User:FutureSocialNeuroscientist/sandbox

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Single lead EEG readout
One second sample of an EEG alpha wave recording. This wave occurs at the same frequency as the mu wave, although the alpha wave is detected over a different part of the brain.
Left motor cortex highlighted on the brain
The left motor cortex, or BA4, is highlighted in green on this left lateral view of the brain. This is the area over which mu waves are detected bilaterally.

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 pyramidal type, in the part of the brain that 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 approximately from ear to ear. A person suppresses mu wave patterns when he or she performs a motor action or, with practice, 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 have suggested that this is a sign that the mirror neuron system is involved in mu wave suppression,[2] although others disagree.[3]

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.[4] Since a group of researchers believe that autism spectrum disorder (ASD) is strongly influenced by a faulty mirror neuron system,[2][5][6] 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.[7]

History[edit]

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.[8][9] These results were later confirmed by additional research groups,[10][11][12] including a study using subdural electrode grids in epileptic patients.[13] 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[14][15] and by passively viewing point-light biological motion.[16]

Mu waves and mirror neurons[edit]

The mirror neuron system consists of a class of neurons that was first studied in the 1990s in macaque monkeys.[6] 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.[17] 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.[18] The firing pattern of mirror neurons in non-human animals suggests that they play a role in mapping others' movements into the brain without actually physically performing the movements. Evidence points to a very similar phenomenon in humans. The right fusiform gyrus, left inferior parietal lobule, right anterior parietal cortex, and left inferior frontal gyrus are of particular interest.[19][6][20] Tests in both monkeys and humans have found that these mirror neurons not only fire during basic motor tasks, but also have components that deal with intention.[21] There is evidence of an important role for mirror neurons in humans.

Mirror neurons and autism[edit]

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][5] 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.[6] This deficiency would explain the difficulty people with autism have in communicating to 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][5]

Development[edit]

The mu wave is detectable during infancy as early as six months, when the peak frequency the wave reaches can be as low as 5.4 Hz.[4] There is a rapid increase in peak frequency in the first year of life,[22] and by age two frequency typically reaches 7.5 Hz.[19] 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.[4][19][22] 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.[19] Mimicking is integral in the development of social skills and understanding nonverbal cues.[4] 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 adults.[4] 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.[23]

Development in individuals with autism[edit]

Based on findings correlating mirror neuron activity and mu wave suppression in individuals with autism, studies have also examined both the development of mirror neurons and therapeutic means for stimulating the system. A recent study has found that the inferior frontal gyrus activation rates increase with age in people with autism. This finding is not apparent in typically developing individuals.[24] These findings are relevant because scientists believe the inferior frontal gyrus is one of the main neural correlates with the mirror neuron system in humans and is related to deficits associated with autism.[20] This increased activation was also associated with greater amounts of eye contact and better social functioning.[24] These findings point to the idea that the mirror neuron system is not non-functional in individuals with autism, but simply abnormal in its development. Other studies have attempted to consciously stimulate the mirror neuron system and suppress mu waves using neurofeedback (a type of biofeedback) given through computers that detected mirror neuron activity. This type of therapy is still in its early phases, and has conflicting forecasts for success.[25][26]

Brain-computer interfaces[edit]

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.[7][27] Few of these technologies are currently in regular use by people with disabilities, but a diverse array are in development at an experimental level.[7][28][29] One type of BCI uses event-related desynchronization (ERD) of the mu wave in order to control the computer.[7] 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.[7][28] 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.[28] This paired pattern intensifies with training,[7][28][29][30] and the training increasingly takes the form of games, some of which utilize virtual reality.[7][28][30] 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.[7][30] 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.[7][28]

See also[edit]

Other brain waves[edit]

References[edit]

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  2. ^ a b c d Oberman, Lindsay M.; Hubbard, Edward M.; McCleery, Joseph P.; Altschuler, Eric L.; Ramachandran, Vilayanur S.; Pineda, Jaime A. (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. {{cite journal}}: Unknown parameter |month= ignored (help)CS1 maint: date and year (link) Cite error: The named reference "Oberman" was defined multiple times with different content (see the help page).
  3. ^ Churchland, Patricia (2011). Braintrust: What Neuroscience Tells Us About Morality. Princeton, NJ: Princeton University Press. p. 156. ISBN 978-0-691-13703-2.
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  19. ^ a b c d Marshall, Peter J.; Meltzoff, Andrew N. (2011). "Neural mirroring systems: Exploring the EEG mu rhythm in human infancy". Developmental Cognitive Neuroscience. 1 (2): 110–123. doi:10.1016/j.dcn.2010.09.001. PMID PMC3081582. Retrieved 23 October 2012. {{cite journal}}: Check |pmid= value (help); Unknown parameter |month= ignored (help)CS1 maint: date and year (link)
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  22. ^ a b 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". Developmental Neuroscience. 33 (2): 130–143. doi:10.1159/000329095. PMC 3221274. PMID 21778699.
  23. ^ Meltzoff, Andrew N.; Kuhl, Patricia K.; Movellan, Javier; Sejnowski, Terrence J. (16). "Foundations for a New Science of Learning". Science. 325 (5938): 284–288. doi:10.1126/science.1175626. PMID PMC2776823. Retrieved 23 October 2012. {{cite journal}}: Check |pmid= value (help); Check date values in: |date= and |year= / |date= mismatch (help); Unknown parameter |month= ignored (help)
  24. ^ a b Bastiaansen, J. A.; 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". Biological Psychiatry. 69 (9): 832–838. doi:10.1016/j.biopsych.2010.11.007. PMID 21310395. Retrieved 21 October 2012. {{cite journal}}: Check date values in: |date= and |year= / |date= mismatch (help); Unknown parameter |month= ignored (help)
  25. ^ Holtmann, Martin; Steiner, Sabina; Hohmann, Sarah; Poustka, Luise; Banaschewski, Tobias; Bölte, Sven (1). "Neurofeedback in autism spectrum disorders". Developmental Medicine & Child Neurology. 53 (11): 986–993. doi:10.1111/j.1469-8749.2011.04043.x. PMID 21752020. Retrieved 10 November 2012. {{cite journal}}: Check date values in: |date= and |year= / |date= mismatch (help); Unknown parameter |month= ignored (help)
  26. ^ Coben, Robert; Linden, Michael; Myers, Thomas E. (24). "Neurofeedback for Autistic Spectrum Disorder: A Review of the Literature". Applied Psychophysiology and Biofeedback. 35 (1): 83–105. doi:10.1007/s10484-009-9117-y. PMID 19856096. Retrieved 10 November 2012. {{cite journal}}: Check date values in: |date= and |year= / |date= mismatch (help); Unknown parameter |month= ignored (help)
  27. ^ Machado, S.; Araújo, F.; Paes, F.; Velasques, B.; Cunha, M.; Budde, H.; Basile, L. F.; Anghinah, R.; Arias-Carrión, O.; Cagy, M.; Piedade, R.; De Graaf, T. A.; Sack, A. T.; Ribeiro, P. (2010). "EEG-based brain-computer interfaces: an overview of basic concepts and clinical applications in neurorehabilitation". Reviews in the Neurosciences. 21 (6): 451–68. doi:10.1515/revneuro.2010.21.6.451. PMID 21438193. Retrieved 10 November 2012.{{cite journal}}: CS1 maint: date and year (link)
  28. ^ a b c d e f Pfurtscheller, Gert (2012). "BCIs that use sensorimotor rhythms". In Wolpaw, Jonathan R.; Wolpaw, Elizabeth Winter (ed.). Brain-Computer Interfaces: Principles and Practice. Oxford: Oxford University Press. pp. 227–240. ISBN 9780195388855. {{cite book}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)CS1 maint: multiple names: editors list (link)
  29. ^ a b Leuthardt, Eric C.; Schalk, Gerwin; Roland, Jarod; Rouse, Adam; Moran, Daniel W. (2009). "Evolution of brain-computer interfaces: going beyond classic motor physiology". Neurosurgical Focus. 27 (1): E4. doi:10.3171/2009.4.FOCUS0979. PMC 2920041. PMID PMC2920041. {{cite journal}}: Check |pmid= value (help); Unknown parameter |month= ignored (help)CS1 maint: date and year (link)
  30. ^ a b c 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". Journal of Neural Engineering. 9 (1): 013001. doi:10.1088/1741-2560/9/1/013001. PMID 22156029. Retrieved 10 November 2012. {{cite journal}}: Check date values in: |date= and |year= / |date= mismatch (help); Unknown parameter |month= ignored (help)
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