Refinement of learned skilled movement representation in motor cortex deep output layer

Qian Li, Ho Ko, Zhong-Ming Qian, Leo Y. C. Yan, Danny C. W. Chan, Gordon Arbuthnott, Ya Ke () and Wing-Ho Yung ()
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Qian Li: School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong
Ho Ko: Faculty of Medicine, The Chinese University of Hong Kong
Zhong-Ming Qian: Laboratory of Neuropharmacology, School of Pharmacy, Fudan University
Leo Y. C. Yan: School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong
Danny C. W. Chan: School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong
Gordon Arbuthnott: Okinawa Institute of Science and Technology Graduate University
Ya Ke: School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong
Wing-Ho Yung: School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong

Nature Communications, 2017, vol. 8, issue 1, 1-19

Abstract: Abstract The mechanisms underlying the emergence of learned motor skill representation in primary motor cortex (M1) are not well understood. Specifically, how motor representation in the deep output layer 5b (L5b) is shaped by motor learning remains virtually unknown. In rats undergoing motor skill training, we detect a subpopulation of task-recruited L5b neurons that not only become more movement-encoding, but their activities are also more structured and temporally aligned to motor execution with a timescale of refinement in tens-of-milliseconds. Field potentials evoked at L5b in vivo exhibit persistent long-term potentiation (LTP) that parallels motor performance. Intracortical dopamine denervation impairs motor learning, and disrupts the LTP profile as well as the emergent neurodynamical properties of task-recruited L5b neurons. Thus, dopamine-dependent recruitment of L5b neuronal ensembles via synaptic reorganization may allow the motor cortex to generate more temporally structured, movement-encoding output signal from M1 to downstream circuitry that drives increased uniformity and precision of movement during motor learning.

Date: 2017
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DOI: 10.1038/ncomms15834

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