<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-19T01:45:34Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/38519" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/38519</identifier><datestamp>2022-01-13T07:53:45Z</datestamp><setSpec>com_1721.1_7582</setSpec><setSpec>com_1721.1_7581</setSpec><setSpec>col_1721.1_131022</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="advisor" lang="en_US">Emilio Bizzi.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">Cheung, Vincent Chi-Kwan</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="other" lang="en_US">Harvard University--MIT Division of Health Sciences and Technology.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department">Harvard University--MIT Division of Health Sciences and Technology</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2007-08-29T19:05:34Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="available">2007-08-29T19:05:34Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="copyright" lang="en_US">2007</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued" lang="en_US">2007</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">http://hdl.handle.net/1721.1/38519</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="oclc" lang="en_US">156911856</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2007.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">This electronic version was submitted by the student author.  The certified thesis is available in the Institute Archives and Special Collections.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Includes bibliographical references (p. 162-170).</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">To achieve any motor behavior, the central nervous system (CNS) must coordinate the many degrees of freedom in the musculoskeletal apparatus. It has been suggested that the CNS simplifies this formidable task of coordination by grouping multiple muscles together into units of activation, or muscle synergies. Previous studies have shown that electromyogram (EMG) signals collected from many muscles during natural behaviors can be reconstructed by linearly combining a few synergies, identified by the non-negative matrix factorization algorithm. But to what extent synergies are neural constraints, or merely structures reflecting experimental constraints, has remained an open question. I address this question with the hypothesis that, muscle synergies are robust neural patterns constraining motor outputs. The strategy adopted was that of analyzing EMGs collected before and after delivery of a perturbation to the motor system. In my first experiment, EMGs from bullfrog muscles were recorded during locomotor behaviors before and after deafferentation. Systematic comparison of intact and deafferented synergies suggests that most of the synergies remained unchanged after afferent removal.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">(cont.) In my second experiment, the frog hindlimb was perturbed by either an inertial load or an elastic load. Using a novel algorithm capable of simultaneously extracting shared and specific synergies, I demonstrate that, most synergies were shared between the different conditions, but their activation patterns were reversibly altered by loading. Overall, my results suggest that muscle synergies are robust, centrally organized structures, and descending and afferent signals cooperate in modulating their activations so that the resulting motor commands can be efficiently adapted to the external environment.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="statementofresponsibility" lang="en_US">by Vincent Chi-Kwan Cheung.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">Ph.D.</dim:field>
   <dim:field mdschema="dc" element="format" qualifier="extent" lang="en_US">170 p.</dim:field>
   <dim:field mdschema="dc" element="language" qualifier="iso" lang="en_US">eng</dim:field>
   <dim:field mdschema="dc" element="publisher" lang="en_US">Massachusetts Institute of Technology</dim:field>
   <dim:field mdschema="dc" element="rights" lang="en_US">M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.</dim:field>
   <dim:field mdschema="dc" element="rights" qualifier="uri">http://dspace.mit.edu/handle/1721.1/7582</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">Harvard University--MIT Division of Health Sciences and Technology.</dim:field>
   <dim:field mdschema="dc" element="title" lang="en_US">Sensory modulation of muscle synergies for motor adaptation during natural behaviors</dim:field>
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   	&lt;Title>Sensory modulation of muscle synergies for motor adaptation during natural behaviors&lt;/Title>
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   	&lt;PublicationDate>2007&lt;/PublicationDate>
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        	&lt;DisplayName>Cheung, Vincent Chi-Kwan&lt;/DisplayName>
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   	&lt;Abstract>To achieve any motor behavior, the central nervous system (CNS) must coordinate the many degrees of freedom in the musculoskeletal apparatus. It has been suggested that the CNS simplifies this formidable task of coordination by grouping multiple muscles together into units of activation, or muscle synergies. Previous studies have shown that electromyogram (EMG) signals collected from many muscles during natural behaviors can be reconstructed by linearly combining a few synergies, identified by the non-negative matrix factorization algorithm. But to what extent synergies are neural constraints, or merely structures reflecting experimental constraints, has remained an open question. I address this question with the hypothesis that, muscle synergies are robust neural patterns constraining motor outputs. The strategy adopted was that of analyzing EMGs collected before and after delivery of a perturbation to the motor system. In my first experiment, EMGs from bullfrog muscles were recorded during locomotor behaviors before and after deafferentation. Systematic comparison of intact and deafferented synergies suggests that most of the synergies remained unchanged after afferent removal.&lt;/Abstract>
   	&lt;Abstract>(cont.) In my second experiment, the frog hindlimb was perturbed by either an inertial load or an elastic load. Using a novel algorithm capable of simultaneously extracting shared and specific synergies, I demonstrate that, most synergies were shared between the different conditions, but their activation patterns were reversibly altered by loading. Overall, my results suggest that muscle synergies are robust, centrally organized structures, and descending and afferent signals cooperate in modulating their activations so that the resulting motor commands can be efficiently adapted to the external environment.&lt;/Abstract>
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