Literature DB >> 24969473

The Human Motor System Supports Sequence-Specific Representations over Multiple Training-Dependent Timescales.

Nicholas F Wymbs1, Scott T Grafton2.   

Abstract

Motor sequence learning is associated with increasing and decreasing motor system activity. Here, we ask whether sequence-specific activity is contingent upon the time interval and absolute amount of training over which the skill is acquired. We hypothesize that within each motor region, the strength of any sequence representation is a non-linear function that can be characterized by 3 timescales. We had subjects train for 6 weeks and measured brain activity with functional magnetic resonance imaging. We used repetition suppression (RS) to isolate sequence-specific representations while controlling for effects related to kinematics and general task familiarity. Following a baseline training session, primary and secondary motor regions demonstrated rapidly increasing RS. With continued training, there was evidence for skill-specific efficiency, characterized by a dramatic decrease in motor system RS. In contrast, after performance had reached a plateau, further training led to a pattern of slowly increasing RS in the contralateral sensorimotor cortex, supplementary motor area, ventral premotor cortex, and anterior cerebellum consistent with skill-specific specialization. Importantly, many motor areas show changes involving more than 1 of these 3 timescales, underscoring the capacity of the motor system to flexibly represent a sequence based on the amount of prior experience.
© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  SMA; motor learning; repetition suppression; sensorimotor; skill learning

Mesh:

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Year:  2014        PMID: 24969473      PMCID: PMC4747644          DOI: 10.1093/cercor/bhu144

Source DB:  PubMed          Journal:  Cereb Cortex        ISSN: 1047-3211            Impact factor:   5.357


  57 in total

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