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  Left Hemisphere Dominance for Bilateral Kinematic Encoding in the Human Brain

Merrick, C., Dixon, T., Breska, A., Lin, J., Chang, E., King-Stephens, D., et al. (2022). Left Hemisphere Dominance for Bilateral Kinematic Encoding in the Human Brain. eLife, 11: e69977. doi:10.7554/eLife.69977.

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https://elifesciences.org/articles/69977 (Publisher version)
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 Creators:
Merrick, CM, Author
Dixon, TC, Author
Breska, A1, Author                 
Lin, JJ, Author
Chang, EF, Author
King-Stephens, D, Author
Laxer, KD, Author
Weber, PB, Author
Carmena, JM, Author
Knight, RT, Author
Ivry, RB, Author
Affiliations:
1Research Group Dynamic Cognition, Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_3344256              

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 Abstract: Neurophysiological studies in humans and nonhuman primates have revealed movement representations in both the contralateral and ipsilateral hemispheres. Inspired by clinical observations, we ask if this bilateral representation differs for the left and right hemispheres. Electrocorticography was recorded in human participants during an instructed-delay reaching task, with movements produced with either the contralateral or ipsilateral arm. Using a cross-validated kinematic encoding model, we found stronger bilateral encoding in the left hemisphere, an effect that was present during preparation and was amplified during execution. Consistent with this asymmetry, we also observed better across-arm generalization in the left hemisphere, indicating similar neural representations for right and left arm movements. Notably, these left hemisphere electrodes were centered over premotor and parietal regions. The more extensive bilateral encoding in the left hemisphere adds a new perspective to the pervasive neuropsychological finding that the left hemisphere plays a dominant role in praxis.

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 Dates: 2022-03
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.7554/eLife.69977
 Degree: -

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Title: eLife
Source Genre: Journal
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Publ. Info: Cambridge : eLife Sciences Publications
Pages: 25 Volume / Issue: 11 Sequence Number: e69977 Start / End Page: - Identifier: Other: URL
ISSN: 2050-084X
CoNE: https://pure.mpg.de/cone/journals/resource/2050-084X