Multipotentiality of the Brain to Be Revisited Repeatedly

The brain is a unified entity that cannot realize any functions in its isolated regions. It is also so dynamic that the functions of its regions and neurons are not necessarily fixed. Historically, this natural notion has been confirmed repeatedly by several experimental findings and theoretical considerations including those by Sherrington, Lashley, Hebb, Olds, and John. However, this notion, which typically can be called “multipotentiality” of the brain proposed by E. R. John, has been repeatedly ignored. Most studies in modern neuroscience are searching for fixed and peculiar regions responsible for individual, even any higher, functions and trying to detect treasured single neurons. This article emphasizes again the multipotentiality and raises promising strategies to investigate such unique features of the brain. First, we introduce the historical background and revisit the pioneering studies and consider the impacts of their views on our understanding of brain structures and functions. The second section emphasizes that the brain-machine interfaces has been presenting the multipotentiality of the brain’s regions and neurons. The third section considers the clinical relevance of the multipotentiality, particularly in relation to neurorehabilitation and the recovery of function after brain damage. Finally, we introduce recent neuroimaging findings indicating the multipotentiality and suggest an adequate experimental strategy to investigate the brain functions based on the view of multipotentiality, in which the assumption of cell-assembly coding is necessarily involved.

[1]  Karim G. Oweiss,et al.  Neuroplasticity subserving the operation of brain–machine interfaces , 2015, Neurobiology of Disease.

[2]  Robert E. Hampson,et al.  Distributed encoding of spatial and object categories in primate hippocampal microcircuits , 2015, Front. Neurosci..

[3]  Lee E. Miller,et al.  Guest Editorial Brain Training: Cortical Plasticity and Afferent Feedback in Brain-Machine Interface Systems , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  Y. Sakurai How do cell assemblies encode information in the brain? , 1999, Neuroscience & Biobehavioral Reviews.

[5]  Tanuj Gulati,et al.  Robust Neuroprosthetic Control from the Stroke Perilesional Cortex , 2015, The Journal of Neuroscience.

[6]  Robert E. Hampson,et al.  Prefrontal cortical microcircuits bind perception to executive control , 2013, Scientific Reports.

[7]  Damian J. Wallace,et al.  Chasing the cell assembly , 2010, Current Opinion in Neurobiology.

[8]  Aaron C. Koralek,et al.  Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills , 2012, Nature.

[9]  M. Filippi,et al.  Functional MRI to study brain plasticity in clinical neurology , 2006, Neurological Sciences.

[10]  Miguel A L Nicolelis Mind out of body. , 2011, Scientific American.

[11]  K. Harris Neural signatures of cell assembly organization , 2005, Nature Reviews Neuroscience.

[12]  D. Sharp,et al.  Contrasting network and modular perspectives on inhibitory control , 2015, Trends in Cognitive Sciences.

[13]  S. Chapman,et al.  Enhancement of cognitive and neural functions through complex reasoning training: evidence from normal and clinical populations , 2014, Front. Syst. Neurosci..

[14]  Yoshio Sakurai,et al.  Brain-machine interfaces can accelerate clarification of the principal mysteries and real plasticity of the brain , 2014, Front. Syst. Neurosci..

[15]  Á. Pascual-Leone,et al.  Brain Plasticity in Blind Subjects Centralizes Beyond the Modal Cortices , 2016, Front. Syst. Neurosci..

[16]  Robert E. Hampson,et al.  Distribution of spatial and nonspatial information in dorsal hippocampus , 1999, Nature.

[17]  Robert E. Hampson,et al.  Prefrontal cortical recordings with biomorphic MEAs reveal complex columnar-laminar microcircuits for BCI/BMI implementation , 2015, Journal of Neuroscience Methods.

[18]  Yoshio Sakurai,et al.  Population coding by cell assemblies—what it really is in the brain , 1996, Neuroscience Research.

[19]  Miguel A. L. Nicolelis,et al.  Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex , 1999, Nature Neuroscience.

[20]  E. Fetz,et al.  Direct control of paralyzed muscles by cortical neurons , 2008, Nature.

[21]  Miguel A. L. Nicolelis,et al.  Principles of neural ensemble physiology underlying the operation of brain–machine interfaces , 2009, Nature Reviews Neuroscience.

[22]  K. Lashley Studies of Cerebral Function in Learning. II. The Effects of Long Continued Practice upon Cerebral L , 1921 .

[23]  Jerald D. Kralik,et al.  Real-time prediction of hand trajectory by ensembles of cortical neurons in primates , 2000, Nature.

[24]  M. Chun,et al.  A neuromarker of sustained attention from whole-brain functional connectivity , 2015, Nature Neuroscience.

[25]  Yoshio Sakurai,et al.  Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: interaction with neurorehabilitation and brain-machine interface , 2014, Front. Syst. Neurosci..

[26]  J F Disterhoft,et al.  Learning centers of rat brain mapped by measuring latencies of conditioned unit responses. , 1972, Journal of neurophysiology.

[27]  Y. Sakurai,et al.  Conditioned enhancement of firing rates and synchrony of hippocampal neurons and firing rates of motor cortical neurons in rats , 2013, The European journal of neuroscience.

[28]  T. Brown,et al.  On the Instability of a Cortical Point , 1912 .

[29]  Yoshio Sakurai,et al.  Diverse synchrony of firing reflects diverse cell-assembly coding in the prefrontal cortex , 2013, Journal of Physiology-Paris.

[30]  David M. Santucci,et al.  Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates , 2003, PLoS biology.

[31]  Mikko Sams,et al.  Functional MRI of the vocalization-processing network in the macaque brain , 2015, Front. Neurosci..

[32]  Tadashi Isa,et al.  Temporal Plasticity Involved in Recovery from Manual Dexterity Deficit after Motor Cortex Lesion in Macaque Monkeys , 2015, The Journal of Neuroscience.

[33]  John A. Wolf,et al.  Neural Substrate Expansion for the Restoration of Brain Function , 2016, Front. Syst. Neurosci..

[34]  Milos Pekny,et al.  Modulation of Neural Plasticity as a Basis for Stroke Rehabilitation , 2012, Stroke.

[35]  Yoshio Sakurai,et al.  Dynamic Synchrony of Local Cell Assembly , 2008, Reviews in the neurosciences.

[36]  E. E. Fetz,et al.  Interfacing With the Computational Brain , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[37]  Daniel Moran,et al.  Evolution of brain–computer interface: action potentials, local field potentials and electrocorticograms , 2010, Current Opinion in Neurobiology.

[38]  E. John Multipotentiality: A Statistical Theory of Brain Function—Evidence and Implications , 1980 .

[39]  Mikhail A Lebedev,et al.  Toward a whole-body neuroprosthetic. , 2011, Progress in brain research.

[40]  Y Sakurai,et al.  Cells in the rat auditory system have sensory-delay correlates during the performance of an auditory working memory task. , 1990, Behavioral neuroscience.

[41]  Y. Sakurai,et al.  Neural Operant Conditioning as a Core Mechanism of Brain-Machine Interface Control , 2016 .

[42]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces: past, present and future , 2006, Trends in Neurosciences.

[43]  E R John,et al.  Switchboard versus statistical theories of learning and memory. , 1972, Science.

[44]  Dragan F. Dimitrov,et al.  Reversible large-scale modification of cortical networks during neuroprosthetic control , 2011, Nature Neuroscience.

[45]  S. Raskin Neuroplasticity and Rehabilitation , 2011 .

[46]  Aaron C. Koralek,et al.  Temporally Precise Cell-Specific Coherence Develops in Corticostriatal Networks during Learning , 2013, Neuron.

[47]  James L Olds Unit recordings during Pavlovian conditioning. , 1975, UCLA forum in medical sciences.

[48]  György Buzsáki,et al.  Neural Syntax: Cell Assemblies, Synapsembles, and Readers , 2010, Neuron.

[49]  R. Saxe,et al.  Language processing in the occipital cortex of congenitally blind adults , 2011, Proceedings of the National Academy of Sciences.

[50]  Mikhail A. Lebedev,et al.  Brain-machine interfaces: an overview , 2014 .

[51]  K. Lashley TEMPORAL VARIATION IN THE FUNCTION OF THE GYRUS PRECENTRALIS IN PRIMATES , 1923 .

[52]  Ehren L. Newman,et al.  DC-shifts in amplitude in-field generated by an oscillatory interference model of grid cell firing , 2014, Front. Syst. Neurosci..

[53]  E. Fetz Volitional control of neural activity: implications for brain–computer interfaces , 2007, The Journal of physiology.

[54]  D. Olton,et al.  Hippocampal cells have mnemonic correlates as well as spatial ones , 1989 .

[55]  F. Attneave,et al.  The Organization of Behavior: A Neuropsychological Theory , 1949 .

[56]  R. Hampson,et al.  Closing the loop in primate prefrontal cortex: inter-laminar processing , 2012, Front. Neural Circuits.

[57]  Niels Birbaumer,et al.  Real-time fMRI brain computer interfaces: Self-regulation of single brain regions to networks , 2014, Biological Psychology.

[58]  Yoshio Sakurai,et al.  Hippocampal cells have behavioral correlates during the performance of an auditory working memory task in the rat , 1990 .

[59]  M. Nicolelis,et al.  Cortical Modulations Increase in Early Sessions with Brain-Machine Interface , 2007, PloS one.

[60]  H Eichenbaum,et al.  Thinking about brain cell assemblies. , 1993, Science.

[61]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces to restore motor function and probe neural circuits , 2003, Nature Reviews Neuroscience.

[62]  B. Dobkin Brain–computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation , 2007, The Journal of physiology.

[63]  Leslie G. Ungerleider,et al.  Imaging Brain Plasticity during Motor Skill Learning , 2002, Neurobiology of Learning and Memory.

[64]  Jeffrey S. Spence,et al.  Neural Mechanisms of Brain Plasticity with Complex Cognitive Training in Healthy Seniors , 2013, Cerebral cortex.