Literature DB >> 34244359

Disengagement of Motor Cortex during Long-Term Learning Tracks the Performance Level of Learned Movements.

Eun Jung Hwang1, Jeffrey E Dahlen2, Madan Mukundan2, Takaki Komiyama1.   

Abstract

Not all movements require the motor cortex for execution. Intriguingly, dependence on motor cortex of a given movement is not fixed, but instead can dynamically change over the course of long-term learning. For instance, rodent forelimb movements that initially require motor cortex can become independent of the motor cortex after an extended period of training. However, it remains unclear whether long-term neural changes rendering the motor cortex dispensable are a simple function of the training length. To address this issue, we trained mice (both male and female) to perform two distinct forelimb movements, forward versus downward reaches with a joystick, concomitantly over several weeks, and then compared the involvement of the motor cortex between the two movements. Most mice achieved different levels of motor performance between the two movements after long-term training. Of the two movements, the one that achieved higher trial-to-trial consistency (i.e., consistent-direction movement) was significantly less affected by inactivation of motor cortex than the other (i.e., variable-direction movement). Two-photon calcium imaging of motor cortical neurons revealed that the consistent-direction movement activates fewer neurons, producing weaker and less consistent population activity than the variable-direction movement. Together, the motor cortex was less engaged and less necessary for learned movements that achieved higher levels of consistency. Thus, the long-term reorganization of neural circuits that frees the motor cortex from the learned movement is not a mere function of training length. Rather, this reorganization tracks the level of motor performance that the animal achieves during training.SIGNIFICANCE STATEMENT Long-term training of a movement reshapes motor circuits, disengaging motor cortex potentially for automatized execution of the learned movement. Acquiring new motor skills often involves learning of multiple movements (e.g., forehand and backhand strokes when learning tennis), but different movements do not always improve at the same time nor reach the same level of proficiency. Here we showed that the involvement of motor cortex after long-term training differs between similar yet distinct movements that reached different levels of expertise. Motor cortex was less engaged and less necessary for the more proficient movement. Thus, disengagement of motor cortex is not a simple function of training time, but instead tracks the level of expertise of a learned movement.
Copyright © 2021 the authors.

Entities:  

Keywords:  motor cortex inactivation; motor learning; mouse motor cortex; mouse reaching; movement consistency; multiple movement learning

Mesh:

Year:  2021        PMID: 34244359      PMCID: PMC8372014          DOI: 10.1523/JNEUROSCI.3049-20.2021

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  40 in total

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Authors:  J N Sanes; J P Donoghue
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2.  Imaging brain plasticity during motor skill learning.

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3.  Lawrence and Kuypers (1968a, b) revisited: copies of the original filmed material from their classic papers in Brain.

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Review 4.  The organization of behavioral repertoire in motor cortex.

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5.  Reorganization of Recurrent Layer 5 Corticospinal Networks Following Adult Motor Training.

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Journal:  J Neurosci       Date:  2019-04-04       Impact factor: 6.167

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Review 7.  Functional recovery following motor cortex lesions in non-human primates: experimental implications for human stroke patients.

Authors:  Warren G Darling; Marc A Pizzimenti; Robert J Morecraft
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8.  Motor learning interference is proportional to occlusion of LTP-like plasticity.

Authors:  Gabriela Cantarero; Byron Tang; Rebecca O'Malley; Rachel Salas; Pablo Celnik
Journal:  J Neurosci       Date:  2013-03-13       Impact factor: 6.167

9.  Parallel, but Dissociable, Processing in Discrete Corticostriatal Inputs Encodes Skill Learning.

Authors:  David A Kupferschmidt; Konrad Juczewski; Guohong Cui; Kari A Johnson; David M Lovinger
Journal:  Neuron       Date:  2017-10-11       Impact factor: 17.173

10.  Cortical pattern generation during dexterous movement is input-driven.

Authors:  Britton A Sauerbrei; Jian-Zhong Guo; Jeremy D Cohen; Matteo Mischiati; Wendy Guo; Mayank Kabra; Nakul Verma; Brett Mensh; Kristin Branson; Adam W Hantman
Journal:  Nature       Date:  2019-12-25       Impact factor: 49.962

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