-wave (SWS) state. This activity spreads across the brain in an anterior-posterior gradient.[7] It has long been believed that alpha waves indicate a wakeful period during sleep. This has been attributed to studies where subjects report non-refreshing sleep and have EEG records reporting high levels of alpha intrusion into sleep. This occurrence is known as alpha wave intrusion.[8] However, it is possible that these explanations may be misleading, as they only focus on alpha waves being generated from the occipital lobe.

Alpha wave intrusion Alpha wave intrusion occurs when alpha waves appear with non-REM sleep when delta activity is expected. It is hypothesized to be associated with fibromyalgia,[9] although the study may be inadequate due to a small sampling size. Despite this, alpha wave intrusion has not been significantly linked to any major sleep disorder, including fibromyalgia, chronic fatigue syndrome (CNF), and major depression. However, it is common in chronic fatigued patients, and may amplify the effects of other sleep disorders.[10]

Biofeedback training Given the alpha wave's connection with relaxed mental states, many people have latched onto the idea of utilizing this state through a technique called biofeedback training. This technique utilizes EEG to indicate to a subject or trainer when the subject is in an alpha wave state, which the subject is then instructed to remain in. There are several different prospects of this training that are currently being explored. Arguably, the most popular one is the use of this training in meditation. Zen-trained meditation masters produce noticeably more alpha waves during meditation. This fact has led to a popular trend of biofeedback training programs for everyday stress relief. Psychologists are hoping to use this technique to help people overcome phobias, calm down hyperactive children, and help children with stuttering problems to relax enough to practice regular speech. There are other uses of biofeedback training beyond therapy. Defense Department researchers are exploring biofeedback as a way of getting captured soldiers to create alpha waves, potentially foiling enemy lie detectors. Biofeedback training has also been receiving attention as a possible way of monitoring attention. It has been theorized that teaching machines could use biofeedback as a way of monitoring children's attention, with the appearance of alpha waves signaling a lapse of attention.[11] Following this lapse-of-attention line of thought, a recent study indicates that alpha waves may be used to predict mistakes. In it, MEGs measured increases of up to 25% in alpha brain wave activity before mistakes occurred. This study used common sense: alpha waves indicate idleness, and mistakes are often made when a person is doing something automatically, or "on auto-pilot", and not paying attention to the task they are performing. After the mistake was noticed by the subject, there was a decrease in alpha waves as the subject began paying more attention. This study hopes to promote the use of wireless EEG technology on employees in high-risk fields, such as air traffic controlling, to monitor alpha wave activity and gauge the attention level of the employee.[12]

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Alpha wave

Alpha waves in a gelatinous conductor As demonstrated by Adrian Upton, it is possible for extraneous sources to cause signals to appear on an EEG readout, causing false signals to be interpreted as healthy alpha waves while the patient's brain that is assumed to be still living is in fact, long dead. An excerpt from the article documenting this fact: "Sometimes it's claimed Jell-O brainwaves are identical to a healthy adult's. That's clearly a stretch, but the Jell-O EEG readings do look pretty similar to a normal human alpha rhythm. Alpha waves are observed when a patient is awake and resting with eyes closed, and in some kinds of sleep and reversible coma. True, the Jell-O waves are a little slower and of much lower amplitude, barely within normal human limits, but that doesn't tell you much by itself. Hypoxia, encephalitis, and other medical conditions can cause reduced frequency and amplitude, as can drug use."[13]

References [1] Palva, S. and Palva, J.M., New vistas for a-frequency band oscillations, Trends Neurosci. (2007), doi:10.1016/j.tins.2007.02.001 [2] Kolev V, Başar-Eroglu C, Aksu F, Başar E. (1994). EEG rhythmicities evoked by visual stimuli in three-year-old children. Int J Neurosci. 75(3-4):257-70. PMID 8050866 [3] Karbowski K. Hans Berger (1873-194). Journal of Neurology. 249(8):1310-1311 [4] Ulrich Kraft. Train Your Brain-Mental exercises with neurofeedback may ease symptoms of attention-deficit disorder, epilepsy and depression--and even boost cognition in healthy brains. Scientific American. 2006 [5] Domino E. F., Ni L. S., et. al(2009). Tobacco smoking produces widespread dominant brainwave alpha frequency increases. International Journal of Psychophysiology. 74(3):192-198. [6] Niedermeyer E.(1997). Alpha rhythms as physiological and abnormal phenomena. International Journal of Psychophysiology. 26(1-3):31-49. [7] Pivik R. T., Harman K. (1995). A Reconceptualization of EEG alpha activity as an index of arousal during sleep: all alpha activity is not equal. Journal of Sleep Research. 4(3):131-137. [8] Allas Task Force (1992). ASDA report on EEG arousals: scoring rules and examples. Sleep. 15(2):173-184. [9] Germanowicz D, Lumertz MS, Martinez D, Margarites AF (2006). "Sleep disordered breathing concomitant with fibromyalgia syndrome". J Bras Pneumol 32 (4): 333–8. PMID 17268733. [10] (1994). Alpha-delta sleep in patients with a chief complaint of chronic fatigue. Southern Medical Journal. 87(4) [11] Time. Behavior: Alpha Wave of the Future. Jul, 1971 [12] "Brain Wave Patterns Can Predict Blunders, New Study Finds" (http:/ / www. news. ucdavis. edu/ search/ news_detail. lasso?id=9031). UC Davis News and Information. University of California, Davis campus. 23 March 2009. . [13] http:/ / www. straightdope. com/ columns/ read/ 2942/ can-brainwaves-be-detected-in-lime-jell-o

• Brazier, M. A. B. (1970), The Electrical Activity of the Nervous System, London: Pitman

External links • EEG Alpha waves biofeedback interactive game project (http://www.eegproject.com)

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Beta wave

Beta wave Beta wave, or beta rhythm, is the term used to designate the frequency range of human brain activity between 12 and 30 Hz (12 to 30 transitions or cycles per second). Beta waves are Beta waves split into three sections: Low Beta Waves (12.5-16 Hz, "Beta 1 power"); Beta Waves (16.5–20 Hz, "Beta 2 power"); and High Beta Waves (20.5-28 Hz, "Beta 3 power").[1] Beta states are the states associated with normal waking consciousness.

Function Low amplitude beta waves with multiple and varying frequencies are often associated with active, busy, or anxious thinking and active concentration.[2] Over the motor cortex beta waves are associated with the muscle contractions that happen in isotonic movements and are suppressed prior to and during movement changes.[3] Bursts of beta activity are associated with a strengthening of sensory feedback in static motor control and reduced when there is movement change.[4] Beta activity is increased when movement has to be resisted or voluntarily suppressed.[5] The artificial induction of increased beta waves over the motor cortex by a form of electrical stimulation called Transcranial alternating-current stimulation consistent with its link to isotonic contraction produces a slowing of motor movements.[6]

References [1] Rangaswamy M, Porjesz B, Chorlian DB, Wang K, Jones KA, Bauer LO, Rohrbaugh J, O'Connor SJ, Kuperman S, Reich T, Begleiter (2002). "Beta power in the EEG of alcoholics". BIOLOGICAL PSYCHOLOGY 52 (8): 831–842. PMID 12372655. [2] Baumeister J, Barthel T, Geiss KR, Weiss M (2008). "Influence of phosphatidylserine on cognitive performance and cortical activity after induced stress". NUTRITIONAL NEUROSCIENCE 11 (3): 103–110. PMID 18616866. [3] Baker, SN (2007). "Oscillatory interactions between sensorimotor cortex and the periphery". Current opinion in neurobiology 17 (6): 649–55. doi:10.1016/j.conb.2008.01.007. PMC 2428102. PMID 18339546. [4] Lalo, E; Gilbertson, T; Doyle, L; Di Lazzaro, V; Cioni, B; Brown, P (2007). "Phasic increases in cortical beta activity are associated with alterations in sensory processing in the human". Experimental brain research. Experimentelle Hirnforschung. Experimentation cerebrale 177 (1): 137–45. doi:10.1007/s00221-006-0655-8. PMID 16972074. [5] Zhang, Y; Chen, Y; Bressler, SL; Ding, M (2008). "Response preparation and inhibition: the role of the cortical sensorimotor beta rhythm". Neuroscience 156 (1): 238–46. doi:10.1016/j.neuroscience.2008.06.061. PMC 2684699. PMID 18674598. [6] Pogosyan, A; Gaynor, LD; Eusebio, A; Brown, P (2009). "Boosting cortical activity at Beta-band frequencies slows movement in humans". Current biology : CB 19 (19): 1637–41. doi:10.1016/j.cub.2009.07.074. PMC 2791174. PMID 19800236.

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Gamma wave

Gamma wave A gamma wave is a pattern of neural oscillation in humans with a frequency between 25 to 100 Hz,[1] though 40 Hz is typical.[2] According to a popular theory, gamma Gamma waves waves may be implicated in creating the unity of conscious perception (the binding problem).[3][4][5] However, there is no agreement on the theory; as a researcher suggests: Whether or not gamma wave activity is related to subjective awareness is a very difficult question which cannot be answered with certainty at the present time.[6]

History Gamma waves were initially ignored before the development of digital electroencephalography as analog electroencephalography is restricted to recording and measuring rhythms that are usually less than 25 Hz.[1] One of the earliest reports on them was in 1964 using recordings of the electrical activity of electrodes implanted in the visual cortex of awake monkeys.[7]

Linked to unity of consciousness? History of idea The idea that distinct regions in the brain were being stimulated simultaneously was suggested by the finding in 1988[2] that two neurons oscillate synchronously (though they are not directly connected) when a single external object stimulates their respective receptive fields. Subsequent experiments by many others demonstrated this phenomenon in a wide range of visual cognition. In particular, Francis Crick and Christof Koch in 1990[8] argued that there is a significant relation between the binding problem and the problem of visual consciousness and, as a result, that synchronous 40 Hz oscillations may be causally implicated in visual awareness as well as in visual binding. A lead article by Andreas K. Engel et al. in the journal Consciousness and Cognition (1999) that argues for temporal synchrony as the basis for consciousness, defines the gamma wave hypothesis thus: [9] The hypothesis is that synchronization of neuronal discharges can serve for the integration of distributed neurons into cell assemblies and that this process may underlie the selection of perceptually and behaviorally relevant information.

Role in attentive focus The suggested mechanism is that gamma waves relate to neural consciousness via the mechanism for conscious attention: The proposed answer lies in a wave that originating in the thalamus, sweeps the brain from front to back, 40 times per second, drawing different neuronal circuits into synch with the precept, and thereby bringing the precept into the attentional foreground. If the thalamus is damaged even a little bit, this wave stops, conscious awarenesses do not form, and the patient slips into profound coma.[4] Thus the claim is that when all these neuronal clusters oscillate together during these transient periods of synchronized firing, they help bring up memories and associations from the visual precept to other notions. This

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Gamma wave brings a distributed matrix of cognitive processes together to generate a coherent, concerted cognitive act, such as perception. This has led to theories that gamma waves are associated with solving the binding problem.[3] Gamma waves are observed as neural synchrony from visual cues in both conscious and subliminal stimuli.[10] This research also sheds light on how neural synchrony may explain stochastic resonance in the nervous system.[11] They are also implicated in REM sleep, which involves visualizations, and also during anesthesia.[6]

Contemporary research A 2009 study published in Nature successfully induced gamma waves in mice brains. Researchers performed this study using optogenetics (the method of combining genetic engineering with light to manipulate the activity of individual nerve cells). The protein channelrhodopsin-2 (ChR2), which sensitizes cells to light, was genetically engineered into these mice, specifically to be expressed in a target-group of interneurons. These fast-spiking (FS) interneurons, known for high electrical activity, were then activated with an optical fiber and laser—the second step in optogenetics. In this way, the cell activity of these interneurons was manipulated in the frequency range of 8–200 Hz. The study produced empirical evidence of gamma wave induction in the approximate interval of 25–100 Hz. The gamma waves were most apparent at a frequency of 40 Hz; this indicates that the gamma waves evoked by FS manipulation are a resonating brain circuit property. This is the first study in which it's been shown that a brain state can be induced through the activation of a specific group of cells.[12]

Relation to meditation Experiments on Tibetan Buddhist monks have shown a correlation between transcendental mental states and gamma waves.[13][14] A suggested explanation is based on the fact that the gamma is intrinsically localized. Neuroscientist Sean O'Nuallain suggests that this very existence of synchronized gamma indicates that something akin to a singularity - or, to be more prosaic, a conscious experience - is occurring.[13] This work adduces experimental and simulated data to show that what meditation masters have in common is the ability to put the brain into a state in which it is maximally sensitive. As hinted above, gamma waves have been observed in Tibetan Buddhist monks. A 2004 study took eight long-term Tibetan Buddhist practitioners of meditation and, using electrodes, monitored the patterns of electrical activity produced by their brains as they meditated. The researchers compared the brain activity of the monks to a group of novice meditators (the study had these subjects meditate an hour a day for one week prior to empirical observation). In a normal meditative state, both groups were shown to have similar brain activity. However, when the monks were told to generate an objective feeling of compassion during meditation, their brain activity began to fire in a rhythmic, coherent manner, suggesting neuronal structures were firing in harmony. This was observed at a frequency of 25–40 Hz, the rhythm of gamma waves. These gamma-band oscillations in the monk’s brain signals were the largest seen in humans (apart from those in states such as seizures). Conversely, these gamma-band oscillations were scant in novice meditators. Though, a number of rhythmic signals did appear to strengthen in beginner meditators with further experience in the exercise, implying that the aptitude for one to produce gamma-band rhythm is trainable.[15] Such evidence and research in gamma-band oscillations may explain the heightened sense of consciousness, bliss, and intellectual acuity subsequent to meditation. Notably, meditation is known to have a number of health benefits: stress reduction, mood elevation, and increased life expectancy of the mind and its cognitive functions. The current Dalai Lama meditates for four hours each morning, and he says that it is hard work. He elaborates that if neuroscience can construct a way in which he can reap the psychological and biological rewards of meditation without going through the practice each morning, he would be apt to adopt the innovation.[16] The aforementioned study in which gamma states were induced in mice may be a step in this direction.

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Gamma wave

Opposing evidence Many neuroscientists are not convinced of the gamma wave argument. Arguments against it range from the possibility of mismeasurement – it has been suggested that EEG-measured gamma waves could be in many cases an artifact of electromyographic activity[17][18] – to relations to other neural function, such as minute eye movements[19] However, proponents like O'Nuallain and Andreas Engel argue that gamma evidence persists even with careful signal separation.[13][20] Moreover, recent studies using magnetoencephalography (MEG), which does not suffer the potential artifacts associated with EEG, have identified gamma activity associated with sensory processing, mainly in the visual cortex.[21][22][23][24] Bearing this theory in mind, a number of questions remain unexplained regarding details of exactly how the temporal synchrony results in a conscious awareness or how a new percept "calls for"[4] the synchrony, etc.

Other brain waves • Delta wave – (0.1–4 Hz) • Theta wave – (4–7 Hz) • Alpha wave – (8–12 Hz) • Mu wave – (8–13 Hz) • Beta wave – (12–30 Hz)

References [1] Hughes JR (July 2008). "Gamma, fast, and ultrafast waves of the brain: their relationships with epilepsy and behavior". Epilepsy Behav 13 (1): 25–31. doi:10.1016/j.yebeh.2008.01.011. PMID 18439878. [2] Ian Gold (1999). "Does 40-Hz oscillation play a role in visual consciousness?". Consciousness and Cognition 8 (2): 186–195. doi:10.1006/ccog.1999.0399. PMID 10448001. [3] Buzsaki, György (2006). "Cycle 9, The Gamma Buzz" (http:/ / www. amazon. com/ dp/ 0195301064). Rhythms of the brain. Oxford. . [4] Robert Pollack, The Missing Moment (http:/ / www. cse. iitk. ac. in/ ~amit/ books/ pollack-1999-missing-moment-how. html), 1999 [5] W. Singer and C.M. Gray, Visual feature integration and the temporal correlation hypothesis. Annu. Rev. Neurosci. 18 (1995), pp. 555-586 [6] Vanderwolf CH (Feb 2000). "Are neocortical gamma waves related to consciousness?" (http:/ / linkinghub. elsevier. com/ retrieve/ pii/ S0006-8993(99)02351-3). Brain Res 855 (2): 217–24. doi:10.1016/S0006-8993(99)02351-3. PMID 10677593. . [7] Hughes JR. (1964). Responses from the visual cortex of unanesthetized monkeys. pp. 99–153. In: Pfeiffer CC, Smythies JR, (Eds), International review of neurobiology vol. 7, Academic Press, New York OCLC 43986646 [8] Crick, F., & Koch, C. (1990b). Towards a neurobiological theory of consciousness. Seminars in the Neurosciences v.2, 263-275. [9] Andreas K. Engel, Pascal Fries, Peter Koenig, Michael Brecht, Wolf Singer (1999). "Temporal Binding, Binocular Rivalry, and Consciousness". Consciousness and Cognition 8 (2). [10] Melloni L, Molina C, Pena M, Torres D, Singer W, Rodriguez E (Mar 2007). "Synchronization of neural activity across cortical areas correlates with conscious perception". J Neurosci 27 (11): 2858–65. doi:10.1523/JNEUROSCI.4623-06.2007. PMID 17360907. [11] Ward LM, Doesburg SM, Kitajo K, MacLean SE, Roggeveen AB (Dec 2006). "Neural synchrony in stochastic resonance, attention, and consciousness". Can J Exp Psychol 60 (4): 319–26. doi:10.1037/cjep2006029. PMID 17285879. [12] <J. Cardin, M. Carle, K. Meletis, U. Knoblich, F. Zhang, K. Deisseroth (2009) Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature, 459: 663-668.> [13] O'Nuallain, Sean. "Zero Power and Selflessness: What Meditation and Conscious Perception Have in Common" (https:/ / www. novapublishers. com/ catalog/ product_info. php?products_id=10068). . Retrieved 2009-05-30. Journal: Cognitive Sciences 4(2). [14] Kaufman, Marc (January 3, 2005). "Meditation Gives Brain a Charge, Study Finds" (http:/ / www. washingtonpost. com/ wp-dyn/ articles/ A43006-2005Jan2. html). The Washington Post. . Retrieved May 3, 2010. [15] Lutz A., Greischar L.L., Rawlings N.B., Ricard M., Davidson R.J. (2004). "Long-term meditators self-induce high apmlitude gamma synchrony during mental practice". Proceedings of the National Academy of Sciences USA 101: 16369–16373. [16] "Scientific American:Meditation On Demand" (http:/ / www. scientificamerican. com/ article. cfm?id=meditation-on-demand). . [17] Whitham EM, Pope KJ, Fitzgibbon SP et al. (Aug 2007). "Scalp electrical recording during paralysis: quantitative evidence that EEG frequencies above 20 Hz are contaminated by EMG" (http:/ / linkinghub. elsevier. com/ retrieve/ pii/ S1388-2457(07)00198-8). Clin Neurophysiol 118 (8): 1877–88. doi:10.1016/j.clinph.2007.04.027. PMID 17574912. . [18] Whitham EM, Lewis T, Pope KJ et al. (May 2008). "Thinking activates EMG in scalp electrical recordings" (http:/ / linkinghub. elsevier. com/ retrieve/ pii/ S1388-2457(08)00045-X). Clin Neurophysiol 119 (5): 1166–75. doi:10.1016/j.clinph.2008.01.024. PMID 18329954. .

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Gamma wave [19] Yuval-Greenberg S, Tomer O, Keren AS, Nelken I, Deouell LY (May 2008). "Transient induced gamma-band response in EEG as a manifestation of miniature saccades" (http:/ / linkinghub. elsevier. com/ retrieve/ pii/ S0896-6273(08)00301-2). Neuron 58 (3): 429–41. doi:10.1016/j.neuron.2008.03.027. PMID 18466752. . [20] Dynamic predictions: Oscillations and synchrony in top-down processing, AK Engel, P Fries, W Singer, Nature Reviews Neuroscience, 2001 [21] Adjamian P, Holliday IE, Barnes GR, Hillebrand A, Hadjipapas A, and Singh KD. (2004) Induced stimulus-dependent Gamma oscillations in visual stress. European Journal of Neuroscience; 20: 587–592. [22] Hadjipapas A., Adjamian P, Swettenham J.B., Holliday I.E., Barnes G.R. (2007). "Stimuli of varying spatial scale induce gamma activity with distinct temporal characteristics in human visual cortex". Neuroimage 35 (2): 518–30. [23] Muthukumaraswamy SD, Singh KD (2008). "Spatiotemporal frequency tuning of BOLD and gamma band MEG responses compared in primary visual cortex". NeuroImage 40: 1552–1560. [24] Swettenham JB, Muthukumaraswamy SD, Singh KD (2009). "Spectral properties of induced and evoked gamma oscillations in human early visual cortex to moving and stationary stimuli". Journal of Neurophysiology 102: 1241–1253.

Further reading • Kaufman, Marc (January 3, 2005). "Meditation Gives Brain a Charge, Study Finds" (http://www. washingtonpost.com/wp-dyn/articles/A43006-2005Jan2.html). WashingtonPost.com. Retrieved June 16, 2005. • Bruce Bower (2004). "Synchronized thinking. Brain activity linked to schizophrenia, skillful meditation". Science News (Science News, Vol. 166, No. 20) 166 (20): 310. doi:10.2307/4015767. JSTOR 4015767.

External links • EpilepsyHealth.com (http://www.epilepsyhealth.com/biofeedback.html) - 'A Sampling from Chapter 3' Biofeedback, Neurofeedback and Epilepsy, Sally Fletcher (2005) • Gamma: Insight, Consciousness, or Microsaccades? (http://scienceblogs.com/developingintelligence/2009/06/ gamma_insight_and_consciousnes.php) - A summary of recent research (6/26/2009) • How Thinking Can Change the Brain (http://www.dalailama.com/news/post/ 104-how-thinking-can-change-the-brain), dalailama.com. 2007-01-29.

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Mu wave

Mu wave Mu waves, also known as mu rhythms, comb or wicket rhythms, arciform rhythms, or sensorimotor rhythms, are synchronized patterns of electrical activity involving large numbers of neurons, probably of the One second sample of an EEG alpha wave recording. This wave occurs at frequencies similar to the mu wave, although the alpha wave is detected over a pyramidal type, in the part of the brain that different part of the brain. controls voluntary movement.[1] These patterns as measured by electroencephalography (EEG), magnetoencephalography (MEG), or electrocorticography (ECoG) repeat at a frequency of 8–13 Hz and are most prominent when the body is physically at rest.[1] Unlike the alpha wave, which occurs at a similar frequency over the resting visual cortex at the back of the scalp, the mu wave is found over the motor cortex, in a band The left motor cortex, or BA4, is highlighted in approximately from ear to ear. A person green on this left lateral view of the brain. This is suppresses mu wave patterns when he or she the area over which mu waves are detected performs a motor action or, with practice, bilaterally. when he or she visualizes performing a motor action. This suppression is called desynchronization of the wave because EEG wave forms are caused by large numbers of neurons firing in synchrony. The mu wave is even suppressed when one observes another person performing a motor action. Researchers such as V. S. Ramachandran and colleagues have suggested that this is a sign that the mirror neuron system is involved in mu wave suppression,[2][3] although others disagree.[4] The mu wave is of interest to a variety of scholars. Scientists who study neural development are interested in the details of the development of the mu wave in infancy and childhood and its role in learning.[5] Since a group of researchers believe that autism spectrum disorder (ASD) is strongly influenced by a faulty mirror neuron system[2][6][7] and that mu wave suppression is a downstream indication of mu wave activity,[3] many of these scientists have kindled a more popular interest in investigating the mu wave in people with ASD. Assorted investigators are also in the process of using mu waves to develop a new technology: the brain-computer interface (BCI). With the emergence of BCI systems, clinicians hope to give the severely physically disabled population new methods of communication and a means to manipulate and navigate their environments.[8]

History Mu waves have been studied since the 1930s, and are referred to as the wicket rhythm because the rounded EEG waves resemble croquet wickets. In 1950, Henri Gastaut and his coworkers reported desynchronization of these waves not only during active movements of their subjects, but also while the subjects observed actions executed by someone else.[9][10] These results were later confirmed by additional research groups,[11][12][13] including a study using subdural electrode grids in epileptic patients.[14] The latter study showed mu suppression while the patients observed moving body parts in somatic areas of the cortex that corresponded to the body part moved by the actor. Further studies have shown that the mu waves can also be desynchronized by imagining actions[15][16] and by passively viewing point-light biological motion.[17]

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Mu wave

Mu waves and mirror neurons The mirror neuron system consists of a class of neurons that was first studied in the 1990s in macaque monkeys.[7] Studies have found sets of neurons that fire when these monkeys perform simple tasks and also when the monkeys view others performing the same simple tasks.[18] This suggests they play a role in mapping others' movements into the brain without actually physically performing the movements. These sets of neurons are called mirror neurons and together make up the mirror neuron system. Mu waves are suppressed when these neurons fire, a phenomenon which allows researchers to study mirror neuron activity in humans.[19] There is evidence that mirror neurons exist in humans as well as in non-human animals. The right fusiform gyrus, left inferior parietal lobule, right anterior parietal cortex, and left inferior frontal gyrus are of particular interest.[20][7][21] Some researchers believe that mu wave suppression can be a consequence of mirror neuron activity throughout the brain, and represents a higher-level integrative processing of mirror neuron activity.[3] Tests in both monkeys (using invasive measuring techniques) and humans (using EEG and fMRI) have found that these mirror neurons not only fire during basic motor tasks, but also have components that deal with intention.[22] There is evidence of an important role for mirror neurons in humans, and mu waves may represent a high level coordination of those mirror neurons.[3]

Mirror neurons and autism Autism is a disorder that is associated with social and communicative deficits. A single cause of autism has yet to be identified, but the mu wave and mirror neuron system have been studied specifically for their role in the disorder. In a typically developing individual, the mirror neuron system responds when he or she either watches someone perform a task or performs the task him- or herself. In individuals with autism, mirror neurons become active (and consequently mu waves are suppressed) only when the individual performs the task him- or herself.[2][6] This finding has led some scientists, notably V. S. Ramachandran and colleagues, to view autism as disordered understanding of other individuals' intentions and goals thanks to problems with the mirror neuron system.[7] This deficiency would explain the difficulty people with autism have in communicating with and understanding others. While most studies of the mirror neuron system and mu waves in people with autism have focused on simple motor tasks, some scientists speculate that these tests can be expanded to show that problems with the mirror neuron system underlie overarching cognitive and social deficits.[2][6]

Development A fruitful conceptualization of mu waves in pediatric use that is independent of their frequency is that mu wave suppression is a representation of activity going on in the world, and is detectable in the frontal and parietal networks.[3] A resting oscillation becomes suppressed during the observation of sensory information such as sounds or sights, usually within the frontoparietal (motor) cortical region.[3] Measured in this way, the mu wave is detectable during infancy as early as four to six months, when the peak frequency the wave reaches can be as low as 5.4 Hz.[5][23] There is a rapid increase in peak frequency in the first year of life,[23] and by age two frequency typically reaches 7.5 Hz.[20] The peak frequency of the mu wave increases with age until maturation into adulthood, when it reaches its final and stable frequency of 8–13 Hz.[5][20][23] These varying frequencies are measured as activity around the central sulcus, within the Rolandic cortex.[3] Mu waves are thought to be indicative of an infant’s developing ability to imitate. This is important because the ability to imitate plays a vital role in the development of motor skills, tool use, and understanding causal information through social interaction.[20] Mimicking is integral in the development of social skills and understanding nonverbal cues.[5] Causal relationships can be made through social learning without requiring experience firsthand. In action execution, mu waves are present in both infants and adults before and after the execution of a motor task and its accompanying desynchronization. While executing a goal-oriented action, however, infants exhibit a higher degree of desynchronization than do adults. Just as with an action execution, during action observation infants’ mu waves not only show a desynchronization, but show a desynchronization greater in degree than the one evidenced in

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Mu wave adults.[5] This tendency for changes in degree of desynchronization, rather than actual changes in frequency, becomes the measure for mu wave development throughout adulthood, although the most changes take place during the first year of life.[23] Understanding the mechanisms that are shared between action perception and execution in the earliest years of life has implications for language development. Learning and understanding through social interaction comes from imitating movements as well as vowel sounds. Sharing the experience of attending to an object or event with another person can be a powerful force in the development of language.[24]

Development in individuals with autism Based on findings correlating mirror neuron activity and mu wave suppression in individuals with autism as in typically developing individuals,[25] studies have examined both the development of mirror neurons and therapeutic means for stimulating the system. A recent study has found that fMRI activation magnitudes in the inferior frontal gyrus increase with age in people with autism. This finding was not apparent in typically developing individuals. Furthermore, greater activation was associated with greater amounts of eye contact and better social functioning.[26] Scientists believe the inferior frontal gyrus is one of the main neural correlates with the mirror neuron system in humans and is often related to deficits associated with autism.[21] These findings suggest that the mirror neuron system may not be non-functional in individuals with autism, but simply abnormal in its development. This information is significant to the present discussion because mu waves may be integrating different areas of mirror neuron activity in the brain.[3] Other studies have assessed attempts to consciously stimulate the mirror neuron system and suppress mu waves using neurofeedback (a type of biofeedback given through computers that analyze real time recordings of brain activity, in this case EEGs of mu waves). This type of therapy is still in its early phases of implementation for individuals with autism, and has conflicting forecasts for success.[27][28]

Brain-computer interfaces Brain-computer interfaces (BCIs) are a developing technology that clinicians hope will one day bring more independence and agency to the severely physically disabled. Those the technology has the potential to help include people with near-total or total paralysis, such as those with tetraplegia (quadriplegia) or advanced amyotrophic lateral sclerosis (ALS); BCIs are intended to help them to communicate or even move objects such as motorized wheelchairs, neuroprostheses, or robotic grasping tools.[8][29] Few of these technologies are currently in regular use by people with disabilities, but a diverse array are in development at an experimental level.[8][30][31] One type of BCI uses event-related desynchronization (ERD) of the mu wave in order to control the computer.[8] This method of monitoring brain activity takes advantage of the fact that when a group of neurons is at rest they tend to fire in synchrony with each other. When a participant is cued to imagine movement (an "event"), the resulting desynchronization (the group of neurons that was firing in synchronous waves now firing in complex and individualized patterns) can be reliably detected and analyzed by a computer. Users of such an interface are trained in visualizing movements, typically of the foot, hand, and/or tongue, which are each in different locations on the cortical homunculus and thus distinguishable by an electroencephalograph (EEG) or electrocorticograph (ECoG) recording of electrical activity over the motor cortex.[8][30] In this method, computers monitor for a typical pattern of mu wave ERD contralateral to the visualized movement combined with event-related synchronization (ERS) in the surrounding tissue.[30] This paired pattern intensifies with training,[8][30][31][32] and the training increasingly takes the form of games, some of which utilize virtual reality.[8][30][32] Some researchers have found that the feedback from virtual reality games is particularly effective in giving the user tools to improve control of his or her mu wave patterns.[8][32] The ERD method can be combined with one or more other methods of monitoring the brain's electrical activity to create hybrid BCIs, which often offer more flexibility than a BCI that uses any single monitoring method.[8][30]

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Mu wave

References [1] Amzica, Florin; Fernando Lopes da Silva (2010). "Celluluar Substrates of Brain Rhythms". In Schomer, Donald L.; Fernando Lopes da Silva. Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields (6th ed.). Philadelphia, Pa.: Lippincott Williams & Wilkins. pp. 33–63. ISBN 978-0-7817-8942-4. [2] Oberman, Lindsay M.; Edward M. Hubbarda; Eric L. Altschulera; Vilayanur S. Ramachandran; Jaime A. Pineda (July 2005). "EEG evidence for mirror neuron dysfunction in autism spectrum disorders". Cognitive Brain Research 24 (2): 190–198. doi:10.1016/j.cogbrainres.2005.01.014. PMID 15993757. [3] Pineda, Jaime A. (1). "The functional significance of mu rhythms: Translating “seeing” and “hearing” into “doing”" (http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 15925412). Brain Research Reviews 50 (1): 57–68. doi:10.1016/j.brainresrev.2005.04.005. PMID 15925412. . Retrieved 12 November 2012. [4] Churchland, Patricia (2011). Braintrust: What Neuroscience Tells Us About Morality. Princeton, NJ: Princeton University Press. pp. 156. ISBN 978-0-691-13703-2. [5] Nyström, Pär; Ljunghammar, Therese; Rosander, Kerstin; Von Hofsten, Claes (March 2011). "Using mu rhythm desynchronization to measure mirror neuron activity in infants" (http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 22213903). Developmental Science 14 (2): 327–335. doi:10.1111/j.1467-7687.2010.00979.x. PMID 22213903. . Retrieved 2 October 2012. [6] Bernier, R.; Dawson, G.; Webb, S.; Murias, M. (1 August 2007). "EEG mu rhythm and imitation impairments in individuals with autism spectrum disorder" (http:/ / www. ncbi. nlm. nih. gov/ pmc/ articles/ PMC2709976/ ). Brain and Cognition 64 (3): 228–237. doi:10.1016/j.bandc.2007.03.004. PMID 17451856. . Retrieved 15 September 2012. [7] Williams, Justin H.G.; Waiter, Gordon D.; Gilchrist, Anne; Perrett, David I.; Murray, Alison D.; Whiten, Andrew (1 January 2006). "Neural mechanisms of imitation and ‘mirror neuron’ functioning in autistic spectrum disorder". Neuropsychologia 44 (4): 610–621. doi:10.1016/j.neuropsychologia.2005.06.010. PMID 16140346. [8] Pfurtscheller, Gert; Christa Neuper (2010). "EEG-Based Brain-Computer Interfaces". In Schomer, Donald L.; Fernando H. Lopes da Silva. Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields (6th ed. ed.). Philadelphia, Pa.: Lippincott Williams & Wilkins. pp. 1227–1236. ISBN 978-0-7817-8942-4. [9] Cohen-Seat, G., Gastaut, H., Faure, J., & Heuyer, G. (1954). Etudes experimentales de l’activite nerveuse pendant la projection cinematographique. Rev. Int. Filmologie, 5, 7-64. [10] Gastaut, H. J., & Bert, J. (1954). EEG changes during cinematographic presentation. Electroencephalogr. Clin. Neurophysiol., 6, 433-444. [11] Cochin, S., Barthelemy, C., Lejeune, B., Roux, S., & Martineau, J. (1998). Perception of motion and qEEG activity in human adults. Electroencephalogr Clin Neurophysiol, 107(4), 287-295. [12] Cochin, S., Barthelemy, C., Roux, S., & Martineau, J. (1999). Observation and execution of movement: similarities demonstrated by quantified electroencephalography. Eur J Neurosci, 11(5), 1839-1842. [13] Muthukumaraswamy, S. D., Johnson, B. W., & McNair, N. A. (2004). Mu rhythm modulation during observation of an object-directed grasp. Brain Res Cogn Brain Res, 19(2), 195-201. [14] Arroyo, S., Lesser, R. P., Gordon, B., Uematsu, S., Jackson, D., & Webber, R. (1993). Functional significance of the mu rhythm of human cortex: an electrophysiologic study with subdural electrodes. Electroencephalography and Clinical Neurophysiology 87(3), 76-87. [15] Pfurtscheller, G., Brunner, C., Schlogl, A., & Lopes da Silva, F. H. (2006). Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage, 31(1), 153-159. [16] Pineda, J. A., Allison, B. Z., & Vankov, A. (2000). The effects of self-movement, observation, and imagination on mu rhythms and readiness potentials (RP's): toward a brain–computer interface (BCI). IEEE Trans Rehabil Eng, 8(2), 219-222. [17] Ulloa, E. R., & Pineda, J. A. (2007). Recognition of point-light biological motion: mu rhythms and mirror neuron activity. Behav Brain Res, 183(2), 188-194. [18] di Pellegrino, G.; Fadiga, L.; Fogassi, L.; Gallese, F.; Rizzolatti, G. (1992). "Understanding motor events: A neurophysiological study". Experimental Brain Research 91 (1): 176–180. PMID 1301372. [19] Rizzolatti, G; Fogassi, L; Gallese, V (2001 Sep). "Neurophysiological mechanisms underlying the understanding and imitation of action.". Nature reviews. Neuroscience 2 (9): 661–70. PMID 11533734. [20] Marshall, Peter J.; Meltzoff, Andrew N. (April 2011). "Neural mirroring systems: Exploring the EEG mu rhythm in human infancy" (http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 21528008). Developmental Cognitive Neuroscience 1 (2): 110–123. doi:10.1016/j.dcn.2010.09.001. PMID PMC3081582. . Retrieved 23 October 2012. [21] Keuken, M. C.; Hardie, A.; Dorn, B. T.; Dev, S.; Paulus, M. P.; Jonas, K. J.; Den Wildenberg, W. P.; Pineda, J. A. (6). "The role of the left inferior frontal gyrus in social perception: an rTMS study" (http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 21281612). Brain Research 1383: 196–205. PMID 21281612. . Retrieved 21 October 2012. [22] Sinigaglia, C; Rizzolatti, G (2011 Mar). "Through the looking glass: self and others." (http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 21220203). Consciousness and cognition 20 (1): 64–74. PMID 21220203. . Retrieved 8 November 2012. [23] Berchicci, M.; Zhang, T.; Romero, L.; Peters, A.; Annett, R.; Teuscher, U.; Bertollo, M.; Okada, Y.; Stephen, J.; Comani, S. (21 July 2011). "Development of Mu Rhythm in Infants and Preschool Children" (http:/ / www. ncbi. nlm. nih. gov/ pubmed?term=(Berchicci[Author]) AND development[Title]). Developmental Neuroscience 33 (2): 130–143. doi:10.1159/000329095. PMID 21778699. . Retrieved 2 October 2012. [24] Meltzoff, A. N.; Kuhl, P. K.; Movellan, J.; Sejnowski, T. J. (16). "Foundations for a New Science of Learning" (http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 19608908). Science 325 (5938): 284–288. doi:10.1126/science.1175626. PMID PMC2776823. . Retrieved 23 October 2012.

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Mu wave [25] Pineda, J.A.; Juavinett, A.; Datko, M. (1). "Self-regulation of brain oscillations as a treatment for aberrant brain connections in children with autism" (http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 22999736). Medical Hypotheses 79 (6): 790–798. doi:10.1016/j.mehy.2012.08.031. PMID 22999736. . Retrieved 11 November 2012. [26] Bastiaansen, JA; Thioux, M; Nanetti, L; van der Gaag, C; Ketelaars, C; Minderaa, R; Keysers, C (1). "Age-related increase in inferior frontal gyrus activity and social functioning in autism spectrum disorder." (http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 21310395). Biological Psychiatry 69 (9): 832–838. PMID 21310395. . Retrieved 21 October 2012. [27] Holtmann, Martin; Steiner, Sabina; Hohmann, Sarah; Poustka, Luise; Banaschewski, Tobias; Bölte, Sven (1). "Neurofeedback in autism spectrum disorders" (http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 21752020). Developmental Medicine & Child Neurology 53 (11): 986–993. doi:10.1111/j.1469-8749.2011.04043.x. PMID 21752020. . Retrieved 10 November 2012. [28] Coben, Robert; Linden, Michael; Myers, Thomas E. (24). "Neurofeedback for Autistic Spectrum Disorder: A Review of the Literature" (http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 19856096). Applied Psychophysiology and Biofeedback 35 (1): 83–105. doi:10.1007/s10484-009-9117-y. PMID 19856096. . Retrieved 10 November 2012. [29] Machado, S; Araújo, F; Paes, F; Velasques, B; Cunha, M; Budde, H; Basile, LF; Anghinah, R; Arias-Carrión, O; Cagy, M; Piedade, R; de Graaf, TA; Sack, AT; Ribeiro, P (2010). "EEG-based brain-computer interfaces: an overview of basic concepts and clinical applications in neurorehabilitation." (http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 21438193). Reviews in the neurosciences 21 (6): 451–68. PMID 21438193. . Retrieved 10 November 2012. [30] Pfurtscheller, Gert; McFarland, Dennis J. (2012). "BCIs that use sensorimotor rhythms". In Wolpaw, Jonathan R.; Wolpaw, Elizabeth Winter. Brain-Computer Interfaces: Principles and Practice. Oxford: Oxford University Press. pp. 227–240. ISBN 9780195388855. [31] Leuthardt, Eric C.; Schalk, Gerwin; Roland, Jarod; Rouse, Adam; Moran, Daniel W. (July 2009). "Evolution of brain-computer interfaces: going beyond classic motor physiology" (http:/ / www. ncbi. nlm. nih. gov/ pmc/ articles/ PMC2920041/ ). Neurosurgical Focus 27 (1): E4. doi:10.3171/2009.4.FOCUS0979. PMID PMC2920041. . Retrieved 21 October 2012. [32] Allison, B Z; Leeb, R; Brunner, C; Müller-Putz, G R; Bauernfeind, G; Kelly, J W; Neuper, C (1). "Toward smarter BCIs: extending BCIs through hybridization and intelligent control" (http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 22156029). Journal of Neural Engineering 9 (1): 013001. doi:10.1088/1741-2560/9/1/013001. PMID 22156029. . Retrieved 10 November 2012.

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Hypothalamus

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Hypothalamus Brain: Hypothalamus

Location of the human hypothalamus

Diencephalon Latin

hypothalamus

Gray's

subject #189 812

[1]

NeuroNames hier-358 [2] MeSH

Hypothalamus

[3]

NeuroLex ID birnlex_734 [4]

The hypothalamus (from Greek ὑπό = under and θάλαμος = room, chamber) is a portion of the brain that contains a number of small nuclei with a variety of functions. One of the most important functions of the hypothalamus is to link the nervous system to the endocrine system via the pituitary gland (hypophysis). The hypothalamus is located below the thalamus, just above the brain stem. In the terminology of neuroanatomy, it forms the ventral part of the diencephalon. All vertebrate brains contain a hypothalamus. In humans, it is roughly the size of an almond. The hypothalamus is responsible for certain metabolic processes and other activities of the autonomic nervous system. It synthesizes and secretes certain neurohormones, often called hypothalamic-releasing hormones, and these in turn stimulate or inhibit the secretion of pituitary hormones. The hypothalamus controls body temperature, hunger, thirst,[5] fatigue, sleep, and circadian cycles.

Structure and inputs The hypothalamus is a brain structure composed of distinct nuclei and less anatomically distinct areas. It is found in all vertebrate nervous systems. In mammals, the axons of magnocellular neurosecretory cells of the paraventricular nucleus and the supraoptic nucleus, which contain oxytocin and vasopressin (also called antidiuretic hormone), comprise the posterior pituitary. Parvocellular neurons of the paraventricular nucleus contain neurons that release corticotropin-releasing hormone and other hormones into the hypophyseal portal system where these hormones diffuse to the anterior pituitary.

Hypothalamus The hypothalamus coordinates many hormonal and behavioural circadian rhythms, complex patterns of neuroendocrine outputs, complex homeostatic mechanisms,[6] and important behaviours. The hypothalamus must therefore respond to many different signals, some of which are generated externally and some internally. The hypothalamus is thus richly connected with many parts of the central nervous system, including the brainstem reticular formation and autonomic zones, the limbic forebrain (particularly the amygdala, septum, diagonal band of Broca, and the olfactory bulbs, and the cerebral cortex). The hypothalamus is responsive to: • • • • • •

Light: daylength and photoperiod for regulating circadian and seasonal rhythms Olfactory stimuli, including pheromones Steroids, including gonadal steroids and corticosteroids Neurally transmitted information arising in particular from the heart, the stomach, and the reproductive tract Autonomic inputs Blood-borne stimuli, including leptin, ghrelin, angiotensin, insulin, pituitary hormones, cytokines, plasma concentrations of glucose and osmolarity etc. • Stress • Invading microorganisms by increasing body temperature, resetting the body's thermostat upward.

Olfactory stimuli Olfactory stimuli are important for sex and neuroendocrine function in many species. For instance if a pregnant mouse is exposed to the urine of a 'strange' male during a critical period after coitus then the pregnancy fails (the Bruce effect). Thus during coitus, a female mouse forms a precise 'olfactory memory' of her partner which persists for several days. Pheromonal cues aid synchronisation of oestrus in many species; in women, synchronised menstruation may also arise from pheromonal cues, although the role of pheromones in humans is doubted by many.

Blood-borne stimuli Peptide hormones have important influences upon the hypothalamus, and to do so they must evade the blood–brain barrier. The hypothalamus is bounded in part by specialized brain regions that lack an effective blood–brain barrier; the capillary endothelium at these sites is fenestrated to allow free passage of even large proteins and other molecules. Some of these sites are the sites of neurosecretion - the neurohypophysis and the median eminence. However others are sites at which the brain samples the composition of the blood. Two of these sites, the SFO (subfornical organ) and the OVLT (organum vasculosum of the lamina terminalis) are so-called circumventricular organs, where neurons are in intimate contact with both blood and CSF. These structures are densely vascularized, and contain osmoreceptive and sodium-receptive neurons which control drinking, vasopressin release, sodium excretion, and sodium appetite. They also contain neurons with receptors for angiotensin, atrial natriuretic factor, endothelin and relaxin, each of which is important in the regulation of fluid and electrolyte balance. Neurons in the OVLT and SFO project to the supraoptic nucleus and paraventricular nucleus, and also to preoptic hypothalamic areas. The circumventricular organs may also be the site of action of interleukins to elicit both fever and ACTH secretion, via effects on paraventricular neurons. It is not clear how all peptides that influence hypothalamic activity gain the necessary access. In the case of prolactin and leptin, there is evidence of active uptake at the choroid plexus from blood into CSF. Some pituitary hormones have a negative feedback influence upon hypothalamic secretion; for example, growth hormone feeds back on the hypothalamus, but how it enters the brain is not clear. There is also evidence for central actions of prolactin. Findings have suggested that thyroid hormone (T4) is taken up by the hypothalamic glial cells in the infundibular nucleus/ median eminence, and that it is here converted into T3 by the type 2 deiodinase (D2). Subsequently, T3 is transported into the thyrotropin-releasing hormone (TRH) producing neurons in the paraventricular nucleus. There has been found thyroid hormone receptors in these neurons, indicating that they are indeed sensitive to T3 stimuli.

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Hypothalamus Additionally these neurons expressed MCT8, a thyroid hormone transporter, supporting the theory that T3 is transported into them. T3 could then bind to the thyroid hormone receptor in these neurons, and affect the production of thyrotropin-releasing hormone, and thereby regulating thyroid hormone production. [7] The hypothalamus functions as a type of thermostat for the body.[8] It sets a desired body temperature, and stimulates either heat production and retention to raise the blood temperature to a higher setting, or sweating and vasodilation to cool the blood to a lower temperature. All fevers result from a raised setting in the hypothalamus; elevated body temperatures due to any other cause are classified as hyperthermia.[8] Rarely, direct damage to the hypothalamus, such as from a stroke, will cause a fever; this is sometimes called a hypothalamic fever. However, it is more common for such damage to cause abnormally low body temperatures.[8]

Steroids The hypothalamus contains neurons that react strongly to steroids and glucocorticoids – (the steroid hormones of the adrenal gland, released in response to ACTH). It also contains specialized glucose-sensitive neurons (in the arcuate nucleus and ventromedial hypothalamus), which are important for appetite. The preoptic area contains thermosensitive neurons; these are important for TRH secretion.

Neural inputs The hypothalamus receives many inputs from the brainstem; notably from the nucleus of the solitary tract, the locus coeruleus, and the ventrolateral medulla. Oxytocin secretion in response to suckling or vagino-cervical stimulation is mediated by some of these pathways; vasopressin secretion in response to cardiovascular stimuli arising from chemoreceptors in the carotid body and aortic arch, and from low-pressure atrial volume receptors, is mediated by others. In the rat, stimulation of the vagina also causes prolactin secretion, and this results in pseudo-pregnancy following an infertile mating. In the rabbit, coitus elicits reflex ovulation. In the sheep, cervical stimulation in the presence of high levels of estrogen can induce maternal behavior in a virgin ewe. These effects are all mediated by the hypothalamus, and the information is carried mainly by spinal pathways that relay in the brainstem. Stimulation of the nipples stimulates release of oxytocin and prolactin and suppresses the release of LH and FSH. Cardiovascular stimuli are carried by the vagus nerve, but the vagus also conveys a variety of visceral information, including for instance signals arising from gastric distension to suppress feeding. Again this information reaches the hypothalamus via relays in the brainstem. In addition hypothalamic function is responsive to --and regulated by-- levels of all three classical monoamine neurotransmitters, i.e. noradrenaline, dopamine and 5-hydroxytryptamine (serotonin), in those tracts from which it receives enervation. For example noradrenergic inputs arising from the locus coeruleus have important regulatory effects upon CRH levels.

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Hypothalamus

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Nuclei The hypothalamic nuclei include the following:[9][10][11]

A cross section of the monkey hypothalamus displays 2 of the major hypothalamic nuclei on either side of the fluid-filled 3rd ventricle.

Hypothalamic nuclei

Hypothalamus

70

Hypothalamic nuclei on one side of the hypothalamus, shown in a 3-D computer reconstruction

Region

Area

Nucleus

Function

[12]

Anterior Medial Medial preoptic nucleus

• •

Regulates the release of gonadotropic hormones from the adenohypophysis Contains the sexually dimorphic nucleus, which releases GnRH, differential development between sexes is based upon in utero testosterone levels

Supraoptic nucleus (SO)

• •

oxytocin release vasopressin release

Paraventricular nucleus* (PV)

• • •

corticotropin-releasing hormone release oxytocin release [13] vasopressin release

Anterior hypothalamic nucleus (AH)

• • • •

thermoregulation panting sweating thyrotropin inhibition

Suprachiasmatic nucleus (SC)

• •

vasopressin release Circadian rhythms

Lateral nucleus (LT)

•

thirst and hunger

Part of supraoptic nucleus (SO)

•

vasopressin release

Lateral Lateral preoptic nucleus

Hypothalamus

Tuberal

71

Medial Dorsomedial hypothalamic nucleus (DM)

• • •

Blood Pressure Heart Rate GI stimulation

Ventromedial nucleus (VM)

• •

satiety neuroendocrine control

Arcuate nucleus (AR)

• • •

Growth hormone-releasing hormone (GHRH) feeding Dopamine

•

thirst and hunger

•

memory

• • •

Increase blood pressure pupillary dilation shivering

Lateral Lateral nucleus (LT) Lateral tuberal nuclei Posterior Medial Mammillary nuclei (part of mammillary bodies) (MB) Posterior nucleus (PN)

Lateral Lateral nucleus (LT)

• - Note: Paraventricular nucleus is not to be confused with periventricular nucleus. See also: ventrolateral preoptic nucleus, periventricular nucleus.

Outputs The outputs of the hypothalamus can be divided into two categories: neural projections, and endocrine hormones.[14]

Neural projections Most fiber systems of the hypothalamus run in two ways (bidirectional). • Projections to areas caudal to the hypothalamus go through the medial forebrain bundle, the mammillotegmental tract and the dorsal longitudinal fasciculus. • Projections to areas rostral to the hypothalamus are carried by the mammillothalamic tract, the fornix and terminal stria. • Projections to areas of the sympathetic motor system (lateral horn spinal segments T1-L2/L3) are carried by the hypothalamospinal tract and they activate the sympathetic motor pathway

Endocrine hormones The hypothalamus contains cells that produce thyrotropin-releasing hormone, gonadotropin-releasing hormone, growth hormone-releasing hormone, corticotropin-releasing hormone, somatostatin, and dopamine, as well as vasopressin and oxytocin. These assorted hormones are released into the blood stream, targeting other organ systems, most notably the pituitary.[15][16] The hypothalamus affects the endocrine system and governs emotional behavior, such as anger and sexual activity. Most of the hypothalamic hormones generated are distributed to the pituitary via the hypophyseal portal system.[17] The hypothalamus maintains homeostasis; this includes a regulation of blood pressure, heart rate, and temperature.

Hypothalamus

Secreted hormone Thyrotropin-releasing hormone (Prolactin-releasing hormone) Dopamine (Prolactin-inhibiting hormone) Growth hormone-releasing hormone

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Abbreviation

Produced by

Effect

TRH, TRF, or PRH

Parvocellular neurosecretory neurons

Stimulate thyroid-stimulating hormone (TSH) release from anterior pituitary (primarily) Stimulate prolactin release from anterior pituitary

DA or PIH

Dopamine neurons of the arcuate nucleus

Inhibit prolactin release from anterior pituitary

GHRH

Neuroendocrine neurons of Stimulate Growth hormone (GH) release from anterior pituitary the Arcuate nucleus

Somatostatin SS, GHIH, or (growth hormone-inhibiting SRIF hormone)

Neuroendocrine cells of the Inhibit Growth hormone (GH) release from anterior pituitary Periventricular nucleus Inhibit thyroid-stimulating hormone (TSH) release from anterior pituitary

Gonadotropin-releasing hormone

GnRH or LHRH

Neuroendocrine cells of the Stimulate follicle-stimulating hormone (FSH) release from anterior Preoptic area pituitary Stimulate luteinizing hormone (LH) release from anterior pituitary

Corticotropin-releasing hormone

CRH or CRF

Parvocellular neurosecretory neurons

Stimulate adrenocorticotropic hormone (ACTH) release from anterior pituitary

Magnocellular neurosecretory cells

Uterine contraction Lactation (letdown reflex)

Magnocellular neurosecretory neurons

Increase in the permeability to water of the cells of distal tubule and collecting duct in the kidney and thus allows water reabsorption and excretion of concentrated urine

Oxytocin

Vasopressin (antidiuretic hormone)

ADH or AVP

Control of food intake The extreme lateral part of the ventromedial nucleus of the hypothalamus is responsible for the control of food intake. Stimulation of this area causes increased food intake. Bilateral lesion of this area causes complete cessation of food intake. Medial parts of the nucleus have a controlling effect on the lateral part. Bilateral lesion of the medial part of the ventromedial nucleus causes hyperphagia and obesity of the animal. Further lesion of the lateral part of the ventromedial nucleus in the same animal produces complete cessation of food intake. There are different hypotheses related to this regulation:[18] 1. Lipostatic hypothesis - this hypothesis holds that adipose tissue produces a humoral signal that is proportionate to the amount of fat and acts on the hypothalamus to decrease food intake and increase energy output. It has been evident that a hormone leptin acts on the hypothalamus to decrease food intake and increase energy output. 2. Gutpeptide hypothesis - gastrointestinal hormones like Grp, glucagons, CCK and others claimed to inhibit food intake. The food entering the gastrointestinal tract triggers the release of these hormones which acts on the brain to produce satiety. The brain contains both CCK-A and CCK-B receptors. 3. Glucostatic hypothesis - the activity of the satiety center in the ventromedial nuclei is probably governed by the glucose utilization in the neurons. It has been postulated that when their glucose utilization is low and consequently when the arteriovenous blood glucose difference across them is low, the activity across the neurons decrease. Under these conditions, the activity of the feeding center is unchecked and the individual feels hungry. Food intake is rapidly increased by intraventricular administration of 2-deoxyglucose therefore decreasing glucose utilization in cells. 4. Thermostatic hypothesis - according to this hypothesis, a decrease in body temperature below a given set point stimulates appetite, while an increase above the set point inhibits appetite.

Hypothalamus

Sexual dimorphism Several hypothalamic nuclei are sexually dimorphic, i.e. there are clear differences in both structure and function between males and females. Some differences are apparent even in gross neuroanatomy: most notable is the sexually dimorphic nucleus within the preoptic area. However most of the differences are subtle changes in the connectivity and chemical sensitivity of particular sets of neurons. The importance of these changes can be recognised by functional differences between males and females. For instance, males of most species prefer the odor and appearance of females over males, which is instrumental in stimulating male sexual behavior. If the sexually dimorphic nucleus is lesioned, this preference for females by males diminishes. Also, the pattern of secretion of growth hormone is sexually dimorphic, and this is one reason why in many species, adult males are much larger than females.

Responses to ovarian steroids Other striking functional dimorphisms are in the behavioral responses to ovarian steroids of the adult. Males and females respond differently to ovarian steroids, partly because the expression of estrogen-sensitive neurons in the hypothalamus is sexually dimorphic, i.e. estrogen receptors are expressed in different sets of neurons. Estrogen and progesterone can influence gene expression in particular neurons or induce changes in cell membrane potential and kinase activation, leading to diverse non-genomic cellular functions. Estrogen and progesterone bind to their cognate nuclear hormone receptors, which translocate to the cell nucleus and interact with regions of DNA known as hormone response elements (HREs) or get tethered to another transcription factor's binding site. Estrogen receptor (ER) has been shown to transactivate other transcription factors in this manner, despite the absence of an estrogen response element (ERE) in the proximal promoter region of the gene. ERs and progesterone receptors (PRs) are generally gene activators, with increased mRNA and subsequent protein synthesis following hormone exposure. Male and female brains differ in the distribution of estrogen receptors, and this difference is an irreversible consequence of neonatal steroid exposure. Estrogen receptors (and progesterone receptors) are found mainly in neurons in the anterior and mediobasal hypothalamus, notably: • the preoptic area (where LHRH neurons are located) • the periventricular nucleus (where somatostatin neurons are located) • the ventromedial hypothalamus (which is important for sexual behavior).

Gonadal steroids in neonatal life of rats In neonatal life, gonadal steroids influence the development of the neuroendocrine hypothalamus. For instance, they determine the ability of females to exhibit a normal reproductive cycle, and of males and females to display appropriate reproductive behaviors in adult life. • If a female rat is injected once with testosterone in the first few days of postnatal life (during the "critical period" of sex-steroid influence), the hypothalamus is irreversibly masculinized; the adult rat will be incapable of generating an LH surge in response to estrogen (a characteristic of females), but will be capable of exhibiting male sexual behaviors (mounting a sexually receptive female). • By contrast, a male rat castrated just after birth will be feminized, and the adult will show female sexual behavior in response to estrogen (sexual receptivity, lordosis behavior).

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Hypothalamus

Androgens in primates In primates, the developmental influence of androgens is less clear, and the consequences are less understood. Within the brain, testosterone is aromatized to (estradiol), which is the principal active hormone for developmental influences. The human testis secretes high levels of testosterone from about week 8 of fetal life until 5–6 months after birth (a similar perinatal surge in testosterone is observed in many species), a process that appears to underlie the male phenotype. Estrogen from the maternal circulation is relatively ineffective, partly because of the high circulating levels of steroid-binding proteins in pregnancy.

Human sexual orientation and the hypothalamus According to D.F. Swaab, writing in a July 2008 paper, "Neurobiological research related to sexual orientation in humans is only just gathering momentum, but the evidence already shows that humans have a vast array of brain differences, not only in relation to gender, but also in relation to sexual orientation."[19] Swaab first reported on the relationship between sexual orientation in males and the hypothalamus's "clock", the suprachiasmatic nucleus (SCN). In 1990, Swaab and Hofman[20] reported that the suprachiasmatic nucleus in homosexual men was significantly larger than in heterosexual men. Then in 1995, Swaab et al.[21] linked brain development to sexual orientation by treating male rats both pre- and postnatally with ATD, an aromatase blocker in the brain. This produced an enlarged SCN and bisexual behavior in the adult male rats. In 1991, LeVay showed that part of the sexually dimorphic nucleus (SDN) known as the 3rd interstitial nucleus of the anterior hypothalamus (INAH 3), is nearly twice as large in heterosexual men than in homosexual men and heterosexual women, in terms of volume. In 2004 and 2006, two studies by Berglund, Lindström, and Savic[22][23] used Positron Emission Tomography (PET) to observe how the hypothalamus responds to smelling common odors, the scent of testosterone found in male sweat, and the scent of estrogen found in female urine. These studies showed that the hypothalamus of heterosexual men and homosexual women both respond to estrogen. Also, the hypothalamus of homosexual men and heterosexual women both respond to testosterone. The hypothalamus of all four groups did not respond to the common odors, which produced a normal olfactory response in the brain.

Other influences upon hypothalamic development Sex steroids are not the only important influences upon hypothalamic development; in particular, pre-pubertal stress in early life (of rats) determines the capacity of the adult hypothalamus to respond to an acute stressor.[24] Unlike gonadal steroid receptors, glucocorticoid receptors are very widespread throughout the brain; in the paraventricular nucleus, they mediate negative feedback control of CRF synthesis and secretion, but elsewhere their role is not well understood.

Fear processing The medial zone of hypothalamus is part of a circuitry that controls motivated behaviors, like defensive behaviors.[25] Analyses of Fos-labeling showed that a series of nuclei in the "behavioral control column" is important in regulating the expression of innate and conditioned defensive behaviors.[26]

Antipredatory defensive behavior Exposure to a predator (such as a cat) elicits defensive behaviors in laboratory rodents, even when the animal has never been exposed to a cat.[27] In the hypothalamus, this exposure causes an increase in Fos-labeled cells in the anterior hypothalamic nucleus, the dorsomedial part of the ventromedial nucleus, and in the ventrolateral part of the premammillary nucleus (PMDvl).[28] The premammillary nucleus has an important role in expression of defensive behaviors towards a predator, since lesions in this nucleus abolish defensive behaviors, like freezing and

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flight.[29][28] The PMD does not modulate defensive behavior in other situations, as lesions of this nucleus had minimal effects on post-shock freezing scores.[29] The PMD has important connections to the dorsal periaqueductal gray, an important structure in fear expression.[30][31] In addition, animals display risk assessment behaviors to the environment previously associated with the cat. Fos-labeled cell analysis showed that the PMDvl is the most activated structure in the hypothalamus, and inactivation with muscimol prior to exposure to the context abolishes the defensive behavior.[28] Therefore, the hypothalamus, mainly the PMDvl, has an important role in expression of innate and conditioned defensive behaviors to a predator.

Social defeat Likewise, the hypothalamus has a role in social defeat: nuclei in medial zone are also mobilized during an encounter with an aggressive conspecific. The defeated animal has an increase in Fos levels in sexually dimorphic structures, such as the medial pre-optic nucleus, the ventrolateral part of ventromedial nucleus, and the ventral premammilary nucleus.[32] Such structures are important in other social behaviors, such as sexual and aggressive behaviors. Moreover, the premammillary nucleus also is mobilized, the dorsomedial part but not the ventrolateral part.[32] Lesions in this nucleus abolish passive defensive behavior, like freezing and the "on-the-back" posture.[32]

Additional images

Median sagittal section of brain of human embryo of three months.

Human brain left dissected midsagittal view

Endocrine glands in the human head and neck and their hormones

References [1] [2] [3] [4] [5] [6] [7]

http:/ / education. yahoo. com/ reference/ gray/ subjects/ subject?id=189#p812 http:/ / braininfo. rprc. washington. edu/ Scripts/ hiercentraldirectory. aspx?ID=358 http:/ / www. nlm. nih. gov/ cgi/ mesh/ 2007/ MB_cgi?mode=& term=Hypothalamus http:/ / www. neurolex. org/ wiki/ birnlex_734 Definition of hypothalamus - NCI Dictionary of Cancer Terms (http:/ / www. cancer. gov/ Templates/ db_alpha. aspx?CdrID=46359) hypothalamus (http:/ / www. sci. uidaho. edu/ med532/ hypothal. htm) Fliers, Eric; Unmehopa, Alkemade (7 June 2006). "Functional neuroanatomy of thyroid hormone feedback in the human hypothalamus and pituitary gland" (http:/ / www. ncbi. nlm. nih. gov/ pubmed/ 16707210). Molecular and Cellular Endocrinology 251 (1–2): 1–8. doi:10.1016/j.mce.2006.03.042. PMID 16707210. . Retrieved 7 July 2011. [8] Fauci, Anthony, et al. (2008). Harrison's Principles of Internal Medicine (17 ed.). McGraw-Hill Professional. pp. 117–121. ISBN 978-0-07-146633-2. [9] Diagram of Nuclei (psycheducation.org) (http:/ / www. psycheducation. org/ emotion/ pics/ big hypothalamus. htm) [10] Diagram of Nuclei (universe-review.ca) (http:/ / universe-review. ca/ I10-80-nuclei. jpg) [11] Diagram of Nuclei (utdallas.edu) (http:/ / www. utdallas. edu/ ~tres/ integ/ hom3/ display13_04. html) [12] Unless else specified in table, then ref is: Guyton Twelfth Edition [13] Walter F., PhD. Boron (2005). Medical Physiology: A Cellular And Molecular Approaoch. Elsevier/Saunders. ISBN 1-4160-2328-3. Page 840 [14] Hypothalamus and ANS (http:/ / thalamus. wustl. edu/ course/ hypoANS. html) [15] Hormones of the Hypothalamus (http:/ / biology. about. com/ gi/ o. htm?zi=1/ XJ& zTi=1& sdn=biology& cdn=education& tm=17& gps=159_804_1263_647& f=00& tt=11& bt=0& bts=0& zu=http:/ / www. ultranet. com/ ~jkimball/ BiologyPages/ H/ Hypothalamus. html)

Hypothalamus [16] Melmed S, Jameson JL (2005). "Disorders of the anterior pituitary and hypothalamus". In Kasper DL, Braunwald E, Fauci AS, et al.. Harrison's Principles of Internal Medicine (16th ed.). New York, NY: McGraw-Hill. pp. 2076–97. ISBN 0-07-139140-1. [17] Overview of Hypothalamic and Pituitary Hormones (http:/ / www. vivo. colostate. edu/ hbooks/ pathphys/ endocrine/ hypopit/ overview. html) [18] Theologides A (1976). "Anorexia-producing intermediary metabolites". Am J Clin Nutr 29 (5): 552–8. PMID 178168. [19] Swaab DF (2008). "Sexual orientation and its basis in brain structure and function". http:/ / www. pnas. org/ content/ 105/ 30/ 10273. full 105 (30): 10273–10274. doi:10.1073/pnas.0805542105. PMC 2492513. PMID 18653758. [20] Swaab DF, Hofman MA (1990). "An enlarged suprachiasmatic nucleus in homosexual men". Brain Res. 537 (1–2): 141–8. doi:10.1016/0006-8993(90)90350-K. PMID 2085769. [21] Swaab DF, Slob AK, Houtsmuller EJ, Brand T, Zhou JN (1995). "Increased number of vasopressin neurons in the suprachiasmatic nucleus (SCN) of 'bisexual' adult male rats following perinatal treatment with the aromatase blocker ATD". Developmental Brain Research 85 (2): 273–279. doi:10.1016/0165-3806(94)00218-O. PMID 7600674. [22] Savic I, Berglund H, Lindström P (2005). "Brain response to putative pheromones in homosexual men". PNAS 102 (20): 7356–7361. doi:10.1073/pnas.0407998102. PMC 1129091. PMID 15883379. [23] Savic I, Berglund H, Lindström P (2006). "Brain response to putative pheromones in lesbian women". PNAS 103 (21): 8269–8274. doi:10.1073/pnas.0600331103. PMC 1570103. PMID 16705035. [24] Romeo, Russell D; Rudy Bellani, Ilia N. Karatsoreos, Nara Chhua, Mary Vernov, Cheryl D. Conrad and Bruce S. McEwen (2005). "Stress History and Pubertal Development Interact to Shape Hypothalamic-Pituitary-Adrenal Axis Plasticity" (http:/ / endo. endojournals. org/ cgi/ content/ short/ 147/ 4/ 1664). Endocrinology (The Endocrine Society) 147 (4): 1664–1674. doi:10.1210/en.2005-1432. PMID 16410296. . Retrieved 2007-10-16. [25] Swanson, L.W. (2000). "Cerebral Hemisphere Regulation of Motivated Behavior". Brain Research 886: 113-164. doi:10.1016/S0006-8993(00)02905-X. [26] Canteras, N.S. (2002). "The medial hypothalamic defensive system:Hodological organization and functional implications". Pharmacology, Biochemistry & Behavior 71: 481-491. doi:10.1016/S0091-3057(01)00685-2. [27] Ribeiro-Barbosa, E.R.; et al (2005). "An alternative experimental procedure for studying predator-related defensive responses.". Neuroscience & Biobehavioral Reviews 29 (8): 1255-1263. doi:10.1016/j.neubiorev.2005.04.006. [28] Cezário, A.F. (2008). "Hypothalamic sites responding to predator threats--the role of the dorsal premammillary nucleus in unconditioned and conditioned antipredatory defensive behavior.". European Journal of Neuroscience 28 (5): 1003-1015. doi:10.1111/j.1460-9568.2008.06392.x. [29] Blanchard, D.C. (2003). "Dorsal premammillary nucleus differentially modulates defensive behaviors induced by different threat stimuli in rats". Neuroscience Letters 345 (3): 145-148. doi:10.1016/S0304-3940(03)00415-4. [30] Canteras, N.S.; Swanson, L.W. (1992). "The dorsal premammillary nucleus: an unusual component of the mammillary body." (http:/ / www. pnas. org/ content/ 89/ 21/ 10089. long). PNAS 89 (21): 10089-10093. . [31] Behbehani, M.M. (1995). "Functional characteristics of the midbrain periaqueductal gray.". Progress in Neurobiology 46 (6): 575-605. doi:10.1016/0301-0082(95)00009-K. [32] Motta, S.C.; et al (2009). "Dissecting the brain's fear system reveals the hypothalamus is critical for responding in subordinate conspecific intruders." (http:/ / www. pnas. org/ content/ 106/ 12/ 4870. full. pdf+ html). PNAS 106 (12): 4870-4875. .

Added reference • de Vries, GJ, and Sodersten P (2009) Sex differences in the brain: the relation between structure and function. Hormones and Behavior 55:589-596.

External links • BrainMaps at UCDavis Hypothalamus (http://brainmaps.org/index.php?q=Hypothalamus) • The Hypothalamus and Pituitary at endotexts.org (http://www.endotext.org/neuroendo/neuroendo3b/ neuroendo3b.htm) • NIF Search - Hypothalamus (http://www.neuinfo.org/nif/nifgwt.html?query="Hypothalamus") via the Neuroscience Information Framework • Space-filling and cross-sectional diagrams of hypothalamic nuclei: right hypothalamus (http://www. netterimages.com/image/8535.htm), anterior (http://www.netterimages.com/image/8584.htm), tubular (http://www.netterimages.com/image/8586.htm), posterior (http://www.netterimages.com/image/8588. htm).

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Hippocampus Brain: Hippocampus

The hippocampus is located in the medial temporal lobe of the brain. In this lateral view of the human brain, the frontal lobe is at left, the occipital lobe at right, and the temporal and parietal lobes have largely been removed to reveal the hippocampus underneath. Part of

Temporal lobe

NeuroNames

hier-164

MeSH

Hippocampus

NeuroLex ID

birnlex_721

[1] [2]

[3]

The hippocampus is a major component of the brains of humans and other vertebrates. It belongs to the limbic system and plays important roles in the consolidation of information from short-term memory to long-term memory and spatial navigation. Humans and other mammals have two hippocampi, one in each side of the brain. The hippocampus is a part of the cerebral cortex, and in primates is located in the medial temporal lobe, underneath the cortical surface. It contains two main interlocking parts: Ammon's horn[4] and the dentate gyrus. In Alzheimer's disease, the hippocampus is one of the first regions of the brain to suffer damage; memory problems and disorientation appear among the first symptoms. Damage to the hippocampus can also result from oxygen starvation (hypoxia), encephalitis, or medial temporal lobe epilepsy. People with extensive, bilateral hippocampal damage may experience anterograde amnesia—the inability to form or retain new memories.

MRI coronal view of a hippocampus shown in red

In rodents, the hippocampus has been studied extensively as part of a brain system responsible for spatial memory and navigation. Many neurons in the rat and mouse hippocampus respond as place cells: that is, they fire bursts of action potentials when the animal passes through a specific part of its environment. Hippocampal place cells interact extensively with head direction cells, whose activity acts as an inertial compass, and with grid cells in the neighboring entorhinal cortex. Since different neuronal cell types are neatly organized into layers in the hippocampus, it has frequently been used as a model system for studying neurophysiology. The form of neural plasticity known as long-term potentiation (LTP) was first discovered to occur in the hippocampus and has often been studied in this structure. LTP is widely believed to be one of the main neural mechanisms by which memory is stored in the brain.

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Name The earliest description of the ridge running along the floor of the temporal horn of the lateral ventricle comes from the Venetian anatomist Julius Caesar Aranzi (1587), who initially likened it to a seahorse, using the Latin: hippocampus (from Greek: ἵππος, "horse" and Greek: κάμπος, "sea monster") or alternatively to a silkworm. The German anatomist Duvernoy (1729), the first to illustrate the structure, also wavered between "seahorse" and "silkworm." "Ram's horn" was proposed by the Danish anatomist Jacob Winsløw in 1732; and a decade later his fellow Parisian, the surgeon de Garengeot, used "cornu Ammonis" - horn of (the ancient Egyptian god) Amun.[5]

The Hungarian neuroscientist László Seress' 1980 preparation of the human hippocampus and fornix compared with a seahorse.

Another mythological reference appeared with the term pes hippocampi, which may date back to Diemerbroeck in 1672, introducing a comparison with the shape of the folded back forelimbs and webbed feet of the Classical hippocampus (Greek: ἱππόκαμπος), a sea monster with a horse's forequarters and a fish's tail. The hippocampus was then described as pes hippocampi major, with an adjacent bulge in the occipital horn, the calcar avis, being named pes hippocampi minor.[5] The renaming of the hippocampus as hippocampus major, and the calcar avis as hippocampus minor, has been attributed to Félix Vicq-d'Azyr systematising nomenclature of parts of the brain in 1786. Mayer mistakenly used the term hippopotamus in 1779, and was followed by some other authors until Karl Friedrich Burdach resolved this error in 1829. In 1861 the hippocampus minor became the centre of a dispute over human evolution between Thomas Henry Huxley and Richard Owen, satirised as the Great Hippocampus Question. The term hippocampus minor fell from use in anatomy textbooks, and was officially removed in the Nomina Anatomica of 1895.[6] Today, the structure is called the hippocampus rather than hippocampus major, with pes hippocampi often being regarded as synonymous with De Garengeot's "cornu Ammonis",[5] a term which survives in the names of the four main histological divisions of the hippocampus: CA1, CA2, CA3 and CA4.[7]

Functions Historically, the earliest widely held hypothesis was that the hippocampus is involved in olfaction. This idea was cast into doubt by a series of anatomical studies that did not find any direct projections to the hippocampus from the olfactory bulb.[8] However, later work did confirm that the olfactory bulb does project into the ventral part of the lateral entorhinal cortex, and field CA1 in the ventral hippocampus sends axons to the main olfactory bulb,[9] the anterior olfactory nucleus, and to the primary olfactory cortex. There continues to be some interest in hippocampal olfactory responses, particularly the role of the hippocampus in memory for odors, but few people believe today that olfaction is its primary function.[10][11] Over the years, three main ideas of hippocampal function have dominated the literature: inhibition, memory, and space. The behavioral inhibition theory (caricatured by O'Keefe and Nadel as "slam on the brakes!")[12] was very popular up to the 1960s. It derived much of its justification from two observations: first, that animals with hippocampal damage tend to be hyperactive; second, that animals with hippocampal damage often have difficulty learning to inhibit responses that they have previously been taught, especially if the response requires remaining quiet as in a passive avoidance test. Jeffrey Gray developed this line of thought into a full-fledged theory of the role of the hippocampus in anxiety.[13] The inhibition theory is currently the least popular of the three.[14] The second major line of thought relates the hippocampus to memory. Although it had historical precursors, this idea derived its main impetus from a famous report by Scoville and Brenda Milner[15] describing the results of surgical destruction of the hippocampus (in an attempt to relieve epileptic seizures), in Henry Molaison,[16] known until his

Hippocampus death in 2008 as patient H.M. The unexpected outcome of the surgery was severe anterograde and partial retrograde amnesia: Molaison was unable to form new episodic memories after his surgery and could not remember any events that occurred just before his surgery, but retained memories for things that happened years earlier, such as his childhood. This case produced such enormous interest that Molaison reportedly became the most intensively studied medical subject in history.[17] In the ensuing years, other patients with similar levels of hippocampal damage and amnesia (caused by accident or disease) have been studied as well, and thousands of experiments have studied the physiology of activity-driven changes in synaptic connections in the hippocampus. There is now almost universal agreement that the hippocampus plays some sort of important role in memory; however, the precise nature of this role remains widely debated.[18][19] The third important theory of hippocampal function relates the hippocampus to space. The spatial theory was originally championed by O'Keefe and Nadel, who were influenced by E.C. Tolman's theories about "cognitive maps" in humans and animals. O'Keefe and his student Dostrovsky in 1971 discovered neurons in the rat hippocampus that appeared to them to show activity related to the rat's location within its environment.[20] Despite skepticism from other investigators, O'Keefe and his co-workers, especially Lynn Nadel, continued to investigate this question, in a line of work that eventually led to their very influential 1978 book The Hippocampus as a Cognitive Map.[21] As with the memory theory, there is now almost universal agreement that spatial coding plays an important role in hippocampal function, but the details are widely debated.[22]

Role in memory Psychologists and neuroscientists generally agree that the hippocampus has an important role in the formation of new memories about experienced events (episodic or autobiographical memory).[19][23] Part of this role is hippocampal involvement in the detection of novel events, places and stimuli.[24] Some researchers view the hippocampus as part of a larger medial temporal lobe memory system responsible for general declarative memory (memories that can be explicitly verbalized—these would include, for example, memory for facts in addition to episodic memory).[18] Due to bilateral symmetry the brain has a hippocampus in both cerebral hemispheres, so every normal brain has two of them. If damage to the hippocampus occurs in only one hemisphere, leaving the structure intact in the other hemisphere, the brain can retain near-normal memory functioning.[25] Severe damage to the hippocampus in both hemispheres results in profound difficulties in forming new memories (anterograde amnesia), and often also affects memories formed before the damage (retrograde amnesia). Although the retrograde effect normally extends some years before the brain damage, in some cases older memories remain—this sparing of older memories leads to the idea that consolidation over time involves the transfer of memories out of the hippocampus to other parts of the brain.[26] Damage to the hippocampus does not affect some types of memory, such as the ability to learn new skills (playing a musical instrument, or solving certain types of puzzles, for example). This fact suggests that such abilities depend on different types of memory (procedural memory) and different brain regions. Furthermore, amnesic patients frequently show "implicit" memory for experiences even in the absence of conscious knowledge. For example, a patient asked to guess which of two faces they have seen most recently may give the correct answer the majority of the time, in spite of stating that they have never seen either of the faces before. Some researchers distinguish between conscious recollection, which depends on the hippocampus, and familiarity, which depends on portions of the medial temporal cortex.[27]

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Role in spatial memory and navigation Studies conducted on freely moving rats and mice have shown that many hippocampal neurons have "place fields", that is, they fire bursts of action potentials when a rat passes through a particular part of the environment. Evidence for place cells in primates is limited, perhaps in part because it is difficult to record brain activity from freely moving monkeys. Place-related hippocampal neural activity has been reported in monkeys moving around inside a room while seated in a restraint chair;[29] on the other hand, Edmund Rolls and his colleagues instead described hippocampal cells that fire in relation to the place a monkey is looking at, rather than the place its body is located.[30] In humans, cells with location-specific firing patterns have been reported in a study of patients with drug-resistant epilepsy who were undergoing an invasive procedure to localize the source of their seizures, with a view to surgical resection. The patients had diagnostic electrodes implanted in their hippocampus and then used a computer to move around in a virtual reality town.[31]

Spatial firing patterns of seven place cells recorded from a single electrode in the dorsal CA1 layer of a rat. The rat ran several hundred laps clockwise around an elevated triangular track, stopping in the middle of each arm to eat a small portion of food reward. Black dots indicate positions of the rat's head; colored dots indicate places where action potentials occurred, using a [28] different color for each cell.

Place responses in rats and mice have been studied in hundreds of experiments over four decades, yielding a large quantity of information.[22] Place cell responses are shown by pyramidal cells in the hippocampus proper, and granule cells in the dentate gyrus. These constitute the great majority of neurons in the densely packed hippocampal layers. Inhibitory interneurons, which make up most of the remaining cell population, frequently show significant place-related variations in firing rate, but much weaker than that shown by pyramidal or granule cells. There is little if any spatial topography in the representation: cells lying next to each other in the hippocampus generally have uncorrelated spatial firing patterns. Place cells are typically almost silent when a rat is moving around outside the place field, but reach sustained rates as high as 40 Hz when the rat is near the center. Neural activity sampled from 30–40 randomly chosen place cells carries enough information to allow a rat's location to be reconstructed with high confidence. The size of place fields varies in a gradient along the length of the hippocampus, with cells at the dorsal end showing the smallest fields, cells near the center showing larger fields, and cells at the ventral tip fields that cover the entire environment.[22] In some cases, the firing rate of rat hippocampal cells depends not only on place but also on the direction a rat is moving, the destination toward which it is traveling, or other task-related variables.[32] The discovery of place cells in the 1970s led to a theory that the hippocampus might act as a cognitive map—a neural representation of the layout of the environment.[33] Several lines of evidence support the hypothesis. It is a frequent observation that without a fully functional hippocampus, humans may not remember where they have been and how to get where they are going: getting lost is one of the most common symptoms of amnesia.[34] Studies with animals have shown that an intact hippocampus is required for initial learning and long-term retention of some spatial memory tasks, particularly ones that require finding the way to a hidden goal.[35][36][37][38] The "cognitive map hypothesis" has been further advanced by recent discoveries of head direction cells, grid cells, and border cells in several parts of the rodent brain that are strongly connected to the hippocampus.[22][39] Brain imaging shows that people have more active hippocampi when correctly navigating, as tested in a computer-simulated "virtual" navigation task.[40] Also, there is evidence that the hippocampus plays a role in finding shortcuts and new routes between familiar places. For example, London's taxi drivers must learn a large number of places and the most direct routes between them (they have to pass a strict test, The Knowledge, before being licensed to drive the famous black cabs). A study at University College London by Maguire, et al.. (2000)[41] showed that

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part of the hippocampus is larger in taxi drivers than in the general public, and that more experienced drivers have bigger hippocampi. Whether having a bigger hippocampus helps an individual to become a cab driver, or if finding shortcuts for a living makes an individual's hippocampus grow is yet to be elucidated. However, in that study Maguire, et al.. examined the correlation between size of the grey matter and length of time that had been spent as a taxi driver, and found a positive correlation between the length of time an individual had spent as a taxi driver and the volume of the right hippocampus. It was found that the total volume of the hippocampus remained constant, from the control group vs. taxi drivers. That is to say that the posterior portion of a taxi driver's hippocampus is indeed increased, but at the expense of the anterior portion. There have been no known detrimental effects reported from this disparity in hippocampal proportions.[41]

Anatomy Anatomically, the hippocampus is an elaboration of the edge of the cerebral cortex.[42] The structures that line the edge of the cortex make up the so-called limbic system (Latin limbus = border): these include the hippocampus, cingulate cortex, olfactory cortex, and amygdala. Paul MacLean once suggested, as part of his triune brain theory, that the limbic structures comprise the neural basis of emotion. Some neuroscientists no longer believe that the concept of a unified "limbic system" is valid, though.[43] However, the hippocampus is anatomically connected to parts of the brain that are involved with emotional behavior—the septum, the hypothalamic mammillary body, and the anterior nuclear complex in the thalamus so its role as a limbic structure cannot be completely dismissed.

Nissl-stained coronal section of the brain of a macaque monkey, showing hippocampus (circled). Source: brainmaps.org

The hippocampus as a whole has the shape of a curved tube, which has been analogized variously to a seahorse, a ram's horn (Cornu Ammonis, hence the subdivisions CA1 through CA4), or a banana.[42] It can be distinguished as a zone where the cortex narrows into a single layer of densely packed pyramidal neurons 3-6 cells deep in rats, which curl into a tight U shape; one edge of the "U," field CA4, is embedded into a backward facing strongly flexed V-shaped cortex, the dentate gyrus. It consists of ventral and dorsal portions, both of which share similar composition but are parts of different neural circuits.[44] This general layout holds across the full range of mammalian species, from hedgehog to human, although the details vary. In the rat, the two hippocampi resemble a pair of bananas, joined at the stems by the hippocampal commissure that crosses the midline under the anterior corpus callosum. In human or monkey brains, the portion of the hippocampus down at the bottom, near the base of the temporal lobe, is much broader than the part at the top. One of the consequences of this complex geometry is that cross-sections through the hippocampus can show a variety of shapes, depending on the angle and location of the cut.

Hippocampus

The entorhinal cortex (EC), located in the parahippocampal gyrus, is considered to be part of the hippocampal region because of its anatomical connections. The EC is strongly and reciprocally connected with many other parts of the cerebral cortex. In addition, the medial septal nucleus, the anterior nuclear complex and nucleus reuniens of the thalamus and the supramammillary nucleus of the hypothalamus, as well as the raphe nuclei Basic circuit of the hippocampus, as drawn by Santiago Ramon y Cajal. DG: and locus coeruleus in the brainstem send dentate gyrus. Sub: subiculum. EC: entorhinal cortex axons to the EC. The main output pathway (perforant path, first described by Ramon y Cajal) of EC axons comes from the large stellate pyramidal cells in layer II that "perforate" the subiculum and project densely to the granule cells in the dentate gyrus, apical dendrites of CA3 get a less dense projection, and the apical dendrites of CA1 get a sparse projection. Thus, the perforant path establishes the EC as the main "interface" between the hippocampus and other parts of the cerebral cortex. The dentate granule cell axons (called mossy fibers) pass on the information from the EC on thorny spines that exit from the proximal apical dendrite of CA3 pyramidal cells. Then, CA3 axons exit from the deep part of the cell body, and loop up into the region where the apical dendrites are located, then extend all the way back into the deep layers of the entorhinal cortex—the Shaffer collaterals completing the reciprocal circuit; field CA1 also sends axons back to the EC, but these are more sparse than the CA3 projection. Within the hippocampus, the flow of information from the EC is largely unidirectional, with signals propagating through a series of tightly packed cell layers, first to the dentate gyrus, then to the CA3 layer, then to the CA1 layer, then to the subiculum, then out of the hippocampus to the EC, mainly due to collateralization of the CA3 axons. Each of these layers also contains complex intrinsic circuitry and extensive longitudinal connections.[42] Several other connections play important roles in hippocampal function.[42] Beyond the output to the EC, additional output pathways go to other cortical areas including the prefrontal cortex. A very important large output goes to the lateral septal area and to the mammillary body of the hypothalamus. The hippocampus receives modulatory input from the serotonin, norepinephrine, and dopamine systems, and from nucleus reuniens of the thalamus to field CA1. A very important projection comes from the medial septal area, which sends cholinergic and GABAergic fibers to all parts of the hippocampus. The inputs from the septal area play a key role in controlling the physiological state of the hippocampus: destruction of the septal area abolishes the hippocampal theta rhythm, and severely impairs certain types of memory.[45] The cortical region adjacent to the hippocampus is known collectively as the parahippocampal gyrus (or parahippocampus).[46] It includes the EC and also the perirhinal cortex, which derives its name from the fact that it lies next to the rhinal sulcus. The perirhinal cortex plays an important role in visual recognition of complex objects, but there is also substantial evidence that it makes a contribution to memory which can be distinguished from the contribution of the hippocampus, and that complete amnesia occurs only when both the hippocampus and the parahippocampus are damaged.[46]

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Hippocampal Formation Various sections of the hippocampal formation are shown to be functionally and anatomically distinct. The dorsal (DH), ventral (VH), and intermediate regions of the hippocampal formation serve different functions, project with differing pathways, and have varying degrees of place field neurons (Fanselow & Dong, 2009). The dorsal region of the hippocampal formation serves for spatial memory, verbal memory, and learning of conceptual information. Using the radial arm maze Pothuizen et al. (2004), found lesions in the DH to cause spatial memory impairment while VH lesions did not. Its projecting pathways include the medial septal complex, and supramammillary nucleus. The dorsal hippocampal formation also has more place field neurons than both the ventral and intermediate hippocampal formation (Jung et al., 1994). The intermediate hippocampus has overlapping characteristics with both the ventral and dorsal hippocampus (Fanselow & Dong, 2009). Using PHAL anterograde tracing methods, Cenquizca and Swanson (2007) located the moderate projections to two primary olfactory cortical areas and prelimbic areas of the mPFC. This region has the least amount of place field neurons. The ventral hippocampus functions in fear conditioning and affective processes. Anagnostaras et al. (2002) showed that alterations to the ventral hippocampus reduced the amount of information sent to the amygdala by the dorsal and ventral hippocampus, consequentially altering fear conditioning in rats.

Physiology The hippocampus shows two major "modes" of activity, each associated with a distinct pattern of neural population activity and waves of electrical activity as measured by an electroencephalogram (EEG). These modes are named after the EEG patterns associated with them: theta and large irregular activity (LIA). The main characteristics described below are for the rat, which is the animal most extensively studied.[47] The theta mode appears during states of active, alert behavior (especially locomotion), and also during REM (dreaming) sleep.[48] In the theta mode, the EEG is dominated by large regular waves with a frequency range of 6–9 Hz, and the main groups of hippocampal neurons Examples of rat hippocampal EEG and CA1 neural activity in the theta (pyramidal cells and granule cells) show (awake/behaving) and LIA (slow-wave sleep) modes. Each plot show 20 seconds sparse population activity, which means that of data, with a hippocampal EEG trace at the top, spike rasters from 40 in any short time interval, the great majority simultaneously recorded CA1 pyramidal cells in the middle (each raster line of cells are silent, while the small remaining represents a different cell), and a plot of running speed at the bottom. The top plot represents a time period during which the rat was actively searching for scattered fraction fire at relatively high rates, up to 50 food pellets. For the bottom plot, the rat was asleep. spikes in one second for the most active of them. An active cell typically stays active for half a second to a few seconds. As the rat behaves, the active cells fall silent and new cells become active, but the overall percentage of active cells remains more or less constant. In many situations, cell activity is determined largely by the spatial location of the animal, but other behavioral variables also clearly influence it.

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Hippocampus The LIA mode appears during slow-wave (non-dreaming) sleep, and also during states of waking immobility, such as resting or eating.[48] In the LIA mode, the EEG is dominated by sharp waves, which are randomly timed large deflections of the EEG signal lasting for 200–300 ms. These sharp waves also determine the population neural activity patterns. Between them, pyramidal cells and granule cells are very quiet (but not silent). During a sharp wave, as many as 5–10% of the neural population may emit action potentials during a period of 50 ms; many of these cells emit bursts of several action potentials. These two hippocampal activity modes can be seen in primates as well as rats, with the exception that it has been difficult to see robust theta rhythmicity in the primate hippocampus. There are, however, qualitatively similar sharp waves, and similar state-dependent changes in neural population activity.[49]

Theta rhythm Because of its densely packed neural layers, the hippocampus generates some of the largest EEG signals of any brain structure. In some situations the EEG is dominated by regular waves at 3–10 Hz, often continuing for many seconds. These reflect subthreshold membrane potentials and strongly modulate the spiking of hippocampal neurons and synchronise across the hippocampus in a travelling wave pattern.[50] This EEG pattern is known as a theta rhythm.[51] Theta rhythmicity is very obvious in rabbits and rodents, and also clearly present in cats and dogs. Whether theta can be seen in primates is a vexing question.[52] In rats (the animals that have been the most extensively studied), theta is seen mainly in two conditions: first, when an animal is walking or in some other way actively interacting with its surroundings; second, during REM sleep.[53] The function of theta has not yet been convincingly explained, although numerous theories have been proposed.[47] The most popular hypothesis has been to relate it to learning and memory. For example, the phase with which theta at the time of stimulation of a neuron shapes the effect of that stimulation upon its synapses and therefore may affect learning and memory dependent upon synaptic plasticity.[54] It is well-established that lesions of the medial septum—the central node of the theta system—cause severe disruptions of memory. However, the medium septum is more than just the controller of theta, it is also the main source of cholinergic projections to the hippocampus.[42] It has not been established that septal lesions exert their effects specifically by eliminating the theta rhythm.[55]

Sharp waves During sleep, or during waking states when an animal is resting or otherwise not engaged with its surroundings, the hippocampal EEG shows a pattern of irregular slow waves, somewhat larger in amplitude than theta waves. This pattern is occasionally interrupted by large surges called sharp waves.[56] These events are associated with bursts of spike activity, lasting 50–100 msec, in pyramidal cells of CA3 and CA1. They are also associated with short-lasting high-frequency EEG oscillations called "ripples", with frequencies in the range 150–200 Hz in rats. Sharp waves are most frequent during sleep, when they occur at an average rate around 1 per second (in rats), but in a very irregular temporal pattern. Sharp waves are less frequent during inactive waking states, and are usually smaller. Sharp waves have also been observed in humans and monkeys. In macaques, sharp waves are robust, but do not occur as frequently as in rats.[49] One of the most interesting aspects of sharp waves is that they appear to be associated with memory. Wilson and McNaughton 1994,[57] and numerous later studies, reported that when hippocampal place cells have overlapping spatial firing fields (and therefore often fire in near-simultaneity), they tend to show correlated activity during sleep following the behavioral session. This enhancement of correlation, commonly known as reactivation, has been found to occur mainly during sharp waves.[58] It has been proposed that sharp waves are, in fact, reactivations of neural activity patterns that were memorized during behavior, driven by strengthening of synaptic connections within the hippocampus.[59] This idea forms a key component of the "two-stage memory" theory, advocated by Buzsáki and others, which proposes that memories are stored within the hippocampus during behavior, and then later transferred to the neocortex during sleep: sharp waves are suggested to drive Hebbian synaptic changes in the neocortical targets

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Hippocampus of hippocampal output pathways.[60]

Long-term potentiation Since at least the time of Ramon y Cajal, psychologists have speculated that the brain stores memory by altering the strength of connections between neurons that are simultaneously active.[61] This idea was formalized by Donald Hebb in 1948,[62] but for many years thereafter, attempts to find a brain mechanism for such changes failed. In 1973, Tim Bliss and Terje Lømo described a phenomenon in the rabbit hippocampus that appeared to meet Hebb's specifications: a change in synaptic responsiveness induced by brief strong activation and lasting for hours, days, or longer.[63] This phenomenon was soon referred to as long-term potentiation, abbreviated LTP. As a candidate mechanism for memory, LTP has since been studied intensively, and a great deal has been learned about it. The hippocampus is a particularly favorable site for studying LTP because of its densely packed and sharply defined layers of neurons, but similar types of activity-dependent synaptic change have now been observed in many other brain areas.[64] The best-studied form of LTP occurs at synapses that terminate on dendritic spines and use the transmitter glutamate. Several of the major pathways within the hippocampus fit this description, and show LTP.[65] The synaptic changes depend on a special type of glutamate receptor, the NMDA receptor, which has the special property of allowing calcium to enter the postsynaptic spine only when presynaptic activation and postsynaptic depolarization occur at the same time.[66] Drugs that interfere with NMDA receptors block LTP and also have major effects on some types of memory, especially spatial memory. Transgenic mice, genetically modified in ways that disable the LTP mechanism, also generally show severe memory deficits.[66]

Pathology Aging Age-related conditions such as Alzheimer's disease (for which hippocampal disruption is one of the earliest signs[67]) have a severe impact on many types of cognition, but even normal aging is associated with a gradual decline in some types of memory, including episodic memory and working memory. Because the hippocampus is thought to play a central role in memory, there has been considerable interest in the possibility that age-related declines could be caused by hippocampal deterioration.[68] Some early studies reported substantial loss of neurons in the hippocampus of elderly people, but later studies using more precise techniques found only minimal differences.[68] Similarly, some MRI studies have reported shrinkage of the hippocampus in elderly people, but other studies have failed to reproduce this finding. There is, however, a reliable relationship between the size of the hippocampus and memory performance—meaning that not all elderly people show hippocampal shrinkage, but those who do tend to perform less well on some memory tasks.[69] There are also reports that memory tasks tend to produce less hippocampal activation in elderly than in young subjects.[69] Furthermore, a randomized-control study published in 2011 found that aerobic exercise could increase the size of the hippocampus in adults aged 55 to 80 and also improve spatial memory.[70] In rats, where detailed studies of cellular physiology are possible, aging does not cause substantial cell loss in the hippocampus, but it alters synaptic connectivity in several ways.[71] Functional synapses are lost in the dentate gyrus and CA1 region, and NMDA receptor-mediated responses are reduced. These changes may account for deficits in induction and maintenance of long-term potentiation, a form of synaptic plasticity that has been implicated in memory. There are also age-related declines in hippocampal expression of several genes associated with synaptic plasticity.[72] Finally, there are differences in the stability of "place cell" representations. In young rats, the arrangement of place fields is usually altered if the rat is moved into a different environment, but remains the same if a rat is returned to an environment it has visited previously. In aged rats, the place fields frequently fail to "remap" when a rat is moved to a different environment, and also frequently fail to restore the original "map" when the rat is returned to the same environment.

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Hippocampus In a 2012 study, led by neuroscientists at the University of Bristol, it was discovered that a cellular mechanism known as sodium channels was playing a direct role in the changing of activity of neurons, leading to a cognitive decline of the human brain. In the study, after researchers recorded electrical signals known as action potentials in single cells of the hippocampus region, it was discovered that it became difficult for an aged brain to make hippocampal neurons to generate action potentials. The reasoning for this was due to changes to the activation properties of membrane proteins called sodium channels. These proteins would then intervene the rapid upstroke of an action potential, thus allowing a flow of sodium ions into neurons.[73] Another biological influence found to have an effect on how the hippocampus ages is the Apoloprotein E epsilon 4 (APOE-ε4) allele. This allele has been associated with deterioration of the hippocampal region of the brain at various points in development and aging. [74] However, it seems that the allele does not affect everyone in the same way. Bohdi et al. [75] found that the allele is more common in men than in women. Of the 24 men participating in the study, 11 carried the allele. In contrast, only 6 of the 28 women in the study had the APOE-ε4 allele. Although it is found more in men, APOE-ε4 does not affect them to the extent that it does for women. [76] The study indicates that the allele might be connected to greater deleterious effects on the volume of the hippocampus in women than in men, predisposing women more strongly to dementia in older age.

Stress The hippocampus contains high levels of glucocorticoid receptors, which make it more vulnerable to long-term stress than most other brain areas.[77] Stress-related steroids affect the hippocampus in at least three ways: first, by reducing the excitability of some hippocampal neurons; second, by inhibiting the genesis of new neurons in the dentate gyrus; third, by causing atrophy of dendrites in pyramidal cells of the CA3 region. There is evidence that humans who have experienced severe, long-lasting traumatic stress, show atrophy of the hippocampus, more than of other parts of the brain.[78] These effects show up in post-traumatic stress disorder,[79] and they may contribute to the hippocampal atrophy reported in schizophrenia[80] and severe depression.[81] A recent study has also revealed atrophy as a result of depression, but this can be stopped with anti-depressants, even if they are not effective in relieving other symptoms.[82] Hippocampal atrophy is also frequently seen in Cushing's syndrome, a disorder caused by high levels of cortisol in the bloodstream. At least some of these effects appear to be reversible if the stress is discontinued. There is, however, evidence mainly derived from studies using rats that stress shortly after birth can affect hippocampal function in ways that persist throughout life.[83] Sex specific responses to stress have also been demonstrated to have an effect on the hippocampus. Recent research has revealed a difference between the response of adult male and female rats to acute stress. When adult male rats were subjected to acute stressors their hippocampal neurons became bushier (i.e. the number of dendrites per neuron increased). In adult female rats the opposite occurred (i.e. the number of dendrites per hippocampal neuron decreased). This study suggests that adult male rats are better able to cope with acute stress than are adult female rats.[84] Research on chronic stress, however, has shown a different sex specific response. During situations in which adult male and female rats were exposed to chronic stress, the females were shown to be better able to cope. This was demonstrated by assessing how vulnerable to killing by a neurotoxin the neurons of the hippocampus were. The male rats' hippocampal neurons showed increased susceptibility after exposure to chronic stress where as the adult female rats' hippocampal neurons remained less affected by the neurotoxin. While the mechanisms that allow for this neuronal protection are unclear it has been postulated that sex specific hormones may play a role.[85]

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Epilepsy The hippocampus is often the focus of epileptic seizures: hippocampal sclerosis is the most commonly visible type of tissue damage in temporal lobe epilepsy.[86] It is not yet clear, though, whether the epilepsy is usually caused by hippocampal abnormalities, or the hippocampus is damaged by cumulative effects of seizures.[87] In experimental settings where repetitive seizures are artificially induced in animals, hippocampal damage is a frequent result: this may be a consequence of the hippocampus being one of the most electrically excitable parts of the brain. It may also have something to do with the fact that the hippocampus is one of very few brain regions where new neurons continue to be created throughout life.[88]

Schizophrenia The causes of schizophrenia are not at all well understood, but numerous abnormalities of brain structure have been reported. The most thoroughly investigated alterations involve the cerebral cortex, but effects on the hippocampus have also been described. Many reports have found reductions in the size of the hippocampus in schizophrenic subjects.[89] The changes probably result from altered development rather than tissue damage, and show up even in subjects who have never been medicated. Several lines of evidence implicate changes in synaptic organization and connectivity.[89] It is unclear whether hippocampal alterations play any role in causing the psychotic symptoms that are the most important feature of schizophrenia. Anthony Grace and his co-workers have suggested, on the basis of experimental work using animals, that hippocampal dysfunction might produce an alteration of dopamine release in the basal ganglia, thereby indirectly affecting the integration of information in the prefrontal cortex.[90] Others have suggested that hippocampal dysfunction might account for disturbances in long term memory frequently observed in people with schizophrenia.[91]

Transient global amnesia A current hypothesis as to one cause of transient global amnesia—a dramatic sudden temporary near-total loss of short-term memory—is that it may be due to venous congestion of the brain,[92] leading to ischemia of structures, such as the hippocampus, that are involved with memory.[93]

Evolution The hippocampus has a generally similar appearance across the range of mammal species, from monotremes such as the echidna to primates such as humans.[94] The hippocampal-size-to-body-size ratio broadly increases, being about twice as large for primates as for the echidna. It does not, however, increase at anywhere close to the rate of the neocortex-to-body-size ratio. Therefore, the hippocampus takes up a much larger fraction of the cortical mantle in rodents than in primates. In adult humans, the volume of the hippocampus on each side of the brain is about 3–3.5 cm3, as compared to 320–420 cm3 for the volume of the neocortex.[95] There is also a general relationship between the size of the hippocampus and spatial memory. When comparisons are made between similar species, those that have a greater capacity for spatial memory tend to have larger hippocampal volumes.[96] This relationship also extends to sex differences: in species where males and females show strong differences in spatial memory ability, they also tend to show corresponding differences in hippocampal volume.[97] Non-mammalian species do not have a brain structure that looks like the mammalian hippocampus, but they have one that is considered homologous to it. The hippocampus, as pointed out above, is essentially the medial edge of the cortex. Only mammals have a fully developed cortex, but the structure it evolved from, called the pallium, is present in all vertebrates, even the most primitive ones such as the lamprey or hagfish.[98] The pallium is usually divided into three zones: medial, lateral, and dorsal. The medial pallium forms the precursor of the hippocampus. It does not resemble the hippocampus visually, because the layers are not warped into an S shape or enwrapped by the dentate gyrus, but the homology is indicated by strong chemical and functional affinities. There is now evidence that these

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Hippocampus hippocampal-like structures are involved in spatial cognition in birds, reptiles, and fish.[99] In birds, the correspondence is sufficiently well established that most anatomists refer to the medial pallial zone as the "avian hippocampus".[100] Numerous species of birds have strong spatial skills, particularly those that cache food. There is evidence that food-caching birds have a larger hippocampus than other types of birds, and that damage to the hippocampus causes impairments in spatial memory.[101] The story for fish is more complex. In teleost fish (which make up the great majority of existing species), the forebrain is distorted in comparison to other types of vertebrates: most neuroanatomists believe that the teleost forebrain is essentially everted, like a sock turned inside-out, so that structures that lie in the interior, next to the ventricles, for most vertebrates, are found on the outside in teleost fish, and vice versa.[102] One of the consequences of this is that the medial pallium ("hippocampal" zone) of a typical vertebrate is thought to correspond to the lateral pallium of a typical fish. Several types of fish (particularly goldfish) have been shown experimentally to have strong spatial memory abilities, even forming "cognitive maps" of the areas they inhabit.[96] There is evidence that damage to the lateral pallium impairs spatial memory.[103][104] Thus, the role of the hippocampal region in navigation appears to begin far back in vertebrate evolution, predating splits that occurred hundreds of millions of years ago.[105] It is not yet known whether the medial pallium plays a similar role in even more primitive vertebrates, such as sharks and rays, or even lampreys and hagfish. Some types of insects, and molluscs such as the octopus, also have strong spatial learning and navigation abilities, but these appear to work differently from the mammalian spatial system, so there is as yet no good reason to think that they have a common evolutionary origin; nor is there sufficient similarity in brain structure to enable anything resembling a "hippocampus" to be identified in these species. Some have proposed, though, that the insect's mushroom bodies may have a function similar to that of the hippocampus.[106]

Notes [1] http:/ / braininfo. rprc. washington. edu/ Scripts/ hiercentraldirectory. aspx?ID=164 [2] http:/ / www. nlm. nih. gov/ cgi/ mesh/ 2007/ MB_cgi?mode=& term=Hippocampus [3] http:/ / www. neurolex. org/ wiki/ birnlex_721 [4] Pearce, 2001 [5] Duvernoy, 2005 [6] Gross, 1993 [7] Wechsler, 2004 [8] Finger, p. 183 [9] cite pmid 690266 [10] Eichenbaum et al., 1991 [11] Vanderwolf, 2001 [12] Nadel et al., 1975 [13] Gray and McNaughton, 2000 [14] Best & White, 1999 [15] Scoville and Milner, 1957 [16] N.Y. Times, 12-06-2008 [17] Squire, 2009 [18] Squire, 1992 [19] Eichenbaum and Cohen, 1993 [20] O'Keefe and Dostrovsky, 1971 [21] O'Keefe and Nadel, 1978 [22] Moser et al., 2008 [23] Squire and Schacter, 2002 [24] VanElzakker et al., 2008 [25] Di Gennaro G, Grammaldo LG, Quarato PP, Esposito V, Mascia A, Sparano A, Meldolesi GN, Picardi A. Severe amnesia following bilateral medial temporal lobe damage occurring on two distinct occasions. Neurol Sci. 2006 Jun;27(2):129–33. [26] Squire and Schacter, 2002, Ch. 1 [27] Diana et al., 2007 [28] Skaggs et al., 1996

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Matsumara et al., 1999 Rolls and Xiang, 2006 Ekstrom et al., 2003 Smith and Mizumori, 2006 O'Keefe and Nadel Chiu et al., 2004 Morris et al., 1982 Sutherland et al., 1982 Sutherland et al., 2001 Clark et al., 2005 Solstad et al., 2008 Maguire et al., 1998 Maguire et al., 2000 Amaral and Lavenex, 2006 Kötter & Stephan, 1997 Moser and Moser, 1998 Winson, 1978 Eichenbaum et al, 2007 Buzsáki, 2006 Buzsáki et al., 1990 Skaggs et al., 2007 Lubenov & Siapas, 2009

[51] Buzsáki, 2002 [52] Cantero et al., 2003 [53] Vanderwolf, 1969 [54] Huerta & Lisman, 1993 [55] Kahana et al., 2001 [56] Buzsáki, 1986 [57] Wilson & McNaughton, 1994 [58] Jackson et al., 2006 [59] Sutherland & McNaughton, 2000 [60] Buzsáki, 1989 [61] Ramon y Cajal, 1894 [62] Hebb, 1948 [63] Bliss & Lømo, 1973 [64] Cooke & Bliss, 2006 [65] Malenka & Bear, 2004 [66] Nakazawa et al., 2004 [67] Hampel et al., 2008 [68] Prull et al., 2000, p. 105 [69] Prull et al., 2000, p. 107 [70] Erickson et al., 2011 [71] Rosenzweig & Barnes, 2003 [72] Burke & Barnes, 2006 [73] Randall, A.; Booth, C.; Brown, j. (2012). "Age-related changes to Na+ channel gating contribute to modified intrinsic neuronal excitability". Neurobiology of Aging. doi:10.1016/j.neurobiolaging.2011.12.030. [74] Schuff, N.; Woemer, N.; Boreta, L.; Kornfield, T.; Shaw, L.M.; Trojanowski, J.Q.; Thompson, P.M.; JackJr, C.R. et al. (2009). "MRI of hippocampal volume loss in elderly Alzheimer’s disease in relation to ApoE genotype and biomarkers". Brain: A Journal of Neurobiology. [75] Bondi, M.W.; Salmon, D.P.; Monsch, A.U.; Galasko, D.; Butters, N.; Klauber, M.R.; Thal, L.J.; Saitoh, T. (1995). "Episodic memory changes are associated with the APOE-epsilon 4 allele in nondemented older adults". Neurology., [76] Azad, N.A.; Al Bugami, M.; Loy-English, I. (2007). "Gender differences in dementia risk factors". Gender Medicine. [77] Joels, 2008 [78] Fu et al, 2010 [79] Karl A, Schaefer M, Malta LS, Dörfel D, Rohleder N, Werner A. (2006). "A meta-analysis of structural brain abnormalities in PTSD." (http:/ / www. sciencedirect. com/ science/ article/ pii/ S0149763406000285). Neurosci Biobehav Rev. 30 (7): 1004–31. doi:10.1016/j.neubiorev.2006.03.004. PMID 16730374. . [80] Wright IC, Rabe-Hesketh S, Woodruff PW, David AS, Murray RM, Bullmore ET. (2000). "Meta-analysis of regional brain volumes in schizophrenia." (http:/ / ajp. psychiatryonline. org/ cgi/ content/ full/ 157/ 1/ 16). Am J Psychiatry 157 (1): 16–25. PMID 10618008. .

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Hippocampus [81] Kempton MJ, Salvador Z, Munafò MR, Geddes JR, Simmons A, Frangou S, Williams SC. (2011). "Structural Neuroimaging Studies in Major Depressive Disorder: Meta-analysis and Comparison With Bipolar Disorder" (http:/ / archpsyc. ama-assn. org/ cgi/ content/ full/ 68/ 7/ 675). Arch Gen Psychiatry 68 (7): 675–90. doi:10.1001/archgenpsychiatry.2011.60. PMID 21727252. . see also MRI database at www.depressiondatabase.org (http:/ / sites. google. com/ site/ depressiondatabase/ ) [82] Campbell & MacQueen, 2004 [83] Garcia-Segura, pp. 170–71 [84] Shors, T.J. 2001. Acute stress rapidly and persistently enhances memory formation in the male rat. Neurobiol. Learn. Mem. 75: 10–29. [85] Conrad C. D. (2008). Chronic stress-induced hippocampal vulnerability: the glucocorticoid vulnerability hypothesis. Rev. Neurosci. 19, 395–411. [86] Chang and Lowenstein, 2003 [87] Sloviter, 2005 [88] Kuruba et al., 2009 [89] Harrison, 2004 [90] Goto & Grace, 2008 [91] Boyer et al., 2007 [92] Lewis S (1998). "Aetiology of transient global amnesia". The Lancet 352 (9125): 397–9. doi:10.1016/S0140-6736(98)01442-1. PMID 9717945. [93] Chung C.-P., Hsu HY, Chao AC, Chang FC, Sheng WY, Hu HH (2006). "Detection of intracranial venous reflux in patients of transient global amnesia". Neurology 66 (12): 1873–77. doi:10.1212/01.wnl.0000219620.69618.9d. PMID 16801653. [94] West, 1990 [95] Suzuki et al, 2005 [96] Jacobs, 2003 [97] Jacobs et al., 1990 [98] Aboitiz et al., 2003 [99] Rodríguez et al., 2002 [100] Colombo and Broadbent, 2000 [101] Shettleworth, 2003 [102] Nieuwenhuys, 1982 [103] Portavella et al., 2002 [104] Vargas et al., 2006 [105] Broglio et al., 2005 [106] Mizunami et al., 1998

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Further reading Journals • Hippocampus (http://www.wiley.com/WileyCDA/WileyTitle/productCd-HIPO.html) (Wiley)

Books • Per Andersen, Richard Morris, David Amaral, Tim Bliss and John O'Keefe, ed. (2007). The Hippocampus Book. Oxford University Press. ISBN 978-0-19-510027-3. • Henri M. Duvernoy, F. Cattin (2005). The Human Hippocampus: Functional Anatomy, Vascularization, and Serial Sections with MRI. Springer. ISBN 978-3-540-23191-2. • Howard Eichenbaum (2002). The Cognitive Neuroscience of Memory. Oxford University Press US. ISBN 978-0-19-514175-7. • edited by Patricia E. Sharp. (2002). Patricia E. Sharp. ed. The Neural Basis of Navigation: Evidence from Single Cell Recording. Springer. ISBN 978-0-7923-7579-1. • Philippe Taupin (2007). The Hippocampus: Neurotransmission and Plasticity in the Nervous System. Nova Publishers. ISBN 978-1-60021-914-6. • John H Byrne, ed. (2008). Learning and Memory: A comprehensive reference. Elsevier. ISBN 978-0-12-370509-9.

External links • BrainMaps at UCDavis hippocampus (http://brainmaps.org/index.php?q=hippocampus) • Diagram of a Hippocampal Brain Slice (http://www.stanford.edu/group/maciverlab/hippocampal.html) • Hippocampus – Cell Centered Database (http://ccdb.ucsd.edu/sand/main?stype=lite& keyword=hippocampus&Submit=Go&event=display&start=1) • Temporal-lobe.com An interactive diagram of the rat parahippocampal-hippocampal region (http://www. temporal-lobe.com) • NIF Search – Hippocampus (http://www.neuinfo.org/nif/nifgwt.html?query="Hippocampus") via the Neuroscience Information Framework • Search Hippocampus on BrainNavigator (http://www.brainnav.com/browse?highlight=8d89b5&specid=2) via BrainNavigator • Gyorgy Buzsaki (2010) Hippocampus. Scholarpedia. 6(1):1468. (http://www.scholarpedia.org/article/ Hippocampus)

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Neural oscillation Neural oscillation is rhythmic or repetitive neural activity in the central nervous system. Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms localized within individual neurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations in membrane potential or as rhythmic patterns of action potentials, which then produce oscillatory activation of post-synaptic neurons. At the level of neural ensembles, synchronized activity of large numbers of neurons can give rise to macroscopic oscillations, which can be observed in the electroencephalogram (EEG). Oscillatory activity in groups of neurons generally arise from feedback connections between the neurons that result in the synchronization of their firing patterns. The interaction between neurons can give rise to oscillations at a different frequency than the firing frequency of individual neurons. A well-known example of macroscopic neural oscillations is alpha activity. Neural oscillations were observed by researchers as early as Hans Berger, but their functional role is still not fully understood. The possible roles of neural oscillations include feature binding, information transfer mechanisms and the generation of rhythmic motor output. Over the last decades more insight has been gained, especially with advances in brain imaging. A major area of research in neuroscience involves determining how oscillations are generated and what their roles are. Oscillatory activity in the brain is widely observed at different levels of observation and is thought to play a key role in processing neural information. Numerous experimental studies indeed support a functional role of neural oscillations; a unified interpretation, however, is still lacking.

Overview Neural oscillations are observed throughout the central nervous system and at all levels, e.g., spike trains, local field potentials and large-scale oscillations which can be measured by electroencephalography. In general, oscillations can be characterized by their frequency, amplitude and phase. These signal properties can be extracted from neural recordings using time-frequency analysis. In large-scale oscillations, amplitude changes are considered to result from changes in synchronization within a neural ensemble, also referred to as local synchronization. In addition to local Simulation of neural oscillations at 10 Hz. Upper panel shows spiking of individual synchronization, oscillatory activity of neurons (with each dot representing an individual action potential within the population distant neural structures (single of neurons), and the lower panel the local field potential reflecting their summed activity. Figure illustrates how synchronized patterns of action potentials may result in neurons or neural ensembles) can macroscopic oscillations that can be measured outside the scalp. synchronize. Neural oscillations and synchronization have been linked to many cognitive functions such as information transfer, perception, motor control and memory.[1][2][3] Neural oscillations have been most widely studied in neural activity generated by large groups of neurons. Large-scale activity can be measured by techniques such as electroencephalography (EEG). In general, EEG signals have a broad spectral content similar to pink noise, but also reveal oscillatory activity in specific frequency bands.

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The first discovered and best-known frequency band is alpha activity (8–12 Hz) that can be detected from the occipital lobe during relaxed wakefulness and increases when the eyes are closed.[4] Other frequency bands are: delta (1–4 Hz), theta (4–8 Hz), beta (13–30 Hz) and gamma (30–70 Hz) frequency band, where faster rhythms such as gamma activity have been linked to cognitive processing. Indeed, EEG signals change dramatically during sleep and show a transition from faster frequencies to increasingly slower frequencies such as alpha waves. In fact, different sleep stages are commonly characterized by their spectral content.[5] Consequently, neural oscillations have been linked to cognitive states, such as awareness and consciousness.[6][7] Although neural oscillations in human brain activity are mostly investigated using EEG recordings, they are also observed using more invasive recording techniques such as single-unit recordings. Neurons can generate rhythmic patterns of action potentials or spikes. Some types of neurons have the tendency to fire at particular frequencies, so-called resonators.[8] Bursting is another form of rhythmic spiking. Spiking patterns are considered fundamental for information coding in the brain. Oscillatory activity can also be observed in the form of subthreshold membrane potential oscillations (i.e. in the absence of action potentials).[9] If numerous neuron spike in synchrony, they can give rise to oscillations in local field potentials (LFPs). Quantitative models can estimate the strength of neural oscillation in recorded data.[10] Neural oscillations are commonly studied from a mathematical framework and belong to the field of “neurodynamics”, an area of research in the cognitive sciences that places a strong focus upon the dynamic character of neural activity in describing brain function.[11] It considers the brain a dynamical system and uses differential equations to describe how neural activity evolves over time. In particular, it aims to relate dynamic patterns of brain activity to cognitive functions such as perception and memory. In very abstract form, neural oscillations can be analyzed analytically. When studied in a more physiologically realistic setting, oscillatory activity is generally studied using computer simulations of a computational model. The functions of neural oscillations are wide ranging and vary for different types of oscillatory activity. Examples are the generation of rhythmic activity such as a heartbeat and the neural binding of sensory features in perception, such as the shape and color of an object. Neural oscillations also play an important role in many neurological disorders, such as excessive synchronization during seizure activity in epilepsy or tremor in patients with Parkinson's disease. Oscillatory activity can also be used to control external devices in brain-computer interfaces, in which subjects can control an external device by changing the amplitude of particular brain rhythmics.

Physiology Oscillatory activity is observed throughout the central nervous system at all levels of organization. Three different levels have been widely recognized: the micro-scale (activity of a single neuron), the meso-scale (activity of a local group of neurons) and the macro-scale (activity of different brain regions).[12]

Microscopic Neurons generate action potentials resulting from changes in the electric membrane potential. Neurons can generate multiple action potentials in sequence forming so-called spike trains. These spike trains are the basis for neural coding and information transfer in the brain. Spike trains can form all kinds of patterns, such as rhythmic spiking and bursting, and often display oscillatory activity.[13] Oscillatory activity in single neurons can also be observed in sub-threshold fluctuations in membrane potential. These rhythmic changes in membrane potential do not reach the critical threshold and

Tonic firing pattern of single neuron showing rhythmic spiking activity

Neural oscillation therefore do not result in an action potential. They can result from postsynaptic potentials from synchronous inputs or from intrinsic properties of neurons. Neuronal spiking can be classified by their activity patterns. The excitability of neurons can be subdivided in Class I and II. Class I neurons can generate action potentials with arbitrarily low frequency depending on the input strength, whereas Class II neurons generate action potentials in a certain frequency band, which is relatively insensitive to changes in input strength.[8] Class II neurons are also more prone to display sub-threshold oscillations in membrane potential.

Mesoscopic A group of neurons can also generate oscillatory activity. Through synaptic interactions the firing patterns of different neurons may become synchronized and the rhythmic changes in electric potential caused by their action potentials will add up (constructive interference). That is, synchronized firing patterns result in synchronised input into other cortical areas, which gives rise to large-amplitude oscillations of the local field potential. These large-scale oscillations can also be measured outside the scalp using electroencephalography and magnetoencephalography. The electric potentials generated by single neurons are far too small to be picked outside the scalp and EEG or MEG activity always reflects the summation of the synchronous activity of thousands or millions of neurons that have similar spatial orientation.[14] Neurons in a neural ensemble rarely all fire at exactly the same moment, i.e. fully synchronized. Instead, the probability of firing is rhythmically modulated such that neurons are more likely to fire at the same time, which gives rise to oscillations in their mean activity (see figure at top of page). As such, the frequency of large-scale oscillations does not need to match the firing pattern of individual neurons. Isolated cortical neurons fire regularly under certain conditions, but in the intact brain cortical cells are bombarded by highly fluctuating synaptic inputs and typically fire seemingly random. However, if the probability of a large group of neurons is rhythmically modulated at a common frequency, they will generate oscillations in the mean field (see also figure at top of page).[13] Neural ensembles can generate oscillatory activity endogenously through local interactions between excitatory and inhibitory neurons. In particular, inhibitory interneurons play an important role in producing neural ensemble synchrony by generating a narrow window for effective excitation and rhythmically modulating the firing rate of excitatory neurons.[15]

Macroscopic Neural oscillation can also arise from interactions between different brain areas. Time delays play an important role here. Because all brain areas are bidirectionally coupled, these connections between brain areas form feedback loops. Positive feedback loops tends to cause oscillatory activity which frequency is inversely related to the delay time. An example of such a feedback loop is the connections between the thalamus and cortex. This thalamocortical network is able to generate oscillatory activity known as recurrent thalamo-cortical resonance.[16] The thalamocortical network plays an important role in the generation of alpha activity.[17][18]

Mechanisms Neuronal properties Scientists have identified some intrinsic neuronal properties that play an important role in generating membrane potential oscillations. In particular, voltage-gated ion channels are critical in the generation of action potentials. The dynamics of these ion channels have been captured in the well-established Hodgkin-Huxley model that describes how action potentials are initiated and propagated by means of a set of differential equations. Using bifurcation analysis, different oscillatory varieties of these neuronal models can be determined, allowing for the classification of types of neuronal responses. The oscillatory dynamics of neuronal spiking as identified in the Hodgkin-Huxley model closely agree with empirical findings. In addition to periodic spiking, subthreshold membrane potential

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Neural oscillation oscillations, i.e. resonance behavior that does not result in action potentials, may also contribute to oscillatory activity by facilitating synchronous activity of neighboring neurons.[19][20] Like pacemaker neurons in central pattern generators, subtypes of cortical cells fire bursts of spikes (brief clusters of spikes) rhythmically at preferred frequencies. Bursting neurons have the potential to serve as pacemakers for synchronous network oscillations, and bursts of spikes may underlie or enhance neuronal resonance.[13]

Network properties Apart from intrinsic properties of neurons, network properties are also an important source of oscillatory activity. Neurons communicate with one another via synapses and affect the timing of spike trains in the post-synaptic neurons. Depending on the properties of the connection, such as the coupling strength, time delay and whether coupling is excitatory or inhibitory, the spike trains of the interacting neurons may become synchronized.[21] Neurons are locally connected, forming small clusters that are called neural ensembles. Certain network structures promote oscillatory activity at specific frequencies. For example, neuronal activity generated by two populations of interconnected inhibitory and excitatory cells can show spontaneous oscillations that are described by the Wilson-Cowan model. If a group of neurons engages in synchronized oscillatory activity, the neural ensemble can be mathematically represented as a single oscillator.[12] Different neural ensembles are coupled through long-range connections and form a network of weakly coupled oscillators at the next spatial scale. Weakly coupled oscillators can generate a range of dynamics including oscillatory activity.[22] Long-range connections between different brain structures, such as the thalamus and the cortex (see thalamocortical oscillation), involve time-delays due to the finite conduction velocity of axons. Because most connections are reciprocal, they form feed-back loops that support oscillatory activity. Oscillations recorded from multiple cortical areas can become synchronized and form a large-scale network, whose dynamics and functional connectivity can be studied by means of spectral analysis and Granger causality measures.[23] Coherent activity of large-scale brain activity may form dynamic links between brain areas required for the integration of distributed information.[7]

Neuromodulation In addition to fast direct synaptic interactions between neurons forming a network, oscillatory activity is modulated by neurotransmitters on a much slower time scale. That is, the concentration levels of certain neurotransmitters are known to regulate the amount of oscillatory activity. For instance, GABA concentration has been shown to be positively correlated with frequency of oscillations in induced stimuli.[24] A number of nuclei in the brainstem have diffuse projections throughout the brain influencing concentration levels of neurotransmitters such as norepinephrine, acetylcholine and serotonin. These neurotransmitter systems affect the physiological state, e.g., wakefulness or arousal, and have a pronounced effect on amplitude of different brain waves, such as alpha activity.[25]

Mathematical description Oscillations can often be described and analyzed using mathematics. Mathematicians have identified several dynamical mechanisms that generate rhythmicity. Among the most important are harmonic (linear) oscillators, limit cycle oscillators, and delayed-feedback oscillators.[26] Harmonic oscillations appear very frequently in nature—examples are sound waves, the motion of a pendulum, and vibrations of every sort. They generally arise when a physical system is perturbed by a small degree from a minimum-energy state, and are well-understood mathematically. Noise-driven harmonic oscillators realistically simulate alpha rhythm in the waking EEG as well as slow waves and spindles in the sleep EEG. Successful EEG analysis algorithms were based on such models. Several other EEG components are better described by limit-cycle or delayed-feedback oscillations. Limit-cycle oscillations arise from physical systems that show large deviations from equilibrium, whereas delayed-feedback oscillations arise

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when components of a system affect each other after significant time delays. Limit-cycle oscillations can be complex but there are powerful mathematical tools for analyzing them; the mathematics of delayed-feedback oscillations is primitive in comparison. Linear oscillators and limit-cycle oscillators qualitatively differ in terms of how they respond to fluctuations in input. In a linear oscillator, the frequency is more or less constant but the amplitude can vary greatly. In a limit-cycle oscillator, the amplitude tends to be more or less constant but the frequency can vary greatly. A heartbeat is an example of a limit-cycle oscillation in that the frequency of beats varies widely, while each individual beat continues to pump about the same amount of blood. Computational models adopt a variety of abstractions in order to describe complex oscillatory dynamics observed in brain activity. Many models are used in the field, each defined at a different level of abstraction and trying to model different aspects of neural systems. They range from models of the short-term behaviour of individual neurons, through models of how the dynamics of neural circuitry arise from interactions between individual neurons, to models of how behaviour can arise from abstract neural modules that represent complete subsystems.

Single neuron model A model of a biological neuron is a mathematical description of the properties of nerve cells, or neurons, that is designed to accurately describe and predict its biological processes. The most successful and widely-used model of neurons, the Hodgkin-Huxley model, is based on data from the squid giant axon. It is a set of nonlinear ordinary differential equations that approximates the electrical characteristics of a neuron, in particular the generation and propagation of action potentials. The model is very accurate and detailed and Hodgkin and Huxley received the 1963 Nobel Prize in physiology or medicine for this work.

Simulation of a Hindmarsh-Rose neuron showing typical bursting behavior: a fast rhythm generated by individual spikes and a slower rhythm generated by the bursts.

The mathematics of the Hodgkin-Huxley model are quite complicated and several simplifications have been proposed, such as the FitzHugh-Nagumo model and the Hindmarsh-Rose model. Such models only capture the basic neuronal dynamics, such as rhythmic spiking and bursting, but are more computationally efficient. This allows the simulation of a large number of interconnected neurons that form a neural network.

Spiking model A neural network model describes a population of physically interconnected neurons or a group of disparate neurons whose inputs or signalling targets define a recognizable circuit. These models aim to describe how the dynamics of neural circuitry arise from interactions between individual neurons. Local interactions between neurons can result in the synchronization of spiking activity and form the basis of oscillatory activity. In particular, models of interacting pyramidal cells and inhibitory interneurons have been shown to generate brain rhythms such as gamma activity.[27]

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Neural mass model Neural field models are another important tool in studying neural oscillations and are a mathematical framework describing evolution of variables such as mean firing rate in space and time. In modeling the activity of large numbers of neurons, the central idea is to take the density of neurons to the continuum limit, resulting in spatially continuous neural networks. Instead of modelling individual neurons, this approach approximates a group of neurons by its average properties and interactions. It is based on the mean field approach, an area of statistical physics that deals with large-scale systems. Models based on these principles have been used to provide mathematical descriptions of neural oscillations and EEG rhythms. They have for instance been used to investigate visual hallucinations.[29]

Simulation of a neural mass model showing [28] network spiking during the onset of a seizure. As the gain A is increased the network starts to oscillate at 3Hz.

Kuramoto model The Kuramoto model of coupled phase oscillators[30] is one of the most abstract and fundamental model used to investigate neural oscillations and sychronization. It captures the activity of a local system (e.g., a single neuron or neural ensemble) by its circular phase alone and hence ignores the amplitude of oscillations (amplitude is constant).[31] Interactions amongst these oscillators are introduced by a simple algebraic form Simulation of Kuramoto model showing neural synchronization (such as a sin function) and collectively generate a and oscillations in the mean field dynamical pattern at the global scale. The Kuramoto model is widely used to study oscillatory brain activity and several extensions have been proposed that increase its neurobiological plausibility, for instance by incorporating topological properties of local cortical connectivity.[32] In particular, it describes how the activity of a group of interactioning neurons can become synchronized and generate large-scale oscillations. Simulations using the Kuramoto model with realistic long-range cortical connectivity and time-delayed interactions reveal the emergence of slow patterned fluctuations that reproduce resting-state BOLD functional maps, which can be measured using fMRI.[33]

Activity patterns Both single and groups of neurons can generate oscillatory activity spontaneously. In addition, they may show oscillatory responses to perceptual input or motor output. Some types of neurons will fire rhythmically in the absence of any synaptic input. Likewise, brain wide activity reveals oscillatory activity while subjects do not engage in any activity, so-called resting-state activity. These ongoing rhythms can change in different ways in response to perceptual input or motor output. Oscillatory activity may respond by increases or decreases in frequency and amplitude or show a temporary interruption, which is referred to as phase resetting. In addition, external activity may not interact with ongoing activity at all, resulting in an additive response.

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Oscillatory responses

The frequency of ongoing oscillatory activity is increased between t1 and t2.

The amplitude of ongoing oscillatory activity is increased between t1 and t2.

The phase of ongoing oscillatory activity is reset at t1.

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Activity is linearly added to ongoing oscillatory activity between t1 and t2.

Ongoing activity Spontaneous activity is brain activity in the absence of an explicit task, such as sensory input or motor output, and hence also referred to as resting-state activity. It is opposed to induced activity, i.e. brain activity that is induced by sensory stimuli or motor responses. The term ongoing brain activity is used in electroencephalography and magnetoencephalography for those signal components that are not associated with the processing of a stimulus or the occurrence of specific other events, such as moving a body part, i.e. events that do not form evoked potentials/evoked fields, or induced activity. Spontaneous activity is usually considered to be noise if one is interested in stimulus processing. However, spontaneous activity is considered to play a crucial role during brain development, such as in network formation and synaptogenesis. Spontaneous activity may be informative regarding the current mental state of the person (e.g. wakefulness, alertness) and is often used in sleep research. Certain types of oscillatory activity, such as alpha waves, are part of spontaneous activity. Statistical analysis of power fluctuations of alpha activity reveals a bimodal distribution, i.e. a high- and low-amplitude mode, and hence shows that resting-state activity does not just reflect a noise process.[34] In case of fMRI, spontaneous fluctuations in the Blood-oxygen-level dependent (BOLD) signal reveal correlation patterns that are linked to resting states networks, such as the default network.[35] The temporal evolution of resting state networks is correlated with fluctuations of oscillatory EEG activity in different frequency bands.[36] Ongoing brain activity may also have an important role in perception, as it may interact with activity related to incoming stimuli. Indeed, EEG studies suggest that visual perception is dependent on both the phase and amplitude of cortical oscillations. For instance, the amplitude and phase of alpha activity at the moment of visual stimulation predicts whether a weak stimulus will be perceived by the subject.[37][38][39]

Frequency response In response to input, a neuron or neuronal ensemble may change the frequency at which it oscillates. This is very common in single neurons where the firing rate depends on the summed activity it receives. This is referred to as rate coding. Frequency changes are also commonly observed in central pattern generators and directly relate to the speed of motor activities, such as step frequency in walking. Changes in frequency are not so common in oscillatory activity involving different brain areas, as the frequency of oscillatory activity is often related to the time delays between brain areas.

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Amplitude response Next to evoked activity, neural activity related to stimulus processing may result in induced activity. Induced activity refers to modulation in ongoing brain activity induced by processing of stimuli or movement preparation. Hence, they reflect an indirect response in contrast to evoked responses. A well-studied type of induced activity is amplitude change in oscillatory activity. For instance, gamma activity often increases during increased mental activity such as during object representation.[40] Because induced responses may have different phases across measurements and therefore would cancel out during averaging, they can only be obtained using time-frequency analysis. Induced activity generally reflects the activity of numerous neurons: amplitude changes in oscillatory activity are thought to arise from the synchronization of neural activity, for instance by synchronization of spike timing or membrane potential fluctuations of individual neurons. Increases in oscillatory activity are therefore often referred to as event-related synchronization, while decreases are referred to as event-related desynchronization [41]

Phase resetting Another possibility is that input to a neuron or neuronal ensemble resets the phase of ongoing oscillations.[42] Phase resetting is very common in single neurons where spike timing is adjusted to neuronal input. For instance, a neuron may start to spike at a fixed delay in response to periodic input, which is referred to as phase locking.[8] Phase resetting may also occur at the level of neuronal ensembles when the phases of multiple neurons are adjusted simultaneously. Phase resetting of ongoing ensemble oscillations gives an alternative explanation for event-related potentials obtained by averaging multiple EEG trials with respect to the onset of a stimulus or event.[43] That is, if the phase of ongoing oscillations is reset to a fixed phase over multiple trials, oscillations will no longer average out but add up to give rise to an event-related potential. Moreover, phase resetting or phase locking is also fundamental for the synchronization of different neurons or different brain regions.[7][22] In this case the timing of spikes becomes phase locked to the activity of other neurons instead of to external input.

Additive response The term evoked activity is used in electroencephalography and magnetoencephalography for responses in brain activity that are directly related to stimulus-related activity. Evoked potentials and event-related potentials are obtained from the electroencephalogram by stimulus-locked averaging, i.e. averaging different trials at fixed latencies around the presentation of a stimulus. As a consequence, those signal components that are the same in each single measurement are conserved and all others, i.e. ongoing or spontaneous activity, are averaged out. That is, event-related potentials only reflect oscillations in brain activity that are phase-locked to the stimulus or event. Evoked activity is often considered to be independent from ongoing brain activity although this is an ongoing debate.[44]

Function Neural synchronization can be modulated by task constraints, such as attention, and is thought to play a role in feature binding,[45] neuronal communication,[1] and motor coordination.[3] Neuronal oscillations became a hot topic in neuroscience in the 1990s when the studies of the visual system of the brain by Gray, Singer and others appeared to support the neural binding hypothesis.[46] According to this idea, synchronous oscillations in neuronal ensembles bind neurons representing different features of an object. For example, when a person looks at a tree, visual cortex neurons representing the tree trunk and those representing the branches of the same tree would oscillate in synchrony to form a single representation of the tree. This phenomenon is best seen in local field potentials which reflect the synchronous activity of local groups of neurons, but has also been shown in EEG and MEG recordings providing increasing evidence for a close relation between synchronous oscillatory activity and a variety of cognitive functions such as perceptual grouping.[45]

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Pacemaker Cells in the sinoatrial node, located in the right atrium of the heart, spontaneously depolarize approximately 100 times per minute. Although all of the heart's cells have the ability to generate action potentials that trigger cardiac contraction, the sinoatrial node normally initiates it, simply because it generates impulses slightly faster than the other areas. Hence, these cells generate the normal sinus rhythm and are called pacemaker cells as they directly control the heart rate. In the absence of extrinsic neural and hormonal control, cells in the SA node will rhythmically discharge. The sinoatrial node is richly innervated by the autonomic nervous system, which up or down regulates the spontaneous firing frequency of the pacemaker cells.

Central pattern generator Synchronized firing of neurons also forms the basis of periodic motor commands for rhythmic movements. These rhythmic outputs are produced by a group of interacting neurons that form a network, called a central pattern generator. Central pattern generators are neuronal circuits that - when activated - can produce rhythmic motor patterns in the absence of sensory or descending inputs that carry specific timing information. Examples are walking, breathing, and swimming,[47] Most evidence for central pattern generators comes from lower animals, such as the lamprey, but there is also evidence for spinal central pattern generators in humans.[48]

Information processing Neuronal spiking is generally considered the basis for information transfer in the brain. For such a transfer, information needs to be coded in a spiking pattern. Different types of coding schemes have been proposed, such as rate coding and temporal coding.

Perception Synchronization of neuronal firing may serve as a means to group spatially segregated neurons that respond to the same stimulus in order to bind these responses for further joint processing, i.e. to exploit temporal synchrony to encode relations. Purely theoretical formulations of the binding-by-synchrony hypothesis were proposed first,[49] but subsequently extensive experimental evidence has been reported supporting the potential role of synchrony as a relational code.[50] The functional role of synchronized oscillatory activity in the brain was mainly established in experiments performed on awake kittens with multiple electrodes implanted in the visual cortex. These experiments showed that groups of spatially segregated neurons engage in synchronous oscillatory activity when activated by visual stimuli. The frequency of these oscillations was in the range of 40 Hz and differed from the periodic activation induced by the grating, suggesting that the oscillations and their synchronization were due to internal neuronal interactions.[50] Similar findings were shown in parallel by the group of Eckhorn providing further evidence for the functional role of neural synchronization in feature binding.[51] Since then numerous studies have replicated these findings and extended them to different modalities such as EEG, providing extensive evidence of the functional role of gamma oscillations in visual perception. Gilles Laurent and colleagues showed that oscillatory synchronization has an important functional role in odor perception. Perceiving different odors leads to different subsets of neurons firing on different sets of oscillatory cycles.[52] These oscillations can be disrupted by GABA blocker picrotoxin.[53] The disruption of the oscillatory synchronization leads to impairment of behavioral discrimination of chemically similar odorants in bees[54] and to more similar responses across odors in downstream β-lobe neurons.[55] Neural oscillations are also thought be involved in the sense of time[56] and in somatosensory perception.[57] However, recent findings argue against a clock-like function of cortical gamma oscillations.[58]

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Motor coordination Oscillations have been commonly reported in the motor system. Pfurtscheller and colleagues found a reduction in alpha (8–12 Hz) and beta (13–30 Hz) oscillations in EEG activity when subjects made a movement.[41][59] Using intra-cortical recordings, similar changes in oscillatory activity were found in motor cortex when the monkeys performed motor acts that required significant attention.[60][61] In addition, oscillations at spinal level become synchronised to beta oscillations in motor cortex during constant muscle activation, as determined by MEG/EEG-EMG coherence.[62][63][64] Recently it was found that cortical oscillations propagate as travelling waves across the surface of the motor cortex along dominant spatial axes characteristic of the local circuitry of the motor cortex.[65] Oscillatory rhythms at 10 Hz have been recorded in a brain area called the inferior olive, which is associated with the cerebellum.[9] These oscillations are also observed in motor output of physiological tremor[66] and when performing slow finger movements.[67] These findings may indicate that the human brain controls continuous movements intermittently. In support, it was shown that these movement discontinuities are directly correlated to oscillatory activity in a cerebello-thalamo-cortical loop, which may represent a neural mechanism for the intermittent motor control.[68]

Memory Neural oscillations are extensively linked to memory function, in particular theta activity. Theta rhythms are very strong in rodent hippocampi and entorhinal cortex during learning and memory retrieval, and are believed to be vital to the induction of long-term potentiation, a potential cellular mechanism of learning and memory. The coupling between theta and gamma activity is thought to be vital for memory functions.[69] The tight coordination of spike timing of single neurons with the local theta oscillations is linked to successful memory formation in humans, as more stereotyped spiking predicts better memory.[70]

Sleep and Consciousness Sleep is a naturally recurring state characterized by reduced or absent consciousness and proceeds in cycles of rapid eye movement (REM) and non-rapid eye movement (NREM) sleep. The normal order of sleep stages is N1 → N2 → N3 → N2 → REM. Sleep stages are characterized by spectral content of EEG, for instance stage N1 refers to the transition of the brain from alpha waves (common in the awake state) to theta waves, whereas stage N3 (deep or slow-wave sleep) is characterized by the presence of delta waves.

Pathology Specific types of neural oscillations may also appear in pathological situations, such as Parkinson's disease or epilepsy. Interestingly, these pathological oscillations often consist of an aberrant version of a normal oscillation. For example, one of the best known types is the spike and wave oscillation, which is typical of generalized or absence epileptic seizures, and which resembles normal sleep spindle oscillations.

Handwriting of a person affected by Parkinson's disease showing rhythmic tremor activity in the strokes

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Tremor A tremor is an involuntary, somewhat rhythmic, muscle contraction and relaxation involving to-and-fro movements of one or more body parts. It is the most common of all involuntary movements and can affect the hands, arms, eyes, face, head, vocal cords, trunk, and legs. Most tremors occur in the hands. In some people, tremor is a symptom of another neurological disorder. Many different forms of tremor have been identified, such as essential tremor or Parkinsonian tremor. It is argued that tremors are likely to be multifactorial in origin, with contributions from neural oscillations in the central nervous systems, but also from peripheral mechanisms such as reflex loop resonances.[71]

Generalized 3 Hz spike and wave discharges reflecting seizure activity

Epilepsy Epilepsy is a common chronic neurological disorder characterized by seizures. These seizures are transient signs and/or symptoms of abnormal, excessive or hypersynchronous neuronal activity in the brain.

Applications Brain-computer interface Neural oscillations have been considered for use as a control signal for various brain-computer interfaces.[72] A non-invasive BCI interface is created by placing electrodes on the scalp and then measuring the weak electric signals. Non-invasive BCI produces poor signal resolution because the skull dampens and blurs the electromagnetic signals. As a result, the activity of individual neurons can not be recovered, but oscillatory activity can still be reliably detected. In particular, some forms of BCI allow users to control a device by measuring the amplitude of oscillatory activity in specific frequency bands, including mu and beta rhythms.

Examples A non-inclusive list of types of oscillatory activity found in the central nervous system: • • • • • • • • • • • •

Delta wave Theta wave Alpha wave Mu wave Beta wave Gamma wave Sleep spindle Thalamocortical oscillations Subthreshold membrane potential oscillations Bursting Cardiac cycle Epileptic seizure

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"Relevance of nonlinear lumped-parameter models in the analysis of depth-EEG epileptic signals". Biol Cybern 83: 367–378. [29] Bressloff PC, Cowan JD (2003) Spontaneous pattern formation in primary visual cortex. In: J Hogan, AR Krauskopf, M di Bernado, RE Wilson (Eds.), Nonlinear dynamics and chaos: where do we go from here? [30] Kuramoto Y (1984). Chemical Oscillations, Waves, and Turbulence. Dover Publications. [31] Ermentrout B (1994). "An introduction to neural oscillators". In F Ventriglia (ed.), Neural Modeling and Neural Networks: 79–110. [32] Breakspear M, Heitmann S, Daffertshofer A (2010). "Generative models of cortical oscillations: Neurobiological implications of the Kuramoto model". Front Hum Neurosc 4. doi:10.3389/fnhum.2010.00190. [33] Cabral J, Hugues E, Sporns O, Deco G (2011). "Role of local network oscillations in resting-state functional connectivity". Neuroimage 57 (1): 130–9. doi:10.1016/j.neuroimage.2011.04.010. PMID 21511044.

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"The phase of ongoing EEG oscillations predicts visual perception". J Neurosci 29 (24): 7869–76. doi:10.1523/jneurosci.0113-09.2009. PMID 19535598. [39] van Dijk H, Schoffelen JM, Oostenveld R, Jensen O (2008). "Prestimulus oscillatory activity in the alpha band predicts visual discrimination ability". J Neurosci 28 (8): 1816–1823. doi:10.1523/jneurosci.1853-07.2008. [40] Tallon-Baudry C, Bertrand O (1999). "Oscillatory gamma activity in humans and its role in object representation". Trends Cogn Sci 3: 151–162. doi:10.1016/S1364-6613(99)01299-1. [41] Pfurtscheller G, da Silva FHL (1999). "Event-related EEG/MEG synchronization and desynchronization: basic principles". Clin Neurophysiol 110: 1842–1857. doi:10.1016/S1388-2457(99)00141-8. PMID 10576479. [42] Tass PA (2007). Phase resetting in medicine and biology: stochastic modelling and data analysis. Berlin Heidelberg: Springer-Verlag. ISBN 3-540-65697-9. [43] Mäkinen V, Tiitinen H, May P (2005). "Auditory event-related responses are generated independently of ongoing brain activity". NeuroImage 24: 961–968. [44] Makeig S, Westerfield M, Jung TP, Enghoff S, Townsend J, Courchesne E, Sejnowski TJ (2002). "Dynamic brain sources of visual evoked responses". Science 295: 690–694. doi:10.1126/science.1066168. PMID 11809976. [45] Singer W (1993). "Synchronization of cortical activity and its putative role in information processing and learning". Annu Rev Physiol 55: 349–374. doi:10.1146/annurev.ph.55.030193.002025. PMID 8466179. [46] Singer W, Gray CM (1995). "Visual feature integration and the temporal correlation hypothesis". Ann Rev Neurosci 18: 555–586. doi:10.1146/annurev.ne.18.030195.003011. PMID 7605074. [47] Marder E, Bucher D (2001). "Central pattern generators and the control of rhythmic movements". Curr Biol 11: R986-R996. doi:10.1016/S0960-9822(01)00581-4. [48] Dimitrijevic MR, Gerasimenko Y, Pinter MM (1998). "Evidence for a spinal central pattern generator in humans". 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Further reading • Buzsáki, György (2006). Rhythms of the Brain. Oxford University Press. ISBN 978-0-19-530106-9.

External links • • • • •

Binding by synchronization (http://www.scholarpedia.org/article/Binding_by_synchrony) Neural Field Theory (http://www.scholarpedia.org/article/Neural_fields) Spike-and-wave oscillations (http://www.scholarpedia.org/article/Spike-and-wave_oscillations) Synchronization (http://www.scholarpedia.org/article/Synchronization) Bursting (http://www.scholarpedia.org/article/Bursting)

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Sensorimotor rhythm The Sensory Motor Rhythm (SMR) is brain wave rhythm. It is an oscillatory idle rhythm of synchronized electromagnetic brain activity. It appears in spindles in recordings of SMR waves EEG, MEG, and ECoG over the sensorimotor cortex. For most individuals, the frequency of the SMR is in the range of 12 to 15 Hz.[1] The feline SMR has been noted as being analogous to the human mu rhythm.[2]

Meaning The meaning of SMR is not fully understood. Phenomenologically, a person is producing a stronger SMR amplitude when the corresponding sensory-motor areas are idle, e.g. during states of immobility. SMR typically decrease in amplitude when the corresponding sensory or motor areas are activated, e.g. during motor tasks and even during motor imagery.[3] Conceptually, SMR is sometimes mixed up with alpha waves of occipital origin, the strongest source of neural signals in the EEG. One reason might be, that without appropriate spatial filtering the SMR is very difficult to detect as it is usually superimposed by the stronger occipital alpha waves.

Relevance in research Neurofeedback Neurofeedback training can be used to gain control over the SMR activity. Neurofeedback practitioners believe—and have produced experimental evidence to back up their claims[4]—that this feedback enables the subject to learn the regulation of their own SMR. People with learning difficulties,[5] ADHD,[6] epilepsy,[7] and autism[8] may benefit from an increase in SMR activity via neurofeedback. In the field of Brain-Computer Interfaces (BCI), the deliberate modification of the SMR amplitude during motor imagery can be used to control external applications.[9]

References [1] Arroyo, S.; Lesser, RP.; Gordon, B; Uematsu, S; Jackson, D; Webber, R (1993). "Functional significance of the mu rhythm of human cortex: an electrophysiologic study with subdural electrodes". Electroencephalography and Clinical Neurophysiology 87 (3): 76–87. doi:10.1016/0013-4694(93)90114-B. PMID 7691544. [2] http:/ / www. sciencedirect. com/ science?_ob=ArticleURL& _udi=B6SYT-4PP03CV-N& _user=10& _coverDate=06/ 30/ 1979& _rdoc=1& _fmt=high& _orig=search& _origin=search& _sort=d& _docanchor=& view=c& _acct=C000050221& _version=1& _urlVersion=0& _userid=10& md5=195097667f4d5fea855e3f8e4c2b42c8& searchtype=a [3] Ernst Niedermeyer, Fernando Lopes da Silva Electroencephalography. Basic principles, Clinical Applications and Related Fields. 3rd edition, Williams & Wilkins Baltimore 1993 [4] Tobias Egner and M. Barry Sterman, “Neurofeedback treatment of epilepsy: From basic rationale to practical application,” in press [5] Tansey MA (February 1984). "EEG sensorimotor rhythm biofeedback training: some effects on the neurologic precursors of learning disabilities". Int J Psychophysiol 1 (2): 163–77. PMID 6542077. [6] Vernon, David; Tobias Egner, Nick Cooper, Theresa Compton, Claire Neilands, Amna Sheri and John Gruzelier (January 2003). "The effect of training distinct neurofeedback protocols on aspects of cognitive performance". International Journal of Psychophysiology 47 (1): 75–85. doi:10.1016/S0167-8760(02)00091-0. PMID 12543448. [7] Egner, Tobias; M Barry Sterman (February 2006). "Neurofeedback treatment of epilepsy: from basic rationale to practical application". Expert Review of Neurotherapeutics (Future Drugs) 6 (2): 247–257. doi:10.1586/14737175.6.2.247. PMID 16466304.

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Sensorimotor rhythm [8] Pineda, Jaime; Brang, D., Hecht, E., Edwards, L., Carey, S., Bacon, M., Futagaki, C., Suk, D., Tom, J., Birnbaum, C., and Rork, A. (2008). "Positive behavioral and electrophysiological changes following neurofeedback training in children with autism". Research in Autism Spectrum Disorders 2 (3): 557–581. doi:10.1016/j.rasd.2007.12.003. [9] Andrea Kübler and Klaus-Robert Müller. An introduction to brain computer interfacing. In Guido Dornhege, Jose del R. Millán, Thilo Hinterberger, Dennis McFarland, and Klaus-Robert Müller, editors, Toward Brain-Computer Interfacing, pages 1-25. MIT press, Cambridge, MA, 2007

Further reading • Robbins, Jim (2000). A Symphony in the Brain. ISBN 0-87113-807-7. • Sterman, M. B.; Wyrwicka, W. (1967). "EEG correlates of sleep: Evidence for separate forebrain substrates". Brain Research 6 (1): 143–163. doi:10.1016/0006-8993(67)90186-2. PMID 6052533. • Wyrwicka, W.; Sterman, M. B. (1968). "Instrumental conditioning of sensorimotor cortex eeg spindles in the waking cat". Physiology and Behavior 3 (5): 703–707. doi:10.1016/0031-9384(68)90139-X. • Warren, Jeff (2007). "The SMR". The Head Trip: Adventures on the Wheel of Consciousness. Toronto: Random House Canada. ISBN 978-0-679-31408-0.

Sleep spindle A sleep spindle is a burst of oscillatory brain activity visible on an EEG that occurs during stage 2 sleep. It consists of 12–14 Hz waves that occur for at least 0.5 seconds.[1][2]

Function Sleep spindles (sometimes referred to as "sigma bands" or "sigma waves") may represent periods where the brain is inhibiting processing to keep the sleeper in a tranquil state. Along with K-complexes they are defining characteristics of, and indicate the onset of, stage 2 sleep. They are often tapered at both ends and frequently seen over the frontal and central head regions. They may or may not be synchronous, but they should be symmetrical and bilateral. During sleep these spindles are seen in the brain as a burst of activity immediately following muscle twitching. Researchers think the brain, particularly in the young, is learning about what nerves control what specific muscles when asleep.[3][4] Spindles generated in the thalamus have been shown to aid sleeping in the presence of disruptive external sounds. A correlation has been found between the amount of brainwave activity in the thalamus and a sleeper's ability to maintain tranquility. [5] Sleep spindles result from interactions between cells in the thalamus and the cortex. Sleep spindle activity has furthermore been found to be associated with the integration of new information into existing knowledge [6] as well directed remembering and forgetting (fast sleep spindles) [7] During NREM sleep, the brain waves produced by people with schizophrenia lack the normal pattern of slow and fast spindles.[8]

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References [1] Rechtschaffen, A.; Kales, A. (1968). A Manual of Standardized Terminology, Techniques and Scoring System For Sleep Stages of Human Subjects. US Dept of Health, Education, and Welfare; National Institutes of Health. [2] De Gennaro, L.; Ferrara, M. (2003). Sleep spindles: an overview. Sleep medicine reviews, 7(5), 423–440 [3] "To sleep, perchance to twitch" (http:/ / www. apa. org/ monitor/ jan06/ twitch. aspx) [4] "Wiring your brain at college – a new perspective on sleep" (http:/ / intro2psych. wordpress. com/ 2009/ 04/ 01/ wiring-your-brain-at-college-a-new-perspective-on-sleep/ ) [5] Thien Thanh Dang-Vu, Scott M. McKinney, Orfeu M. Buxton, Jo M. Solet, Jeffrey M. Ellenbogen. Spontaneous brain rhythms predict sleep stability in the face of noise. Current Biology - 10 August 2010 (Vol. 20, Issue 15, pp. R626-R627) [6] Tamminen, J.; Payne, J.D.; Stickgold, R.; Wamsley, E.J.; Gareth Gaskell, M. (2010). Sleep spindle activity is associated with the integration of new memories and existing knowledge. The Journal of Neuroscience, 30(43), 14356–60 [7] Saletin, J.M.; Goldstein, A.N.; Walker, M.P (2011). The Role of Sleep in Directed Forgetting and Remembering of Human memories. Cerebral Cortex, 21, 2534–2541 [8] Ferrarelli, F.; Huber, R.; Peterson, M.J.; Massimini, M.; Murphy, M.; Riedner, B.A.; Watson, A.; Bria, P.; Tononi, G. (2007). Reduced Sleep Spindle Activity in Schizophrenia Patients. The American Journal of Psychiatry, 164, A62

114

Biofeedback

115

Biofeedback Biofeedback Intervention

A diagram showing the feedback loop between person, sensor, and processor to help provide biofeedback training. (Polish language captions) [1]

ICD-10-PCS

GZC

ICD-9-CM

94.39

MeSH

D001676

MedlinePlus

002241

[2] [3]

[4]

Biofeedback is the process of gaining greater awareness of many physiological functions primarily using instruments that provide information on the activity of those same systems, with a goal of being able to manipulate them at will.[5][6] Some of the processes that can be controlled include brainwaves, muscle tone, skin conductance, heart rate and pain perception.[7] Biofeedback may be used to improve health, performance, and the physiological changes which often occur in conjunction with changes to thoughts, emotions, and behavior. Eventually, these changes may be maintained without the use of extra equipment, even though no equipment is necessarily required to practice biofeedback.[6]

Biofeedback device for treating posttraumatic stress disorder

Biofeedback has been found to be effective for the treatment of headaches and migraines.[8][9]

Definition Three professional biofeedback organizations, the Association for Applied Psychophysiology and Biofeedback (AAPB), Biofeedback Certification International Alliance (BCIA), and the International Society for Neurofeedback and Research (ISNR), arrived at a consensus definition of biofeedback in 2008:

“

is a process that enables an individual to learn how to change physiological activity for the purposes of improving health and performance. Precise instruments measure physiological activity such as brainwaves, heart function, breathing, muscle activity, and skin temperature. These instruments rapidly and accurately 'feed back' information to the user. The presentation of this information — often in conjunction with changes in thinking, emotions, and behavior — supports desired physiological changes. Over time, these changes can endure without [6] continued use of an instrument.

”

Biofeedback

Sensor modalities Electromyograph An electromyograph (EMG) uses surface electrodes to detect muscle action potentials from underlying skeletal muscles that initiate muscle contraction. Clinicians record the surface electromyogram (SEMG) using one or more active electrodes that are placed over a target muscle and a reference electrode that is placed within six inches of either active. The SEMG is measured in microvolts (millionths of a volt).[10][11] Biofeedback therapists use EMG biofeedback when treating anxiety and worry, chronic pain, computer-related disorder, essential hypertension, headache (migraine, mixed headache, and tension-type headache), low back pain, physical rehabilitation (cerebral palsy, incomplete spinal cord lesions, and stroke), temporomandibular joint disorder (TMD), torticollis, and fecal incontinence, urinary incontinence, and pelvic pain.[12][13]

Feedback thermometer A feedback thermometer detects skin temperature with a thermistor (a temperature-sensitive resistor) that is usually attached to a finger or toe and measured in degrees Celsius or Fahrenheit. Skin temperature mainly reflects arteriole diameter. Hand-warming and hand-cooling are produced by separate mechanisms, and their regulation involves different skills.[14] Hand-warming involves arteriole vasodilation produced by a beta-2 adrenegeric hormonal mechanism.[15] Hand-cooling involves arteriole vasoconstriction produced by the increased firing of sympathetic C-fibers.[16] Biofeedback therapists use temperature biofeedback when treating chronic pain, edema, headache (migraine and tension-type headache), essential hypertension, Raynaud’s disease, anxiety, and stress.[13]

Electrodermograph An electrodermograph (EDG) measures skin electrical activity directly (skin conductance and skin potential) and indirectly (skin resistance) using electrodes placed over the digits or hand and wrist. Orienting responses to unexpected stimuli, arousal and worry, and cognitive activity can increase eccrine sweat gland activity, increasing the conductivity of the skin for electrical current.[14] In skin conductance, an electrodermograph imposes an imperceptible current across the skin and measures how easily it travels through the skin. When anxiety raises the level of sweat in a sweat duct, conductance increases. Skin conductance is measured in microsiemens (millionths of a siemens). In skin potential, a therapist places an active electrode over an active site (e.g., the palmar surface of the hand) and a reference electrode over a relatively inactive site (e.g., forearm). Skin potential is the voltage that develops between eccrine sweat glands and internal tissues and is measured in millivolts (thousandths of a volt). In skin resistance, also called galvanic skin response (GSR), an electrodermograph imposes a current across the skin and measures the amount of opposition it encounters. Skin resistance is measured in kΩ (thousands of ohms).[17] Biofeedback therapists use electrodermal biofeedback when treating anxiety disorders, hyperhidrosis (excessive sweating), and stress.[13][18] Electrodermal biofeedback is used as an adjunct to psychotherapy to increase client awareness of their emotions.[19][20] In addition, electrodermal measures have long served as one of the central tools in polygraphy (lie detection) because they reflect changes in anxiety or emotional activation.[21]

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Electroencephalograph An electroencephalograph (EEG) measures the electrical activation of the brain from scalp sites located over the human cortex. The EEG shows the amplitude of electrical activity at each cortical site, the amplitude and relative power of various wave forms at each site, and the degree to which each cortical site fires in conjunction with other cortical sites (coherence and symmetry).[22] The EEG uses precious metal electrodes to detect a voltage between at least two electrodes located on the scalp. The EEG records both excitatory postsynaptic potentials (EPSPs) and inhibitory postsynaptic potentials (IPSPs) that largely occur in dendrites in pyramidal cells located in macrocolumns, several millimeters in diameter, in the upper cortical layers. Neurofeedback monitors both slow and fast cortical potentials.[23] Slow cortical potentials are gradual changes in the membrane potentials of cortical dendrites that last from 300 ms to several seconds. These potentials include the contingent negative variation (CNV), readiness potential, movement-related potentials (MRPs), and P300 and N400 potentials.[24] Fast cortical potentials range from 0.5 Hz to 100 Hz.[25] The main frequency ranges include delta, theta, alpha, the sensorimotor rhythm, low beta, high beta, and gamma. The specific cutting points defining the frequency ranges vary considerably among professionals. Fast cortical potentials can be described by their predominant frequencies, but also by whether they are synchronous or asynchronous wave forms. Synchronous wave forms occur at regular periodic intervals, whereas asynchronous wave forms are irregular.[23] The synchronous delta rhythm ranges from 0.5 to 3.5 Hz. Delta is the dominant frequency from ages 1 to 2, and is associated in adults with deep sleep and brain pathology like trauma and tumors, and learning disability. The synchronous theta rhythm ranges from 4 to 7 Hz. Theta is the dominant frequency in healthy young children and is associated with drowsiness or starting to sleep, REM sleep, hypnagogic imagery (intense imagery experienced before the onset of sleep), hypnosis, attention, and processing of cognitive and perceptual information. The synchronous alpha rhythm ranges from 8 to 13 Hz and is defined by its waveform and not by its frequency. Alpha activity can be observed in about 75% of awake, relaxed individuals and is replaced by low-amplitude desynchronized beta activity during movement, complex problem-solving, and visual focusing. This phenomenon is called alpha blocking. The synchronous sensorimotor rhythm (SMR) ranges from 12 to 15 Hz and is located over the sensorimotor cortex (central sulcus). The sensorimotor rhythm is associated with the inhibition of movement and reduced muscle tone. The beta rhythm consists of asynchronous waves and can be divided into low beta and high beta ranges (13–21 Hz and 20–32 Hz). Low beta is associated with activation and focused thinking. High beta is associated with anxiety, hypervigilance, panic, peak performance, and worry. EEG activity from 36 to 44 Hz is also referred to as gamma. Gamma activity is associated with perception of meaning and meditative awareness.[23][26][27] Neurotherapists use EEG biofeedback when treating addiction, attention deficit hyperactivity disorder (ADHD), learning disability, anxiety disorders (including worry, obsessive-compulsive disorder and posttraumatic stress disorder), depression, migraine, and generalized seizures.[13][28]

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118

Photoplethysmograph A photoplethysmograph (PPG) measures the relative blood flow through a digit using a photoplethysmographic (PPG) sensor attached by a Velcro band to the fingers or to the temple to monitor the temporal artery. An infrared light source is transmitted through or reflected off the tissue, detected by a phototransistor, and quantified in arbitrary units. Less light is absorbed when blood flow is greater, increasing the intensity of light reaching the sensor.[29] A photoplethysmograph can measure blood volume pulse (BVP), which is the phasic change in blood volume with each heartbeat, heart rate, and heart rate variability (HRV), which consists of beat-to-beat differences in intervals between successive heartbeats.[30][31] A photoplethysmograph can provide useful feedback when temperature feedback shows minimal change. This is because the PPG sensor is more sensitive than a thermistor to minute blood flow changes.[27] Biofeedback therapists can use a photoplethysmograph to supplement temperature biofeedback when treating chronic pain, edema, headache (migraine and tension-type headache), essential hypertension, Raynaud’s disease, anxiety, and stress.[13]

An emWave2 photoplethysmograph for monitoring heart rate variability

Electrocardiograph The electrocardiograph (ECG) uses electrodes placed on the torso, wrists, or legs, to measure the electrical activity of the heart and measures the interbeat interval (distances between successive R-wave peaks in the QRS complex). The interbeat interval, divided into 60 seconds, determines the heart rate at that moment. The statistical variability of that interbeat interval is what we call heart rate variability.[32] The ECG method is more accurate than the PPG method in measuring heart rate variability.[29][33] Biofeedback therapists use HRV biofeedback when treating asthma,[34] COPD,[35] depression,[36] fibromyalgia,[37] heart disease,[38] and unexplained abdominal pain.[39]

Pneumograph A pneumograph or respiratory strain gauge uses a flexible sensor band that is placed around the chest, abdomen, or both. The strain gauge method can provide feedback about the relative expansion/contraction of the chest and abdomen, and can measure respiration rate (the number of breaths per minute).[24] Clinicians can use a pneumograph to detect and correct dysfunctional breathing patterns and behaviors. Dysfunctional breathing patterns include clavicular breathing (breathing that primarily relies on the external intercostals and the accessory muscles of respiration to inflate the lungs), reverse breathing (breathing where the abdomen expands during exhalation and contracts during inhalation), and thoracic breathing (shallow breathing that primarily relies on the external intercostals to inflate the lungs). Dysfunctional breathing behaviors include apnea (suspension of breathing), gasping, sighing, and wheezing.[40] A pneumograph is often used in conjunction with an electrocardiograph (ECG) or photoplethysmograph (PPG) in heart rate variability (HRV) training.[30][41] Biofeedback therapists use pneumograph biofeedback with patients diagnosed with anxiety disorders, asthma, chronic pulmonary obstructive disorder (COPD), essential hypertension, panic attacks, and stress.[13][42]

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Capnometer A capnometer or capnograph uses an infrared detector to measure end-tidal CO2 (the partial pressure of carbon dioxide in expired air at the end of expiration) exhaled through the nostril into a latex tube. The average value of end-tidal CO2 for a resting adult is 5% (36 Torr or 4.8 kPa). A capnometer is a sensitive index of the quality of patient breathing. Shallow, rapid, and effortful breathing lowers CO2, while deep, slow, effortless breathing increases it.[40] Biofeedback therapists use capnometric biofeedback to supplement respiratory strain gauge biofeedback with patients diagnosed with anxiety disorders, asthma, chronic pulmonary obstructive disorder (COPD), essential hypertension, panic attacks, and stress.[13][42][43]

Rheoencephalograph Rheoencephalography (REG), or brain blood flow biofeedback, is a biofeedback technique of a conscious control of blood flow. An electronic device called a rheoencephalograph [from Greek rheos stream, anything flowing, from rhein to flow] is utilized in brain blood flow biofeedback. Electrodes are attached to the skin at certain points on the head and permit the device to measure continuously the electrical conductivity of the tissues of structures located between the electrodes. The brain blood flow technique is based on non-invasive method of measuring bio-impedance. Changes in bio-impedance are generated by blood volume and blood flow and registered by a rheographic device.[44] The pulsative bio-impedance changes directly reflect the total blood flow of the deep structures of brain due to high frequency impedance measurements.[45]

Hemoencephalography Hemoencephalography or HEG biofeedback is a functional infrared imaging technique. As its name describes, it measures the differences in the color of light reflected back through the scalp based on the relative amount of oxygenated and unoxygenated blood in the brain. Research continues to determine its reliability, validity, and clinical applicability. HEG is used to treat ADHD and migraine, and for research.[46]

Applications Incontinence Mowrer detailed the use of a bedwetting alarm that sounds when children urinate while asleep. This simple biofeedback device can quickly teach children to wake up when their bladders are full and to contract the urinary sphincter and relax the detrusor muscle, preventing further urine release. Through classical conditioning, sensory feedback from a full bladder replaces the alarm and allows children to continue sleeping without urinating.[47] Kegel developed the perineometer in 1947 to treat urinary incontinence (urine leakage) in women whose pelvic floor muscles are weakened during pregnancy and childbirth. The perineometer, which is inserted into the vagina to monitor pelvic floor muscle contraction, satisfies all the requirements of a biofeedback device and enhances the effectiveness of popular Kegel exercises.[48] Research has shown that biofeedback can improve the efficacy of pelvic floor exercises and help restore proper bladder functions. The mode of action of vaginal cones, for instance involves a biological biofeedback mechanism . Studies have shown that biofeedback obtained with vaginal cones is as effective as biofeedback induced through physiotherapy electrostimulation.[49] In 1992, the United States Agency for Health Care Policy and Research recommended biofeedback as a first-line treatment for adult urinary incontinence.[50]

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EEG Caton recorded spontaneous electrical potentials from the exposed cortical surface of monkeys and rabbits, and was the first to measure event-related potentials (EEG responses to stimuli) in 1875.[51] Danilevsky published Investigations in the Physiology of the Brain, which explored the relationship between the EEG and states of consciousness in 1877.[52] Beck published studies of spontaneous electrical potentials detected from the brains of dogs and rabbits, and was the first to document alpha blocking, where light alters rhythmic oscillations, in 1890.[53] Sherrington introduced the terms neuron and synapse and published the Integrative Action of the Nervous System in 1906.[54] Pravdich-Neminsky photographed the EEG and event related potentials from dogs, demonstrated a 12–14 Hz rhythm that slowed during asphyxiation, and introduced the term electrocerebrogram in 1912.[55] Forbes reported the replacement of the string galvanometer with a vacuum tube to amplify the EEG in 1920. The vacuum tube became the de facto standard by 1936.[56] Berger (1924) published the first human EEG data. He recorded electrical potentials from his son Klaus's scalp. At first he believed that he had discovered the physical mechanism for telepathy but was disappointed that the electromagnetic variations disappear only millimeters away from the skull. (He did continue to believe in telepathy throughout his life, however, having had a particularly confirming event regarding his sister). He viewed the EEG as analogous to the ECG and introduced the term elektenkephalogram. He believed that the EEG had diagnostic and therapeutic promise in measuring the impact of clinical interventions. Berger showed that these potentials were not due to scalp muscle contractions. He first identified the alpha rhythm, which he called the Berger rhythm, and later identified the beta rhythm and sleep spindles. He demonstrated that alterations in consciousness are associated with changes in the EEG and associated the beta rhythm with alertness. He described interictal activity (EEG potentials between seizures) and recorded a partial complex seizure in 1933. Finally, he performed the first QEEG, which is the measurement of the signal strength of EEG frequencies.[57] Adrian and Matthews confirmed Berger's findings in 1934 by recording their own EEGs using a cathode-ray oscilloscope. Their demonstration of EEG recording at the 1935 Physiological Society meetings in England caused its widespread acceptance. Adrian used himself as a subject and demonstrated the phenomenon of alpha blocking, where opening his eyes suppressed alpha rhythms.[58] Gibbs, Davis, and Lennox inaugurated clinical electroencephalography in 1935 by identifying abnormal EEG rhythms associated with epilepsy, including interictal spike waves and 3 Hz activity in absence seizures.[52] Bremer used the EEG to show how sensory signals affect vigilance in 1935.[59] Walter (1937, 1953) named the delta waves and theta waves, and the contingent negative variation (CNV), a slow cortical potential that may reflect expectancy, motivation, intention to act, or attention. He located an occipital lobe source for alpha waves and demonstrated that delta waves can help locate brain lesions like tumors. He improved Berger's electroencephalograph and pioneered EEG topography.[60] Kleitman has been recognized as the "Father of American sleep research" for his seminal work in the regulation of sleep-wake cycles, circadian rhythms, the sleep patterns of different age groups, and the effects of sleep deprivation. He discovered the phenomenon of rapid eye movement (REM) sleep with his graduate student Aserinsky in 1953.[61] Dement, another of Kleitman's students, described the EEG architecture and phenomenology of sleep stages and the transitions between them in 1955, associated REM sleep with dreaming in 1957, and documented sleep cycles in another species, cats, in 1958, which stimulated basic sleep research. He established the Stanford University Sleep Research Center in 1970.[62] Andersen and Andersson (1968) proposed that thalamic pacemakers project synchronous alpha rhythms to the cortex via thalamocortical circuits.[63]

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Biofeedback Kamiya (1968) demonstrated that the alpha rhythm in humans could be operantly conditioned. He published an influential article in Psychology Today that summarized research that showed that subjects could learn to discriminate when alpha was present or absent, and that they could use feedback to shift the dominant alpha frequency about 1 Hz. Almost half of his subjects reported experiencing a pleasant "alpha state" characterized as an "alert calmness." These reports may have contributed to the perception of alpha biofeedback as a shortcut to a meditative state. He also studied the EEG correlates of meditative states.[64] Brown (1970) demonstrated the clinical use of alpha-theta biofeedback. In research designed to identify the subjective states associated with EEG rhythms, she trained subjects to increase the abundance of alpha, beta, and theta activity using visual feedback and recorded their subjective experiences when the amplitude of these frequency bands increased. She also helped popularize biofeedback by publishing a series of books, including New Mind, New body (1974) and Stress and the Art of Biofeedback (1977).[65][66][67] Mulholland and Peper (1971) showed that occipital alpha increases with eyes open and not focused, and is disrupted by visual focusing; a rediscovery of alpha blocking.[68] Green and Green (1986) investigated voluntary control of internal states by individuals like Swami Rama and American Indian medicine man Rolling Thunder both in India and at the Menninger Foundation. They brought portable biofeedback equipment to India and monitored practitioners as they demonstrated self-regulation. A film containing footage from their investigations was released as Biofeedback: The Yoga of the West (1974). They developed alpha-theta training at the Menninger Foundation from the 1960s to the 1990s. They hypothesized that theta states allow access to unconscious memories and increase the impact of prepared images or suggestions. Their alpha-theta research fostered Peniston's development of an alpha-theta addiction protocol.[69] Sterman (1972) showed that cats and human subjects could be operantly trained to increase the amplitude of the sensorimotor rhythm (SMR) recorded from the sensorimotor cortex. He demonstrated that SMR production protects cats against drug-induced generalized seizures (tonic-clonic seizures involving loss of consciousness) and reduces the frequency of seizures in humans diagnosed with epilepsy. He found that his SMR protocol, which uses visual and auditory EEG biofeedback, normalizes their EEGs (SMR increases while theta and beta decrease toward normal values) even during sleep. Sterman also co-developed the Sterman-Kaiser (SKIL) QEEG database.[70] Birbaumer and colleagues (1981) have studied feedback of slow cortical potentials since the late 1970s. They have demonstrated that subjects can learn to control these DC potentials and have studied the efficacy of slow cortical potential biofeedback in treating ADHD, epilepsy, migraine, and schizophrenia.[71] Lubar (1989) studied SMR biofeedback to treat attention disorders and epilepsy in collaboration with Sterman. He demonstrated that SMR training can improve attention and academic performance in children diagnosed with Attention Deficit Disorder with Hyperactivity (ADHD). He documented the importance of theta-to-beta ratios in ADHD and developed theta suppression-beta enhancement protocols to decrease these ratios and improve student performance.[72]

Electrodermal system Feré demonstrated the exosomatic method of recording of skin electrical activity by passing a small current through the skin in 1888.[73] Tarchanoff used the endosomatic method by recording the difference in skin electrical potential from points on the skin surface in 1889; no external current was applied.[74] Jung employed the galvanometer, which used the exosomatic method, in 1907 to study unconscious emotions in word-association experiments.[75] Marjorie and Hershel Toomim (1975) published a landmark article about the use of GSR biofeedback in psychotherapy.[19] Meyer and Reich discussed similar material in a British publication.[76]

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Musculoskeletal system Jacobson (1930) developed hardware to measure EMG voltages over time, showed that cognitive activity (like imagery) affects EMG levels, introduced the deep relaxation method Progressive Relaxation, and wrote Progressive Relaxation (1929) and You Must Relax (1934). He prescribed daily Progressive Relaxation practice to treat diverse psychophysiological disorders like hypertension.[77] Several researchers showed that human subjects could learn precise control of individual motor units (motor neurons and the muscle fibers they control). Lindsley (1935) found that relaxed subjects could suppress motor unit firing without biofeedback training.[78] Harrison and Mortensen (1962) trained subjects using visual and auditory EMG biofeedback to control individual motor units in the tibialis anterior muscle of the leg.[79] Basmajian (1963) instructed subjects using unfiltered auditory EMG biofeedback to control separate motor units in the abductor pollicis muscle of the thumb in his Single Motor Unit Training (SMUT) studies. His best subjects coordinated several motor units to produce drum rolls. Basmajian demonstrated practical applications for neuromuscular rehabilitation, pain management, and headache treatment.[80] Marinacci (1960) applied EMG biofeedback to neuromuscular disorders (where proprioception is disrupted) including Bell Palsy (one-sided facial paralysis), polio, and stroke.[81] "While Marinacci used EMG to treat neuromuscular disorders, his colleagues only used the EMG for diagnosis. They were unable to recognize its potential as a teaching tool even when the evidence stared them in the face! Many electromyographers who performed nerve conduction studies used visual and auditory feedback to reduce interference when a patient recruited too many motor units. Even though they used EMG biofeedback to guide the patient to relax so that clean diagnostic EMG tests could be recorded, they were unable to envision EMG biofeedback treatment of motor disorders."[82] Whatmore and Kohli (1968) introduced the concept of dysponesis (misplaced effort) to explain how functional disorders (where body activity is disturbed) develop. Bracing your shoulders when you hear a loud sound illustrates dysponesis since this action does not protect against injury.[83] These clinicians applied EMG biofeedback to diverse functional problems like headache and hypertension. They reported case follow-ups ranging from 6 to 21 years. This was long compared with typical 0-24 month follow-ups in the clinical literature. Their data showed that skill in controlling misplaced efforts was positively related to clinical improvement. Last, they wrote The Pathophysiology and Treatment of Functional Disorders (1974) that outlined their treatment of functional disorders.[84] Wolf (1983) integrated EMG biofeedback into physical therapy to treat stroke patients and conducted landmark stroke outcome studies.[85] Peper (1997) applied SEMG to the workplace, studied the ergonomics of computer use, and promoted "healthy computing."[86] Taub (1999, 2006) demonstrated the clinical efficacy of constraint-induced movement therapy (CIMT) for the treatment of spinal cord-injured and stroke patients.[87][88]

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Cardiovascular system Shearn (1962) operantly trained human subjects to increase their heart rates by 5 beats-per-minute to avoid electric shock.[89] In contrast to Shearn's slight heart rate increases, Swami Rama used yoga to produce atrial flutter at an average 306 beats per minute before a Menninger Foundation audience. This briefly stopped his heart's pumping of blood and silenced his pulse.[69] Engel and Chism (1967) operantly trained subjects to decrease, increase, and then decrease their heart rates (this was analogous to ON-OFF-ON EEG training). He then used this approach to teach patients to control their rate of premature ventricular contractions (PVCs), where the ventricles contract too soon. Engel conceptualized this training protocol as illness onset training, since patients were taught to produce and then suppress a symptom.[90] Peper has similarly taught asthmatics to wheeze to better control their breathing.[91] Schwartz (1971, 1972) examined whether specific patterns of cardiovascular activity are easier to learn than others due to biological constraints. He examined the constraints on learning integrated (two autonomic responses change in the same direction) and differentiated (two autonomic responses change inversely) patterns of blood pressure and heart rate change.[92] Schultz and Luthe (1969) developed Autogenic Training, which is a deep relaxation exercise derived from hypnosis. This procedure combines passive volition with imagery in a series of three treatment procedures (standard Autogenic exercises, Autogenic neutralization, and Autogenic meditation). Clinicians at the Menninger Foundation coupled an abbreviated list of standard exercises with thermal biofeedback to create autogenic biofeedback.[93] Luthe (1973) also published a series of six volumes titled Autogenic therapy.[94] Fahrion and colleagues (1986) reported on an 18-26 session treatment program for hypertensive patients. The Menninger program combined breathing modification, autogenic biofeedback for the hands and feet, and frontal EMG training. The authors reported that 89% of their medication patients discontinued or reduced medication by one-half while significantly lowering blood pressure. While this study did not include a double-blind control, the outcome rate was impressive.[95] Freedman and colleagues (1991) demonstrated that hand-warming and hand-cooling are produced by different mechanisms. The primary hand-warming mechanism is beta-adrenergic (hormonal), while the main hand-cooling mechanism is alpha-adrenergic and involves sympathetic C-fibers. This contradicts the traditional view that finger blood flow is exclusively controlled by sympathetic C-fibers. The traditional model asserts that when firing is slow, hands warm; when firing is rapid, hands cool. Freedman and colleagues’ studies support the view that hand-warming and hand-cooling represent entirely different skills.[96] Vaschillo and colleagues (1983) published the first studies of HRV biofeedback with cosmonauts and treated patients diagnosed with psychiatric and psychophysiological disorders.[97][98] Lehrer collaborated with Smetankin and Potapova in treating pediatric asthma patients[99] and published influential articles on HRV asthma treatment in the medical journal Chest.[100]

Pain Budzynski and Stoyva (1969) showed that EMG biofeedback could reduce frontalis muscle (forehead) contraction.[101] They demonstrated in 1973 that analog (proportional) and binary (ON or OFF) visual EMG biofeedback were equally helpful in lowering masseter SEMG levels.[102] Budzynski, Stoyva, Adler, and Mullaney (1973) reported that auditory frontalis EMG biofeedback combined with home relaxation practice lowered tension headache frequency and frontalis EMG levels. A control group that received noncontingent (false) auditory feedback did not improve. This study helped make the frontalis muscle the placement-of-choice in EMG assessment and treatment of headache and other psychophysiological disorders.[103] Sargent, Green, and Walters (1972, 1973) demonstrated that hand-warming could abort migraines and that autogenic biofeedback training could reduce headache activity. The early Menninger migraine studies, although

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Biofeedback methodologically weak (no pretreatment baselines, control groups, or random assignment to conditions), strongly influenced migraine treatment.[104][105] Flor (2002) trained amputees to detect the location and frequency of shocks delivered to their stumps, which resulted in an expansion of corresponding cortical regions and significant reduction of their phantom limb pain.[106] McNulty, Gevirtz, Hubbard, and Berkoff (1994) proposed that sympathetic nervous system innervation of muscle spindles underlies trigger points.[107]

Clinical effectiveness Research Moss, LeVaque, and Hammond (2004) observed that “Biofeedback and neurofeedback seem to offer the kind of evidence-based practice that the health care establishment is demanding."[108][109] "From the beginning biofeedback developed as a research-based approach emerging directly from laboratory research on psychophysiology and behavior therapy, The ties of biofeedback/neurofeedback to the biomedical paradigm and to research are stronger than is the case for many other behavioral interventions” (p. 151).[110] The Association for Applied Psychophysiology and Biofeedback (AAPB) and the International Society for Neurofeedback and Research (ISNR) have collaborated in validating and rating treatment protocols to address questions about the clinical efficacy of biofeedback and neurofeedback applications, like ADHD and headache. In 2001, Donald Moss, then president of the Association for Applied Psychophysiology and Biofeedback, and Jay Gunkelman, president of the International Society for Neurofeedback and Research, appointed a task force to establish standards for the efficacy of biofeedback and neurofeedback. The Task Force document was published in 2002,[111] and a series of white papers followed, reviewing the efficacy of a series of disorders.[112] The white papers established the efficacy of biofeedback for functional anorectal disorders,[113] attention deficit disorder,[114] facial pain and temporomandibular disorder,[115] hypertension,[116] urinary incontinence,[117] Raynaud's phenomenon,[118] substance abuse,[119] and headache.[120] A broader review was published[121] and later updated,[13] applying the same efficacy standards to the entire range of medical and psychological disorders. The 2008 edition reviewed the efficacy of biofeedback for over 40 clinical disorders, ranging from alcoholism/substance abuse to vulvar vestibulitis. The ratings for each disorder depend on the nature of research studies available on each disorder, ranging from anecdotal reports to double blind studies with a control group. Thus, a lower rating may reflect the lack of research rather than the ineffectiveness of biofeedback for the problem.

Efficacy Yucha and Montgomery's (2008) ratings are listed for the five levels of efficacy recommended by a joint Task Force and adopted by the Boards of Directors of the Association for Applied Psychophysiology (AAPB) and the International Society for Neuronal Regulation (ISNR).[111] From weakest to strongest, these levels include: not empirically supported, possibly efficacious, probably efficacious, efficacious, and efficacious and specific. Level 1: Not empirically supported. This designation includes applications supported by anecdotal reports and/or case studies in non-peer reviewed venues. Yucha and Montgomery (2008) assigned eating disorders, immune function, spinal cord injury, and syncope to this category.[13] Level 2: Possibly efficacious. This designation requires at least one study of sufficient statistical power with well identified outcome measures but lacking randomized assignment to a control condition internal to the study. Yucha and Montgomery (2008) assigned asthma, autism, Bell palsy, cerebral palsy, COPD, coronary artery disease, cystic fibrosis, depression, erectile dysfunction, fibromyalgia, hand dystonia, irritable bowel syndrome, PTSD, repetitive strain injury, respiratory failure, stroke, tinnitus, and urinary incontinence in children to this category.[13]

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Biofeedback Level 3: Probably efficacious. This designation requires multiple observational studies, clinical studies, wait list controlled studies, and within subject and intrasubject replication studies that demonstrate efficacy. Yucha and Montgomery (2008) assigned alcoholism and substance abuse, arthritis, diabetes mellitus, fecal disorders in children, fecal incontinence in adults, insomnia, pediatric headache, traumatic brain injury, urinary incontinence in males, and vulvar vestibulitis (vulvodynia) to this category.[13] Level 4: Efficacious. This designation requires the satisfaction of six criteria: (a) In a comparison with a no-treatment control group, alternative treatment group, or sham (placebo) control using randomized assignment, the investigational treatment is shown to be statistically significantly superior to the control condition or the investigational treatment is equivalent to a treatment of established efficacy in a study with sufficient power to detect moderate differences. (b) The studies have been conducted with a population treated for a specific problem, for whom inclusion criteria are delineated in a reliable, operationally defined manner. (c) The study used valid and clearly specified outcome measures related to the problem being treated. (d) The data are subjected to appropriate data analysis. (e) The diagnostic and treatment variables and procedures are clearly defined in a manner that permits replication of the study by independent researchers. (f) The superiority or equivalence of the investigational treatment has been shown in at least two independent research settings. Yucha and Montgomery (2008) assigned anxiety, chronic pain, epilepsy, constipation (adult), headache (adult), hypertension, motion sickness, Raynaud's disease, and temporomandibular disorder to this category.[13] Level 5: Efficacious and specific. The investigational treatment must be shown to be statistically superior to credible sham therapy, pill, or alternative bona fide treatment in at least two independent research settings. Yucha and Montgomery (2008) assigned urinary incontinence (females) to this category.[13]

Criticisms In a health care environment that emphasizes cost containment and evidence-based practice, biofeedback and neurofeedback professionals continue to address skepticism in the medical community about the cost-effectiveness and efficacy of their treatments. Critics question how these treatments compare with conventional behavioral and medical interventions on efficacy and cost. The publication of white papers and rigorous evaluation of biofeedback interventions can address these legitimate questions and educate medical professionals, third-party payers, and the public about the value of these services.[122]

Organizations The Association for Applied Psychophysiology and Biofeedback (AAPB) is a non-profit scientific and professional society for biofeedback and neurofeedback. The International Society for Neurofeedback and Research (ISNR) is a non-profit scientific and professional society for neurofeedback. The Biofeedback Foundation of Europe (BFE) [123] sponsors international education, training, and research activities in biofeedback and neurofeedback.[40] The Northeast Regional Biofeedback Association (NRBS) [124] sponsors theme centered educational conferences, political advocacy for biofeedback friendly legislation, and research activities in biofeedback and neurofeedback in the Northeast regions of the United States

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Certification The Biofeedback Certification International Alliance (formerly the Biofeedback Certification Institute of America) is a non-profit organization that is a member of the Institute for Credentialing Excellence (ICE). BCIA offers biofeedback certification, neurofeedback (also called EEG biofeedback) certification, and pelvic muscle dysfunction biofeedback. BCIA certifies individuals who meet education and training standards in biofeedback and neurofeedback and progressively recertifies those who satisfy continuing education requirements. BCIA certification has been endorsed by the Mayo Clinic,[125] the Association for Applied Psychophysiology and Biofeedback (AAPB), the International Society for Neurofeedback and Research (ISNR),[40] and the Washington State Legislature.[126] The BCIA didactic education requirement includes a 48-hour course from a regionally-accredited academic institution or a BCIA-approved training program that covers the complete General Biofeedback Blueprint of Knowledge and study of human anatomy and physiology. The General Biofeedback Blueprint of Knowledge areas include: I. Orientation to Biofeedback, II. Stress, Coping, and Illness, III. Psychophysiological Recording, IV. Surface Electromyographic (SEMG) Applications, V. Autonomic Nervous System (ANS) Applications, VI. Electroencephalographic (EEG) Applications, VII. Adjunctive Interventions, and VIII. Professional Conduct.[127] Applicants may demonstrate their knowledge of human anatomy and physiology by completing a course in human anatomy, human physiology, or human biology provided by a regionally-accredited academic institution or a BCIA-approved training program or by successfully completing an Anatomy and Physiology exam covering the organization of the human body and its systems. Applicants must also document practical skills training that includes 20 contact hours supervised by a BCIA-approved mentor designed to them teach how to apply clinical biofeedback skills through self-regulation training, 50 patient/client sessions, and case conference presentations. Distance learning allows applicants to complete didactic course work over the internet. Distance mentoring trains candidates from their residence or office.[128] They must recertify every 4 years, complete 55 hours of continuing education during each review period or complete the written exam, and attest that their license/credential (or their supervisor’s license/credential) has not been suspended, investigated, or revoked.[129] Pelvic muscle dysfunction Pelvic Muscle Dysfunction Biofeedback (PMDB) encompasses "elimination disorders and chronic pelvic pain syndromes."[130] The BCIA didactic education requirement includes a 28-hour course from a regionally-accredited academic institution or a BCIA-approved training program that covers the complete Pelvic Muscle Dysfunction Biofeedback Blueprint of Knowledge and study of human anatomy and physiology. The Pelvic Muscle Dysfunction Biofeedback areas include: I. Applied Psychophysiology and Biofeedback, II. Pelvic Floor Anatomy, Assessment, and Clinical Procedures, III. Clinical Disorders: Bladder Dysfunction, IV. Clinical Disorders: Bowel Dysfunction, and V. Chronic Pelvic Pain Syndromes. Currently, only licensed health care providers may apply for this certification. Applicants must also document practical skills training that includes a 4-hour practicum/personal training session and 12 contact hours spent with a BCIA-approved mentor designed to teach them how to apply clinical biofeedback skills through 30 patient/client sessions and case conference presentations. They must recertify every 3 years, complete 36 hours of continuing education or complete the written exam, and attest that their license/credential has not been suspended, investigated or revoked.[129] [131]

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History Claude Bernard proposed in 1865 that the body strives to maintain a steady state in the internal environment (milieu intérieur), introducing the concept of homeostasis.[132] In 1885, J.R. Tarchanoff showed that voluntary control of heart rate could be fairly direct (cortical-autonomic) and did not depend on "cheating" by altering breathing rate.[133] In 1901, J. H. Bair studied voluntary control of the retrahens aurem muscle that wiggles the ear, discovering that subjects learned this skill by inhibiting interfering muscles and demonstrating that skeletal muscles are self-regulated.[134] Alexander Graham Bell attempted to teach the deaf to speak through the use of two devices - the phonautograph, created by Édouard-Léon Scott’s, and a manometric flame. The former translated sound vibrations into tracings on smoked glass to show their acoustic waveforms, while the latter allowed sound to be displayed as patterns of light.[135] After World War II, mathematician Norbert Wiener developed cybernetic theory, that proposed that systems are controlled by monitoring their results.[136] The participants at the landmark 1969 conference at the Surfrider Inn in Santa Monica coined the term biofeedback from Weiner's feedback. The conference resulted in the founding of the Bio-Feedback Research Society, which permitted normally isolated researchers to contact and collaborate with each other, as well as popularizing the term “biofeedback.”[137] The work of B.F. Skinner led researchers to apply operant conditioning to biofeedback, decide which responses could be voluntarily controlled and which could not. The effects of the perception of autonomic nervous system activity was initially explored by George Mandler's group in 1958. In 1965, Maia Lisina combined classical and operant conditioning to train subjects to change blood vessel diameter, eliciting and displaying reflexive blood flow changes to teach subjects how to voluntarily control the temperature of their skin.[138] In 1974, H.D. Kimmel trained subjects to sweat using the galvanic skin response.[139] Hinduism: Biofeedback systems have been known in India and some other countries for millennia. Ancient Hindu practices like Yoga and Pranayama (Breathing techniques)are essentially biofeedback methods. Many yogis and sadhus have been known to exercise control over their physiological processes. In addition to recent research on Yoga, Paul Brunton, the British writer who travelled extensively in India, has written about many cases he has witnessed.

Timeline 1958 - G. Mandler's group studied the process of autonomic feedback and its effects.[140] 1962 - D. Shearn used feedback instead of conditioned stimuli to change heart rate.[141] 1962 - Publication of Muscles Alive by John Basmajian and Carlo De Luca[142] 1968 - Annual Veteran's Administration research meeting in Denver that brought together several biofeedback researchers 1969 - April: Conference on Altered States of Consciousness, Council Grove, KS; October: formation and first meeting of the Biofeedback Research Society (BRS), Surfrider Inn, Santa Monica, CA; co-founder Barbara B. Brown becomes the society's first president 1972 - Review and analysis of early biofeedback studies by D. Shearn in the 'Handbook of Psychophysiology'.[143] 1974 - Publication of The Alpha Syllabus: A Handbook of Human EEG Alpha Activity[144] and the first popular book on biofeedback, New Mind, New Body[145] (December), both by Barbara B. Brown 1975 - American Association of Biofeedback Clinicians founded; publication of The Biofeedback Syllabus: A Handbook for the Psychophysiologic Study of Biofeedback by Barbara B. Brown[146] 1976 - BRS renamed the Biofeedback Society of America (BSA) 1977 - Publication of Beyond Biofeedback by Elmer and Alyce Green[69] and Biofeedback: Methods and Procedures in Clinical Practice by George Fuller[147] and Stress and The Art of Biofeedback by Barbara B. Brown[148] 1978 - Publication of Biofeedback: A Survey of the Literature by Francine Butler[149]

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Biofeedback 1979 - Publication of Biofeedback: Principles and Practice for Clinicians by John Basmajian[150] and Mind/Body Integration: Essential Readings in Biofeedback by Erik Peper, Sonia Ancoli, and Michele Quinn[151] 1980 - First national certification examination in biofeedback offered by the Biofeedback Certification Institute of America (BCIA); publication of Biofeedback: Clinical Applications in Behavioral Medicine by David Olton and Aaron Noonberg[152] and Supermind: The Ultimate Energy by Barbara B. Brown[153] 1984 - Publication of Principles and Practice of Stress Management by Woolfolk and Lehrer[154] and Between Health and Illness: New Notions on Stress and the Nature of Well Being by Barbara B. Brown[155] 1987 - Publication of Biofeedback: A Practitioner's Guide by Mark Schwartz[156] 1989 - BSA renamed the Association for Applied Psychophysiology and Biofeedback 1991 - First national certification examination in stress management offered by BCIA 1994 - Brain Wave and EMG sections established within AAPB 1995 - Society for the Study of Neuronal Regulation (SSNR) founded 1996 - Biofeedback Foundation of Europe (BFE) established 1999 - SSNR renamed the Society for Neuronal Regulation (SNR) 2002 - SNR renamed the International Society for Neuronal Regulation (iSNR) 2003 - Publication of The Neurofeedback Book by Thompson and Thompson[157] 2004 - Publication of Evidence-Based Practice in Biofeedback and Neurofeedback by Carolyn Yucha and Christopher Gilbert[158] 2006 - ISNR renamed the International Society for Neurofeedback and Research (ISNR) 2008 - Biofeedback Neurofeedback Alliance formed to pool the resources of the AAPB, BCIA, and ISNR on joint initiatives 2008 - Biofeedback Alliance and Nomenclature Task Force define biofeedback 2009 - The International Society for Neurofeedback & Research defines neurofeedback[159] 2010 - Biofeedback Certification Institute of America renamed the Biofeedback Certification International Alliance (BCIA)

In popular culture • Biofeedback data and biofeedback technology are used by Massimiliano Peretti in a contemporary art environment, the Amigdalae project. This project explores the way in which emotional reactions filter and distort human perception and observation. During the performance, biofeedback medical technology, such as the EEG, body temperature variations, heart rate, and galvanic responses, are used to analyze an audience's emotions while they watch the video art. Using these signals, the music changes so that the consequent sound environment simultaneously mirrors and influences the viewer's emotional state.[160][161] More information is available at the website of the CNRS French National Center of Neural Research [162]. • Charles Wehrenberg implemented competitive-relaxation as a gaming paradigm with the Will Ball Games circa 1973. In the first bio-mechanical versions, comparative GSR inputs monitored each player's relaxation response and moved the Will Ball across a playing field appropriately using stepper motors. In 1984 Wehrenberg programmed the Will Ball games for Apple II computers. The Will Ball game itself is described as pure competitive-relaxation; Brain Ball is a duel between one player's left and right brain hemispheres; Mood Ball is an obstacle-based game; Psycho Dice is a psycho-kinetic game.[163] In 1999 The HeartMath Institute developed an educational system based on heart rhythm measurement and display on a Personal Computer (Windows/Macintosh). Their systems have been copied by many but are still unique in the way they assist people to learn about and self-manage their physiology. A handheld version of their system was released in 2006 and is

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Biofeedback completely portable being the size of a small mobile phone and having rechargeable batteries. With this unit one can move around and go about daily business while gaining feedback about inner psycho-physiological states. • In 2001, the company Journey to Wild Divine began producing biofeedback hardware and software for the Macintosh and Windows operating systems. Third-party and open-source software and games are also available for the Wild Divine hardware. Tetris 64 makes use of biofeedback to adjust the speed of the tetris puzzle game. • David Rosenboom has worked to develop musical instruments that would respond to mental and physiological commands. Playing these instruments can be learned through a process of biofeedback. • In the mid-seventies, an episode of the television series, "The Bionic Woman", featured a doctor who could "heal" himself using biofeedback techniques to communicate to his body and react to stimuli. For example, he could exhibit "super" powers, such as walking on hot coals, by feeling the heat on the sole of his feet and then convincing his body to react by sending large quantities of perspiration to compensate. He could also convince his body to deliver extremely high levels of adrenalin to provide more energy to allow him to run faster and jump higher. When injured, he could slow his heart rate to reduce blood pressure, send extra platelets to aid in clotting a wound, and direct white blood cells to an area to attack infection.[164]

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[126] (http:/ / apps. leg. wa. gov/ WAC/ default. aspx?cite=296-21-280) Washington State Legislature WAC 296-21-280 Biofeedback Rules. [127] Gevirtz, R. (2003). The behavioral health provider in mind-body medicine. In D. Moss, A. McGrady, T. C. Davies, & I. Wickramasekera (Eds.). Handbook of mind-body medicine for primary care. Thousand Oaks, CA: Sage Publications, Inc. [128] De Bease C (2007). "Biofeedback Certification Institute of America certification: Building skills without walls". Biofeedback 35 (2): 48–49. [129] Shaffer, F., & Schwartz, M. S. (in press). Entering the field and assuring competence. In M. S. Schwartz, & F. Andrasik (Eds.). Biofeedback: A practitioner's guide (4th ed.). New York: The Guilford Press. [130] Dickinson T (2006). "BCIA certification for the biofeedback treatment of pelvic floor disorders". Biofeedback 34 (1): 7. [131] Mandler, George; J.M. Mandler & E. T. Uviller (1958). "Autonomic feedback: The perception of autonomic activity". Journal of Abnormal and Social Psychology 56 (3): 367–373. doi:10.1037/h0048083. [132] Bernard C (1957 (1865)). An Introduction to the study of experimental medicine. Mineola, N.Y: Dover. ISBN 0-486-20400-6. [133] Tarchanoff, JR (1885). "[Voluntary acceleration of the heart beat in man]". Pfluger's Archive der gesamten Physiologie 35: 109–135. [134] Bair, JH (1901). "Development of voluntary control" (http:/ / bjp. rcpsych. org/ cgi/ pdf_extract/ 48/ 200/ 158). Psychological Review 8 (5): 474–510. doi:10.1037/h0074157. . [135] Bruce, Robert C. (1990). Bell: Alexander Graham Bell and the conquest of solitude. Ithaca, N.Y: Cornell University Press. ISBN 0-8014-9691-8. [136] Wiener, Norbert (2007). Cybernetics Or Control And Communication In The Animal And The Machine. Kessinger Publishing, LLC. ISBN 1-4325-9444-3. [137] Moss D (1999). "Biofeedback, mind-body medicine, and the higher limits of human nature". Humanistic and transpersonal psychology: a historical and biographical sourcebook. Westport, Conn: Greenwood Press. ISBN 0-313-29158-6. [138] Lisina MI (1965). "The role of orientation in the transformation of involuntary reactions into voluntary ones". In Voronin IG; Leontiev AN; Luria AR; Sokolov EN & Vinogradova OB. Orienting reflex and exploratory behavior. Washington, DC: American Institute of Biological Studies. pp. 339–44. [139] Kimmel HD (May 1974). "Instrumental conditioning of autonomically mediated responses in human beings". Am Psychol 29 (5): 325–35. doi:10.1037/h0037621. PMID 4847492. [140] Mandler, G, Mandler, JM, and Uviller, ET. Autonomic feedback: The perception of autonomic activity. Journal of Abnormal and Social Psychology. 1958, pp.56, 367-373. [141] Shearn, DW. Operant conditioning of heart rate. Science, 1962, 137, 530-531. [142] Basmajian, J.V., De Luca, C.J., Muscles Alive: Their Functions Revealed by Electromyography, Williams & Wilkins, Baltimore: 1962 [143] Shearn, D.W. "Operant analysis in psychophysiology" in Greenfield, N.S. and Sternbach, R.A., eds., Handbook of Psychophysiology, Holt, Rinehart and Winston, New York: 1972 [144] Brown, B.B. The Alpha Syllabus: A Handbook of Human EEG Alpha Activity, Charles C. Thomas Publisher, Ltd., Springfield, IL: 1974 [145] Brown, B.B. New Mind, New Body: Bio-feedback - New Directions for the Mind, Harper & Row, New York: 1974; paperback edition by Bantam Books, 1975 [146] Brown, B.B. The Biofeedback Syllabus: A Handbook for the Psychophysiologic Study of Biofeedback, Charles C. Thomas Publisher, Ltd., Springfield, IL: 1975 [147] Fuller, G. D., Biofeedback: Methods and Procedures in Clinical Practice, Biofeedback Institute of San Francisco, San Francisco: 1977 [148] Brown, B.B. Stress and The Art of Biofeedback, Harper & Row, New York: 1977 [149] Butler, F., Biofeedback: A survey of the literature, IFI/Plenum Data Company, New York: 1978 [150] Basmajian, J. V. (Ed.). (1979). Biofeedback: Principles and practice for clinicians. Baltimore: Williams & Wilkins. [151] Peper, E., Ancoli, S., and Quinn, M., eds., Mind/Body integration: Essential Readings in Biofeedback, Plenum Press, New York: 1979 [152] Olton, D. S., Noonberg, A. R., Biofeedback: Clinical Applications in Behavioral Medicine, Prentice-Hall, Inc., Englewood Cliffs, NJ: 1980 [153] Brown, B.B. Supermind: The Ultimate Energy, Harper & Row, New York: 1980; paperback edition by Bantam Books, 1983 [154] Woolfolk, R. L., Lehrer, P. M., Principles and practice of stress management, The Guilford Press, New York: 1984 [155] Brown, B.B. Between Health and Illness: New Notions on Stress and the Nature of Well Being, Houghton Mifflin, New York: 1984; paperback edition by Bantam Books, 1985 [156] Schwartz, M., ed., Biofeedback: A practitioner's guide, The Guilford Press, New York: 1987

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[157] Thompson, M. and Thompson, L. The neurofeedback book: An introduction to basic concepts in applied psychophysiology, The Association for Applied Psychophysiology and Biofeedback, Wheat Ridge, CO: 2003 [158] Yucha, C., and Gilbert, C. Evidence-based practice in biofeedback and neurofeedback, The Association for Applied Psychophysiology and Biofeedback, Wheat Ridge, CO: 2004 [159] Biofeedback tutor, Biosource Software, Kirksville, MO: 2010 [160] "under changes" (http:/ / www. kontinuita. com/ ). Kontinuita.com. . Retrieved 2012-01-09. [161] "Scope New York Home" (http:/ / www. scope-art. com/ index. php?option=com_content& task=view& id=102& Itemid=206). Scope-art.com. . Retrieved 2012-01-09. [162] http:/ / cogimage. dsi. cnrs. fr/ seminaires/ resumes/ resume_amygdalae_2005. htm [163] Charles Wehrenberg Will Ball, Solo Zone, San Francisco, 1995/2001 ISBN 1-886163-02-2 [164] http:/ / www. imdb. com/ title/ tt0526163/

External links • • • • •

Biofeedback (http://www.dmoz.org/Health/Alternative/Biofeedback//) at the Open Directory Project Association for Applied Psychophysiology and Biofeedback (AAPB) (http://www.aapb.org/) Biofeedback Certification Institute of America (BCIA) (http://www.bcia.org/) Biofeedback Foundation of Europe (BFE) (http://www.bfe.org/) Biofeedback groups may also be found in all the social media for discussion and information.

• A number of regional groups which are offshoots of the AAPB can be accessed online.

Dreamachine The dreamachine (or dream machine) is a stroboscopic flicker device that produces visual stimuli. Artist Brion Gysin and William S. Burroughs's "systems adviser" Ian Sommerville created the dreamachine after reading William Grey Walter's book, The Living Brain.[1][2]

History In its original form, a dreamachine is made from a cylinder with slits cut in the sides. The cylinder is placed on a record turntable and rotated at 78 or 45 revolutions per minute. A light bulb is suspended in the center of the cylinder and the rotation speed allows the light to come out from the holes at a constant frequency of between 8 and 13 pulses per second. This frequency range corresponds to alpha waves, electrical oscillations normally present in the human brain while relaxing.[2] The Dreamachine is the subject of the National Film Board of Canada 2008 feature documentary film FLicKeR by Nik Sheehan.[3]

Homemade dreamachine at rest (not spinning), lit internally.

Dreamachine

Use A dreamachine is "viewed" with the eyes closed: the pulsating light stimulates the optical nerve and alters the brain's electrical oscillations. The user experiences increasingly bright, complex patterns of color behind their closed eyelids. The patterns become shapes and symbols, swirling around, until the user feels surrounded by colors. It is claimed that using a dreamachine allows one to enter a hypnagogic state.[4] This experience may sometimes be quite intense, but to escape from it, one needs only to open one's eyes.[1] A dreamachine may be dangerous for people with photosensitive epilepsy or other nervous disorders. It is thought that one out of 10,000 adults will experience a seizure while viewing the device; about twice as many children will have a similar ill effect.[5]

Notes [1] Cecil, Paul (March 2000). "Everything is Permuted" (http:/ / www. permuted. org. uk/ dream1. htm). Flickers of the Dreamachine. . Retrieved 2007-03-27. [2] Century, Dan (December 2000). "Brion Gysin and his Wonderful Dreamachine" (http:/ / www. legendsmagazine. net/ 105/ brion. htm). Legends Magazine. . Retrieved 2007-03-27. [3] Film Web site (http:/ / www. flickerflicker. com) [4] Kerekes, David (2003). Headpress 25: William Burroughs & the Flicker Machine. Headpress. p. 13. ISBN 1-900486-26-1. [5] Allen, Mark (2005-01-20). "Décor by Timothy Leary" (http:/ / www. nytimes. com/ 2005/ 01/ 20/ garden/ 20mach. html?ex=1264050000& en=2ead60550b324624& ei=5088& partner=rssnyt). The New York Times. . Retrieved 2007-03-27.

References • Cecil, Paul. (2000). Flickers Of The Dreamachine (http://www.permuted.org.uk/Flickers.htm). ISBN 1-899598-03-0 Download excerpts (http://www.permuted.org.uk/dmpdown.htm)

Further reading • McKenzie, Andrew M. (1989). "The Hafler Trio & Thee Temple Ov Psychick Youth - Present Brion Gysin's Dreamachine" (http://www.discogs.com/release/582394). Belgium: KK records. Retrieved 2010-10-21. • Cecil, Paul (1996). Flickers of the Dreamachine (http://www.permuted.org.uk/Flickers.htm). ISBN 1-899598-03-0. • Geiger, John (2003). The Chapel of Extreme Experience: A Short History of Stroboscopic Light and the Dream Machine (http://softskull.com/detailedbook.php?isbn=1-932360-01-8). ISBN 1-932360-01-8. • Vale, V (1982). Re-Search: William S. Burroughs, Brion Gysin, Throbbing Gristle (http://www.researchpubs. com/Blog/?page_id=13&product_id=54). ISBN 0-940642-05-0. • Gysin, Brion (1992). Dreamachine Plans (http://www.permuted.org.uk/dmplan.htm). ISBN 1-871744-50-4.

External links • Dreamachine exhibition at Cabaret Voltaire (birthplace of Dada), Zürich (http://www.cabaretvoltaire.ch/ ausstellung.php?ID=31&modus=archive) • Dreamachine exhibition at Freud's Dreams Museum, St. Petersburg (Russia) (http://freud.ru/) • Subtleart Dr.Benways Simulacrum, Dreamachine Replica, Audiovisual installation, Collaborative project: Subtleart, New World Revolution and Kito, 2009 (http://cargocollective.com/rudolfamaral#1934691/ dr-benways-dreamachine/) • (French) Interzone: Dreamachine - Machine à rêver (http://www.inter-zone.org/dm.html) • FLicKeR Film Review (http://www.flickerflicker.com) • (http://dreamachine.ca/) • JavaScript Dreamachine (http://www.netliberty.net/dreamachine.html)

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Mind machine A mind machine (aka brain machine, in some countries called a psychowalkman) uses pulsing rhythmic sound and/or flashing light to alter the brainwave frequency of the user.[1] Mind machines are said to induce deep states of relaxation, concentration, and in some cases altered states of consciousness that have been compared to those obtained from meditation and shamanic exploration. The process applied by these machines is also known as brainwave synchronisation or entrainment. Mind machines work by creating a A mind machine with headphones and strobe light goggles. flickering ganzfeld. Since a flickering ganzfeld produces different effects from a static one, mind machines can often also produce a static ganzfeld. [2] A mind machine is similar to a dreamachine in that both produce a flickering ganzfeld. The difference is that a dreamachine can be used by several people at once, but generally has less technical features than a mind machine.

Overview Mind machines typically consist of a control unit, a pair of headphones and/or strobe light goggles. The unit controls the sessions and drives the LEDs in the goggles. Professionally, they are usually referred to as Auditory Visual Stimulation Devices (AVS devices). Sessions will typically aim at directing the average brainwave frequency from a high level to a lower level by ramping down in several sequences. Target frequencies typically correspond to delta (1-3 hertz), theta (4–7 Hz), alpha (8–12 Hz) or beta brain waves (13–40 Hz), and can be adjusted by the user based on the desired effects. There have been a number of claims regarding binaural beats, among them that they may help people memorize and learn, stop smoking, tackle erectile dysfunction and improve athletic performance. Scientific research into binaural beats is very limited. No conclusive studies have been released to support the wilder claims listed above. Mind machines are often used together with biofeedback or neurofeedback equipment in order to adjust the frequency on the fly.[3] Modern mind machines can connect to the Internet to update the software and download new sessions. When sessions are used in conjunction with meditation, neurofeedback, etc. the effect can be amplified. Some clinical research has been done on the use of auditory and visual stimulation to improve cognitive abilities in learning-disabled children (research) [4].

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Safety Rapidly flashing lights may be dangerous for people with photosensitive epilepsy or other nervous disorders. It is thought that one out of 10,000 adults will experience a seizure while viewing such a device; about twice as many children will have a similar ill effect.[5]

References [1] The Use of Auditory and Visual Stimulation for the Treatment of Attention Deficit Hyperactivity Disorder in Children (http:/ / www. neuromedicstechnology. com/ Library/ final69. pdf). Micheletti, Larry S. Doctoral Dissertation, University of Houston, Houston, Texas [2] Wackermann, Jirˇı´ (2008). "Ganzfeld-induced hallucinatory experience, its phenomenology and cerebral electrophysiology" (http:/ / www. efectoganzfeld. com/ uploads/ 5/ 3/ 0/ 3/ 5303662/ ganzfeld. pdf). Cortex 44 (2008) 1364 – 1378. Elsevier. . [3] Mind machines together with online gsr biofeedback (http:/ / www. happy-electronics. eu/ products/ online-biofeedback/ ?lang=en). Happy Electronics [4] http:/ / proquest. umi. com/ pqdlink?Ver=1& Exp=10-13-2012& FMT=7& DID=766101161& RQT=309& attempt=1 [5] Allen, Mark (2005-01-20). "Décor by Timothy Leary" (http:/ / www. nytimes. com/ 2005/ 01/ 20/ garden/ 20mach. html?ex=1264050000& en=2ead60550b324624& ei=5088& partner=rssnyt). The New York Times. . Retrieved 2007-03-27.

Literature • Mind Machine FAQ (http://www.realization.org/page/doc0/doc0036.htm) by J.Brad Hicks Dead link 2012-Dec-18

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Article Sources and Contributors

Article Sources and Contributors Brainwave entrainment  Source: http://en.wikipedia.org/w/index.php?oldid=528655771  Contributors: 2over0, A. di M., Aaron Kauppi, AbsolutDan, Adamantios, Aesir.le, Akerans, Allens, Anarchist42, Army1987, Beccare, Bendroz, Betacommand, Bigjoestalin, Binksternet, Bobsterz, Bonni, CJLL Wright, Clicketyclack, CommonsDelinker, Crommo, Crystaln1, Cst17, DGG, DPic, Dabuek, Deiz, Dethme0w, Deville, Discospinster, Diza, DomQ, Dreamachine.flicker, Dubious Irony, Duck1dong, Eeekster, Elonka, Entheta, Everything Else Is Taken, Fluffy89, Gadfium, Gatewaycat, GeoffreyMay, Grace777, GraemeL, Gregherman713, Hifromeddie, Hu, Hyacinth, IPSOS, J jeg, JIP, Jaberwocky6669, Jimbo787, Jojalozzo, JonathanFreed, JustAGal, K2709, Khazar, Knowledgy, Kobitate94, KrakatoaKatie, Kxra, Leelahcat, Lexicon, LittleHow, Looie496, MER-C, Macsters, MastCell, Matt Crypto, Mattisse, MaveenOlam, Mdwh, Megawiki, MikeRM, Mild Bill Hiccup, Muraridas108, Nasnema, Ninly, OS2Warp, Ohnoitsjamie, Pearle, Piano non troppo, R'n'B, Redheylin, Redhorseby, Riggr Mortis, Rocky4821, Ronz, Sarkar112, Shaktigirl, Sharkwelder, Simbamford, Sintaku, SpecMode, Sticky Parkin, Thumperward, Tompsci, TribeOne, Tronno, Wavedoc1, Wernervb, Wonderwaterhorse, Yakushima, Yeshua2000, Yin xing, Ynhockey, 228 anonymous edits Audio–visual entrainment  Source: http://en.wikipedia.org/w/index.php?oldid=511906109  Contributors: Aaron Kauppi, Amandaj16, Cffrost, ClintGoss, Colonies Chris, DPic, Dicklyon, Eekerz, GeeWilson, Geke, Magioladitis, Mandarax, Nono64, Pamiki, Pjoef, Rich Farmbrough, SchreiberBike, StAnselm, The Anome, 3 anonymous edits Binaural beats  Source: http://en.wikipedia.org/w/index.php?oldid=527331015  Contributors: Aaronbrick, Acousticsheep, Adamantios, Aitias, Algae, All Is One, Allen4names, Andres, AnmaFinotera, Army1987, AtomicCentury, Autarch, AySz88, Bartel02, Beccare, Bella7657, BigrTex, Biotics, Brianski, BrownBean, Buzzardprey, C6541, Can't sleep, clown will eat me, Chaldor, Charlie 1959, Cloistermaximus, Corpx, Cprompt, Crystaln1, Cuulcars, Cxz111, Cypher-neo, DBigXray, Daniel58, DanielRigal, DaveSeidel, David Gerard, Davidhorman, Dbachmann, Dbmoodb, Deltabeignet, Destynova, Deville, Dextrose, Dilvie, Ding, Diogeneselcinico42, Discospinster, Diza, Doctorfluffy, Dori, Dr Backlund, DrChimple, Dragonvoices, Drilnoth, Duck1dong, Dysepsion, Esowteric, Everything Else Is Taken, Execvator, Exercisephys, Eyu100, Fabometric, Fe13ar, Firsfron, Flowanda, Focomoso, Furrykef, Fæ, GB fan, Getkaizer, Gibbsman, Gnubbolo, GoodThought101, GraemeL, Gregplancich, Guoguo12, Headbomb, Heron, Hifromeddie, HouseofLeaves83, Hu12, Hughcharlesparker, Husky, Hyacinth, Hypersigil, Ian.thomson, Ihaveabutt, Inductiveload, Ingolfson, InverseHypercube, J04n, Jagra, Jeraphine Gryphon, Jerde, Jezmck, Jibjibjib, Jim1138, Joecaggi, Jsharpminor, K2709, Kairologic, Kerriz0r, King of Hearts, Knowledgy, Kroeran, Kxra, Kylemew, LeadSongDog, Leelahcat, Lightandsound1221, Little Professor, Lumos3, Luna Santin, MER-C, Ma8thew, Maclir2001, Macsters, Madman, Materialscientist, Mentifisto, Michael Hardy, Mike racer, Mild Bill Hiccup, Millermk, MindTrainingOnline, Mindhush, Mistercow, Monkeyman, Mr Clean, Nacho Insular, Napwell, Natmaka, Neitherday, Neurogeek, Nickleus, Nonstopdrivel, Nubz0r, Ocaasi, Ohnoitsjamie, Omegatron, Orcasha, PStrait, Penwhale, Phlegat, Pjacobi, Postglock, Pweemeeuw, Quale, Quaristice, Quiddity, R'n'B, R3m0t, RJHall, Random user 39849958, RattusMaximus, Ravindranathakila, Rebroad, Reedy, Rich Farmbrough, Rjwilmsi, Robin S, Ronz, Runtime, SPECVLVMSINCERVS, SamClayton, Saml214, Scapler, Scipio Xaos, Scirocco6, Seraphimblade, Sesu Prime, Shaktigirl, Skyhead E, Sleddog116, Soundonmind, SpecMode, Squirrel-monkey, Sruti s, Statikeffeck, Stephan Leeds, StevenAitchison, SudoGhost, Sukmein, Supten, SweetNightmares, Taraborn, TastyPoutine, Terrx, Tevildo, The Evil IP address, Thegiorgio, Travza, Tronno, Twerty, Ubzy, Unused0025, Vary, Vidkun, Wernervb, Westherm, Whalbra08, Whatsinim, WikiDan61, Wikidan829, Wimt, Woodega, Xanzzibar, Yeshua2000, Zarnivop, Zoz, Åkebråke, 424 anonymous edits Isochronic tones  Source: http://en.wikipedia.org/w/index.php?oldid=523164083  Contributors: Akerans, Arda Xi, Bearcat, DPic, DVdm, Djpeters, DoctorKubla, GoingBatty, Johnscheer, Jonjune, Loutherbourg, MC10, Malcolma, Patrixpax, Pkbooo, SpecMode, Woohookitty, 10 anonymous edits Electroencephalography  Source: http://en.wikipedia.org/w/index.php?oldid=528942739  Contributors: A.Warner, A314268, AFLastra, AGToth, AVJP619, AbsolutDan, Adnan niazi, Alex Spade, Alonker, Alphalobe, Andycjp, Anthonyhcole, Aorwing, Arcadian, Ariangiovanni, Army1987, Arthena, Asbestos, Ashdurbat, Ashishbhatnagar72, Ask123, Axfangli, Back ache, Balizarde, Banes, Barrylb, Beefnut, Beland, BenKovitz, Berserkerus, Bewildebeast, Beyondsquirrelly, Bk0, Bobrayner, BrandonSargent, BrightStarSky, Brighterorange, Bruce4949, Btait101, CBM, Caesura, Callumny, Calvin 1998, Cambyzez nl, Can't sleep, clown will eat me, CanadianCaesar, CanisRufus, Capricorn42, Cburnett, Celebere, Ceyockey, CharlotteWebb, Chephyr, Chirality, Ciphers, CliffC, Clngre, Cntras, Codeczero, Colapeninsula, Colin, Corinne68, Correogsk, Cp72, Cronides2, D'Agosta, D.Right, DabMachine, Dancter, Darkcharmr, Datahaki, Davecrosby uk, Debresser, Deele, Delldot on a public computer, Deodar, DerHexer, Dfrankow, Dger, Dimo400, Ding, DocWatson42, DoctorDog, Dontaskme, DopefishJustin, Dranorter, EBlack, EastTN, EeepEeep, Eequor, Eleassar, Elenaschifirnet, Emptymountains, Endoran, Enirpmet, Epbr123, Esprit15d, Extransit, Eykanal, F Woodruff, Fabrictramp, Facts707, Fchapotot, Fernandopestana, FiachraByrne, Fieldday-sunday, Filip em, Fnielsen, Former user, Forteanajones, Fyyer, Geenah, Geenah71, GeoffreyMay, Giftlite, Giraffedata, Glacialfox, Gleng, Glogger, Goethean, GoingBatty, Goldkingtut5, Gronky, Gyro2222, Hans Dunkelberg, Hbent, Headbomb, Heah, Heron, Hewn, Hgamboa, Hoof Hearted, Hu, II MusLiM HyBRiD II, Ilphin, 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Njyoder, Notreallydavid, Odissea, Omegatron, PSYBIRD1, Paiamshadi, Peter Chastain, Piet Delport, Pingveno, Pjacobi, Plasticup, Poorman1, Psydoc, Radomil, Rama, Rcsprinter123, Redheylin, Redhorseby, Rena Silverman, Rhombus, Riana, River2012wiki, Rjanag, Rjwilmsi, Roberrific, Robert K S, Robertmabell, Rolando, Ronz, RoyBoy, Rror, Rsabbatini, Rsrikanth05, Rstdenis, Rvanschaik, Ryan.rakib, Savie Kumara, Sayeth, Scareccrow, Sceptre, Schulze-bonhage, Scrane72, Shantavira, Shooravi, Shwmtpf, Simbamford, Simbven, Simoneau, Sineenuchn, SiobhanHansa, Sirmikey, Sjschen, Snowolf, Solzhenitsyn1, Sonicandfffan, Sonjaaa, Spiritia, Sribulusu, Srujan1001, Staticshakedown, Steve Quinn, Stratocracy, Sun Creator, Supten, TakuyaMurata, TangLab, Tatterfly, Taylorchas, Teemu08, Tekhnofiend, Temporaluser, Th1alb, The Evil IP address, The Thing That Should Not Be, Theo177, Thibbs, Thuglas, Tikiwont, Timokeefe, TjeerdB, Tobby72, Tonyfaull, Trusilver, Tryptofish, Twinsday, TwoTwoHello, Uhhhhhno, Urness.sam, Veronica Roberts, Virtualerian, Vssun, Vuo, Vyroglyph, Wavelength, Welpeo, Whelanrobwiki, Wikifarzin, Wknight94, Wojder, XApple, XXLOLDAXx, Xetrov, Xieliwei, Yanglifu90, Ylwarrior, Zefryl, Zeraeph, Zeuszeus1122, Zsinj, Δ, Σ, Милан Јелисавчић, 541 anonymous edits Thalamus  Source: http://en.wikipedia.org/w/index.php?oldid=525387679  Contributors: 10k, @pple, A314268, Alansohn, Alex.tan, AlexDitto, Anatomist90, Ancheta Wis, Arcadian, Argumzio, AxelBoldt, B44H, Babajobu, Bayle Shanks, Beno1000, Bird, Bobo192, Bortain, Brain-mapper, Brendanconway, Bryan Derksen, Caco de vidro, Cacycle, Carfro, Casliber, Catgut, Cerebral, Chanting Fox, Chris Capoccia, Christophersnelson, CopperKettle, Dan Polansky, DanielCD, Danntm, Dede2008, Delldot, Dgwingert, Diberri, Discospinster, Dmdstudent, Douzzer, Dppowell, Drift chambers, Emperorbma, Enlightenmentreloaded, Ericbateson, Everyking, Fallschirmjäger, Fifo, Fireice, Fnielsen, Freddyd945, Frenkmelk, FrozenMan, Gaius Cornelius, Gerard.percheron, Gleng, 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Heresiarch, Wouldeven9, Xiutwel-0003, Xwdl, Youssefsan, Éder Santos, 309 anonymous edits Delta wave  Source: http://en.wikipedia.org/w/index.php?oldid=523538361  Contributors: A314268, AGToth, Anton Summers, Argumzio, Balloonguy, Ben Ben, BirdValiant, Boud, Chirality, Christinagraves, Cronides2, CyclePat, Dansiman, Djsuess, EagleFan, Erik212, Eykanal, Former user, Forteanajones, Friggida, Galatea151, Gene Nygaard, GregorB, Helge Skjeveland, Hgamboa, Hordaland, Hyacinth, Icweiner, JTiago, Javsav, Jkanters, Jonsafari, K.murphy, Kauczuk, LilHelpa, Linnell, Luke poa, Michael Hardy, Moleskiner, MrSandman, Muntfish, Nk.sheridan, Nomen Nescio, Ost316, Persona13, Qbert203, Rabend, Radagast83, Rjwilmsi, Ronark, Sayeth, Sbeath, Serpentspine, SidP, Splash, Sprachpfleger, The Light6, Theskuj, Tinlv7, TjeerdB, Tokuretsu, Tryptofish, Tycho, WLU, XApple, ZZninepluralZalpha, 44 anonymous edits Theta rhythm  Source: http://en.wikipedia.org/w/index.php?oldid=523538396  Contributors: A. di M., A314268, AGToth, Argumzio, Armarshall, Benhocking, Chris the speller, Clicketyclack, David Woodward, Digfarenough, Discospinster, Djsuess, Down10, Edgar181, Eequor, Erik212, Extransit, Fulara, Gleng, Grey Geezer, Hgamboa, Ianvitro, ImAlsoGreg, JimD, Jonsafari, K.murphy, Kliph, Looie496, Male1979, Mbmaciver, Mgiganteus1, Mike Rosoft, Nickketz, No Parking, Pearle, Rich Farmbrough, Rjalex, Rjwilmsi, Scriberius, Skyfex, Splash, TjeerdB, Tryptofish, UberScienceNerd, Uchaschiysya, Wikitavanti, XApple, Xetxo, 50 anonymous edits Alpha wave  Source: http://en.wikipedia.org/w/index.php?oldid=529261047  Contributors: 2over0, A314268, AGToth, Ali ringo, Anonywiki, Arcadian, Argumzio, Brighterorange, Chirality, Chris the speller, Cpastern, Djsuess, DreamGuy, Exabyte, Giftlite, Gilliam, Giraffedata, Gtg849v, Hgamboa, Hu, Jmh649, Jonsafari, Koavf, Lighthead, LittleHow, Michael Hardy, Michael Tangermann, Mwanner, NickelShoe, OlEnglish, Ombudsman, Pamyan, Personplacething, PuercoPop, Rend, Rjwilmsi, ShelleyAdams, Skyfex, Soulfulscience, Supten, Tekhnofiend, Telekenesis, Thibbs, TjeerdB, Tomiko72, Topbanana, Trusilver, Tryptofish, Twoe gappes, Ward20, Whatever404, 65 anonymous edits Beta wave  Source: http://en.wikipedia.org/w/index.php?oldid=523538278  Contributors: A314268, AGToth, Argumzio, Army1987, BenKovitz, Benbest, Chirality, Clayoquot, Djsuess, Drilnoth, Erik212, Heah, Hgamboa, JimVC3, Jonsafari, LittleHow, Mentifisto, Monito, NCurse, Prometheuspan, Rage, Rayman356307, Rcsprinter123, Rjwilmsi, Sagan, Skyfex, Solace098, Sotcr, Splash, Tekhnofiend, Timberframe, TjeerdB, Tryptofish, WhatamIdoing, Whatever404, Wikitavanti, Wojder, XApple, Zachorious, 32 anonymous edits Gamma wave  Source: http://en.wikipedia.org/w/index.php?oldid=523538319  Contributors: A314268, AGToth, Aaron Brenneman, Adinsmoor, AdjustShift, Anna Lincoln, Antaeus Feldspar, Argumzio, Ask123, Astral, Benny-bo-bop, Bigmantonyd, Blue520, Bluebeam, Boud, Bsadowski1, Charles Matthews, Cheater512, Chirality, CopperKettle, Courcelles, Cycletime, DJM77bci, Dendre, Denisarona, Derheldt, Destynova, Divespluto, Djsuess, Doczilla, Dontaskme, Eolai1, Epolk, Erik212, Ewlyahoocom, Forteanajones, Fratrep, Gaius Cornelius, Gene Nygaard,

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Wikielwikingo, Zacharybarry, Åkebråke, 81 anonymous edits Sensorimotor rhythm  Source: http://en.wikipedia.org/w/index.php?oldid=523538467  Contributors: A314268, AnthraxMan, ArchonMD, Clicketyclack, ClintGoss, Collin Stocks, Djsuess, Eitt, Eubulides, Hgamboa, Jonsafari, Ktr101, LittleHow, Lova Falk, Mckeuken, Michael Tangermann, Ophion, Ost316, Rjwilmsi, Rl, Scottalter, TjeerdB, Tranhungnghiep, Tryptofish, Ttsuchi, Zbisasimone, 21 anonymous edits Sleep spindle  Source: http://en.wikipedia.org/w/index.php?oldid=523538444  Contributors: Aaron Kauppi, Antonio Lopez, Benandorsqueaks, Bobianite, Bwrs, Darktremor, DickAB, Discospinster, Eliptis, Galatea151, Ihope127, Ijustam, Iqzaquezzs, ItsWoody, Jonsafari, Jstedehouder, Kayemmdee, Kstrojny, Mesonym, Mistral2099, Mycroft7, NSR, Neocadre, Niluop, Retpyrc, RichardF, Simbven, TjeerdB, Toferscd, 26 anonymous edits Biofeedback  Source: http://en.wikipedia.org/w/index.php?oldid=527668673  Contributors: ***Ria777, 0x6D667061, ASK, AThing, 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ValDudko, WLU, Wo0dstock79, Wolfrock, Woohookitty, Xtian, 240 anonymous edits Dreamachine  Source: http://en.wikipedia.org/w/index.php?oldid=526410524  Contributors: ***Ria777, 5 albert square, Adamantios, Akseli.palen, BesigedB, Betacommand, Boson, C1k3, Clem Snide, Cottonsgardens, D thing, Dbeiler, Deiz, Devein, Dglenn157, Disinfectantrum, Dreamachine.flicker, Elamita, Evb-wiki, Garethspor, Gwalla, Holzmanj9, I dream of horses, Jeff Silvers, JensAlfke, Jerzy, Lagoona, Luna Santin, MaveenOlam, Maxronnersjo, McGeddon, Mitsukai, Niksheehan, Nof20, Permuted, Pete4512, Qmwpeto, Riefenstahl, Robert551, Ronz, Shadow box, Shawn in Montreal, Sherefong, SimonP, Soniclife89, Sparkit, The despot, TheOldJacobite, Timothyreal, Tregoweth, Trivialist, Useight, Viriditas, Will Beback, Zafiroblue05, Шизомби, 106 anonymous edits Mind machine  Source: http://en.wikipedia.org/w/index.php?oldid=528764801  Contributors: AbsolutDan, Aenar, Amandaj16, Aragorn23, Army1987, B7T, Birdseed101, Crystallina, Devil Master, Dreamachine.flicker, Frap, Gene Nygaard, GraemeL, Hanslicht, JensAlfke, Jimbo787, Jonathan Harford, Jondel, K2709, KrakatoaKatie, MichaelWeed, Mrule7404, Pierre-Alain Gouanvic, Pjacobi, Radiant!, Rajah, Redhorseby, RichardF, Ronz, SimonP, Sophertiti, TedE, Thisglad, TimofKingsland, Trivialist, Tronno, We R One, Yeshua2000, Zachorious, Zahid Abdassabur, ŠJů, 61 anonymous edits

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Image Sources, Licenses and Contributors

Image Sources, Licenses and Contributors File:Binaural beats.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Binaural_beats.svg  License: Creative Commons Attribution-Sharealike 3.0  Contributors: DPic File:Monaural.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Monaural.svg  License: Creative Commons Attribution-Sharealike 3.0  Contributors: DPic File:Isochronic-toes.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Isochronic-toes.svg  License: Creative Commons Attribution-Sharealike 3.0  Contributors: DPic File:Acoustics BinauralBeats.JPG  Source: http://en.wikipedia.org/w/index.php?title=File:Acoustics_BinauralBeats.JPG  License: Public Domain  Contributors: Skyhead E File:EEG_cap.jpg  Source: http://en.wikipedia.org/w/index.php?title=File:EEG_cap.jpg  License: Public Domain  Contributors: Original uploader was Thuglas at en.wikipedia File:Spike-waves.png  Source: http://en.wikipedia.org/w/index.php?title=File:Spike-waves.png  License: Creative Commons Attribution-Sharealike 2.0  Contributors: Der Lange, Lipothymia, Magnus Manske, NEUROtiker, Str4nd, 3 anonymous edits File:1st-eeg.png  Source: http://en.wikipedia.org/w/index.php?title=File:1st-eeg.png  License: Public Domain  Contributors: Hans Berger File:Electroencephalograph Neurovisor-BMM 40 (close view).jpg  Source: http://en.wikipedia.org/w/index.php?title=File:Electroencephalograph_Neurovisor-BMM_40_(close_view).jpg  License: Creative Commons Attribution-Sharealike 3.0,2.5,2.0,1.0  Contributors: Юрий Петрович Маслобоев / Yury Petrovich Masloboev File:eeg raw.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Eeg_raw.svg  License: GNU Free Documentation License  Contributors: Denniss, Hgamboa, Lipothymia, Magnus Manske, NEUROtiker File:eeg delta.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Eeg_delta.svg  License: GNU Free Documentation License  Contributors: Hugo Gamboa File:eeg theta.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Eeg_theta.svg  License: GNU Free Documentation License  Contributors: Hugo Gamboa File:eeg alpha.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Eeg_alpha.svg  License: GNU Free Documentation License  Contributors: Hugo Gamboa File:eeg SMR.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Eeg_SMR.svg  License: GNU Free Documentation License  Contributors: Denniss, Hgamboa, Lipothymia, Magnus Manske, NEUROtiker File:eeg beta.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Eeg_beta.svg  License: GNU Free Documentation License  Contributors: Hugo Gamboa File:eeg gamma.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Eeg_gamma.svg  License: GNU Free Documentation License  Contributors: Hugo Gambo File:HansBerger Univ Jena.jpeg  Source: http://en.wikipedia.org/w/index.php?title=File:HansBerger_Univ_Jena.jpeg  License: Public Domain  Contributors: Jumbolino Image:EEG mit 32 Electroden.jpg  Source: http://en.wikipedia.org/w/index.php?title=File:EEG_mit_32_Electroden.jpg  License: Creative Commons Attribution-Sharealike 2.5  Contributors: Aschoeke Image:Vitasport3_2.jpg  Source: http://en.wikipedia.org/w/index.php?title=File:Vitasport3_2.jpg  License: Creative Commons Attribution-Sharealike 2.5  Contributors: KH251, Rama, 1 anonymous edits Image:Musical brainwave performance at deconism gallery.jpg  Source: http://en.wikipedia.org/w/index.php?title=File:Musical_brainwave_performance_at_deconism_gallery.jpg  License: GNU Free Documentation License  Contributors: AndreasPraefcke, Hyacinth, Shoulder-synth, The cat file:Brain_chrischan thalamus.jpg  Source: http://en.wikipedia.org/w/index.php?title=File:Brain_chrischan_thalamus.jpg  License: GNU Free Documentation License  Contributors: AxelBoldt, Nevit, Petrus Adamus, Túrelio, Was a bee, Wikiborg, 1 anonymous edits file:Thalamusanterolateral.jpg  Source: http://en.wikipedia.org/w/index.php?title=File:Thalamusanterolateral.jpg  License: Creative Commons Attribution 3.0  Contributors: Ben Brahim Mohammed Image:Thalamus small.gif  Source: http://en.wikipedia.org/w/index.php?title=File:Thalamus_small.gif  License: unknown  Contributors: Images are generated by Life Science Databases(LSDB). Image:Thalmus.png  Source: http://en.wikipedia.org/w/index.php?title=File:Thalmus.png  License: Creative Commons Attribution-Sharealike 3.0  Contributors: Madhero88 Image:Spinal cord tracts - English.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Spinal_cord_tracts_-_English.svg  License: Creative Commons Attribution-Sharealike 3.0  Contributors: Polarlys and Mikael Häggström Image:Human brain frontal (coronal) section description.JPG  Source: http://en.wikipedia.org/w/index.php?title=File:Human_brain_frontal_(coronal)_section_description.JPG  License: Creative Commons Attribution 2.5  Contributors: John A Beal, PhD Dep't. of Cellular Biology & Anatomy, Louisiana State University Health Sciences Center Shreveport Image:Human brainstem-thalamus posterior view description.JPG  Source: http://en.wikipedia.org/w/index.php?title=File:Human_brainstem-thalamus_posterior_view_description.JPG  License: Creative Commons Attribution 2.5  Contributors: John A Beal, PhD Dep't. of Cellular Biology & Anatomy, Louisiana State University Health Sciences Center Shreveport Image:Gray773.png  Source: http://en.wikipedia.org/w/index.php?title=File:Gray773.png  License: Public Domain  Contributors: Arcadian, Lipothymia, Magnus Manske, Was a bee Image:Gray723.png  Source: http://en.wikipedia.org/w/index.php?title=File:Gray723.png  License: Public Domain  Contributors: Arcadian, Foroa, Lipothymia, Quibik, Was a bee Image:Gray716.png  Source: http://en.wikipedia.org/w/index.php?title=File:Gray716.png  License: Public Domain  Contributors: Arcadian, Lipothymia, Was a bee Image:Gray730.png  Source: http://en.wikipedia.org/w/index.php?title=File:Gray730.png  License: Public Domain  Contributors: Arcadian, Mormegil, Was a bee Image:Telencephalon-Horiconatal.jpg  Source: http://en.wikipedia.org/w/index.php?title=File:Telencephalon-Horiconatal.jpg  License: Public Domain  Contributors: modified after Gray's Anatomy by Uwe Gille 16:30, 6 August 2005 (UTC) and Hermann Thomas 08:36, 18 September 2006 (UTC) Image:Gray715.png  Source: http://en.wikipedia.org/w/index.php?title=File:Gray715.png  License: Public Domain  Contributors: Arcadian, Aude, Lipothymia, Origamiemensch, Quibik, Was a bee, 1 anonymous edits Image:Gray678.png  Source: http://en.wikipedia.org/w/index.php?title=File:Gray678.png  License: Public Domain  Contributors: Arcadian, Filip em, Lipothymia, Was a bee Image:Gray713.png  Source: http://en.wikipedia.org/w/index.php?title=File:Gray713.png  License: Public Domain  Contributors: Arcadian, Lipothymia, Mikael Häggström, Was a bee Image:Gray685.png  Source: http://en.wikipedia.org/w/index.php?title=File:Gray685.png  License: Public Domain  Contributors: Arcadian, Lipothymia, Was a bee Image:Gray690.png  Source: http://en.wikipedia.org/w/index.php?title=File:Gray690.png  License: Public Domain  Contributors: Arcadian, Lipothymia Image:Gray717.png  Source: http://en.wikipedia.org/w/index.php?title=File:Gray717.png  License: Public Domain  Contributors: Arcadian, Lipothymia, Was a bee Image:Gray718.png  Source: http://en.wikipedia.org/w/index.php?title=File:Gray718.png  License: Public Domain  Contributors: Arcadian, Lipothymia, Quibik, Was a bee File:Slide9gg.JPG  Source: http://en.wikipedia.org/w/index.php?title=File:Slide9gg.JPG  License: Creative Commons Attribution-Sharealike 3.0  Contributors: User:Anatomist90 File:Sleep EEG Stage 4.jpg  Source: http://en.wikipedia.org/w/index.php?title=File:Sleep_EEG_Stage_4.jpg  License: Public Domain  Contributors: Danim, Evrik, Geperdo, Lupo, Magnus Manske, S. Jähnichen, Zscout370, 1 anonymous edits Image:eeg theta.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Eeg_theta.svg  License: GNU Free Documentation License  Contributors: Hugo Gamboa Image:eeg alpha.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Eeg_alpha.svg  License: GNU Free Documentation License  Contributors: Hugo Gamboa Image:eeg beta.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Eeg_beta.svg  License: GNU Free Documentation License  Contributors: Hugo Gamboa Image:eeg gamma.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Eeg_gamma.svg  License: GNU Free Documentation License  Contributors: Hugo Gambo File:Eeg_alpha.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Eeg_alpha.svg  License: GNU Free Documentation License  Contributors: Hugo Gamboa Image:Motor cortex.PNG  Source: http://en.wikipedia.org/w/index.php?title=File:Motor_cortex.PNG  License: GNU Free Documentation License  Contributors: Akinom, Albert kok, Dryke, Was a bee file:LocationOfHypothalamus.jpg  Source: http://en.wikipedia.org/w/index.php?title=File:LocationOfHypothalamus.jpg  License: Public Domain  Contributors: EhJJ, OldakQuill, Was a bee, Роман Беккер file:Illu diencephalon .jpg  Source: http://en.wikipedia.org/w/index.php?title=File:Illu_diencephalon_.jpg  License: Public Domain  Contributors: Arcadian, CielProfond, Was a bee, 1 anonymous edits Image:HIGHPVN.jpg  Source: http://en.wikipedia.org/w/index.php?title=File:HIGHPVN.jpg  License: Attribution  Contributors: John K. Young, Ph.D. (VMHman). This image of the monkey hypothalamus has not been previously published or copyrighted and is freely available to all who want to use it. Image:HypothalamicNuclei.PNG  Source: http://en.wikipedia.org/w/index.php?title=File:HypothalamicNuclei.PNG  License: Public Domain  Contributors: Arcadian, Peace and Passion, Was a bee, 2 anonymous edits Image:3D-Hypothalamus.JPG  Source: http://en.wikipedia.org/w/index.php?title=File:3D-Hypothalamus.JPG  License: Attribution  Contributors: John K. Young, Ph.D. Image:Gray654.png  Source: http://en.wikipedia.org/w/index.php?title=File:Gray654.png  License: Public Domain  Contributors: Arcadian, Was a bee

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Image Sources, Licenses and Contributors Image:Human brain left dissected midsagittal view description 2.JPG  Source: http://en.wikipedia.org/w/index.php?title=File:Human_brain_left_dissected_midsagittal_view_description_2.JPG  License: Creative Commons Attribution 2.5  Contributors: John A Beal, PhD Dep't. of Cellular Biology & Anatomy, Louisiana State University Health Sciences Center Shreveport Image:Endocrine central nervous en.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Endocrine_central_nervous_en.svg  License: Public Domain  Contributors: LadyofHats file:Gray739-emphasizing-hippocampus.png  Source: http://en.wikipedia.org/w/index.php?title=File:Gray739-emphasizing-hippocampus.png  License: unknown  Contributors: Derivative work: Looie496 File:MRI Location Hippocampus up..png  Source: http://en.wikipedia.org/w/index.php?title=File:MRI_Location_Hippocampus_up..png  License: Creative Commons Zero  Contributors: Amber Rieder, Jenna Traynor File:Hippocampus and seahorse cropped.JPG  Source: http://en.wikipedia.org/w/index.php?title=File:Hippocampus_and_seahorse_cropped.JPG  License: Creative Commons Attribution-Sharealike 3.0,2.5,2.0,1.0  Contributors: Hippocampus_and_seahorse.JPG: Professor Laszlo Seress derivative work: Anthonyhcole (talk) Image:Triangle-place-cells.png  Source: http://en.wikipedia.org/w/index.php?title=File:Triangle-place-cells.png  License: Public Domain  Contributors: Looie496 Image:Brainmaps-macaque-hippocampus.jpg  Source: http://en.wikipedia.org/w/index.php?title=File:Brainmaps-macaque-hippocampus.jpg  License: Creative Commons Attribution 3.0  Contributors: brainmaps.org Image:CajalHippocampus (modified).png  Source: http://en.wikipedia.org/w/index.php?title=File:CajalHippocampus_(modified).png  License: Public Domain  Contributors: original: Santiago Ramón y Cajal (1852–1934) derivative = Looie496 Image:Rat-hippocampal-activity-modes.png  Source: http://en.wikipedia.org/w/index.php?title=File:Rat-hippocampal-activity-modes.png  License: Public Domain  Contributors: Looie496 File:SimulationNeuralOscillations.png  Source: http://en.wikipedia.org/w/index.php?title=File:SimulationNeuralOscillations.png  License: Public Domain  Contributors: TjeerdB File:Current Clamp recording of Neuron.GIF  Source: http://en.wikipedia.org/w/index.php?title=File:Current_Clamp_recording_of_Neuron.GIF  License: Public Domain  Contributors: Bilz0r, Rvfrolov File:Simulation of hrose neuron.png  Source: http://en.wikipedia.org/w/index.php?title=File:Simulation_of_hrose_neuron.png  License: Creative Commons Attribution 3.0  Contributors: Original uploader was TjeerdB at en.wikipedia File:NeuralMassSimulation.png  Source: http://en.wikipedia.org/w/index.php?title=File:NeuralMassSimulation.png  License: Creative Commons Attribution 3.0  Contributors: TjeerdB File:KuramotoModel.ogv  Source: http://en.wikipedia.org/w/index.php?title=File:KuramotoModel.ogv  License: Creative Commons Attribution 3.0  Contributors: TjeerdB File:Freq response.png  Source: http://en.wikipedia.org/w/index.php?title=File:Freq_response.png  License: Creative Commons Attribution-Sharealike 3.0  Contributors: User:TjeerdB File:Amp response.png  Source: http://en.wikipedia.org/w/index.php?title=File:Amp_response.png  License: Creative Commons Attribution-Sharealike 3.0  Contributors: User:TjeerdB File:Phase resetting.png  Source: http://en.wikipedia.org/w/index.php?title=File:Phase_resetting.png  License: Creative Commons Attribution-Sharealike 3.0  Contributors: User:TjeerdB File:Additive response.png  Source: http://en.wikipedia.org/w/index.php?title=File:Additive_response.png  License: Creative Commons Attribution-Sharealike 3.0  Contributors: User:TjeerdB File:Writing by a Parkinson's disease patient.png  Source: http://en.wikipedia.org/w/index.php?title=File:Writing_by_a_Parkinson's_disease_patient.png  License: Public Domain  Contributors: Jean-Martin Charcot Image:Stage2sleep.svg  Source: http://en.wikipedia.org/w/index.php?title=File:Stage2sleep.svg  License: Public Domain  Contributors: User:Neocadre. Original uploader was Ijustam at en.wikipedia File:Biofeedback.png  Source: http://en.wikipedia.org/w/index.php?title=File:Biofeedback.png  License: Creative Commons Attribution-ShareAlike 3.0 Unported  Contributors: Marek Jacenko File:Biofeedback training program for post-traumatic stress symptoms.jpg  Source: http://en.wikipedia.org/w/index.php?title=File:Biofeedback_training_program_for_post-traumatic_stress_symptoms.jpg  License: Creative Commons Attribution 2.0  Contributors: Army Medicine File:EmWave2, powering up.jpg  Source: http://en.wikipedia.org/w/index.php?title=File:EmWave2,_powering_up.jpg  License: Creative Commons Attribution-Sharealike 3.0  Contributors: Morn File:Dreamachine still lit.jpg  Source: http://en.wikipedia.org/w/index.php?title=File:Dreamachine_still_lit.jpg  License: Public Domain  Contributors: Avron, Ingolfson, Steve Burnett, TommyBee File:Mind machine.jpg  Source: http://en.wikipedia.org/w/index.php?title=File:Mind_machine.jpg  License: Creative Commons Attribution-Sharealike 3.0  Contributors: User:Kemitsv

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License

License Creative Commons Attribution-Share Alike 3.0 Unported //creativecommons.org/licenses/by-sa/3.0/

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