Musical training induces functional and structural auditory-motor network plasticity in young adults.
Identifieur interne : 000771 ( Main/Exploration ); précédent : 000770; suivant : 000772Musical training induces functional and structural auditory-motor network plasticity in young adults.
Auteurs : Qiongling Li [République populaire de Chine] ; Xuetong Wang [République populaire de Chine] ; Shaoyi Wang [République populaire de Chine] ; Yongqi Xie [République populaire de Chine] ; Xinwei Li [République populaire de Chine] ; Yachao Xie [République populaire de Chine] ; Shuyu Li [République populaire de Chine]Source :
- Human brain mapping [ 1097-0193 ] ; 2018.
Descripteurs français
- KwdFr :
- Adulte (MeSH), Analyse de variance (MeSH), Apprentissage (physiologie), Cartographie cérébrale (MeSH), Encéphale (imagerie diagnostique), Encéphale (physiologie), Facteurs temps (MeSH), Femelle (MeSH), Humains (MeSH), Imagerie par résonance magnétique (MeSH), Jeune adulte (MeSH), Musique (MeSH), Mâle (MeSH), Perception auditive (physiologie), Performance psychomotrice (physiologie), Plasticité neuronale (physiologie), Stimulation acoustique (MeSH), Traitement d'image par ordinateur (MeSH), Voies nerveuses (imagerie diagnostique), Voies nerveuses (physiologie).
- MESH :
- imagerie diagnostique : Encéphale, Voies nerveuses.
- physiologie : Apprentissage, Encéphale, Perception auditive, Performance psychomotrice, Plasticité neuronale, Voies nerveuses.
- Adulte, Analyse de variance, Cartographie cérébrale, Facteurs temps, Femelle, Humains, Imagerie par résonance magnétique, Jeune adulte, Musique, Mâle, Stimulation acoustique, Traitement d'image par ordinateur.
English descriptors
- KwdEn :
- Acoustic Stimulation (MeSH), Adult (MeSH), Analysis of Variance (MeSH), Auditory Perception (physiology), Brain (diagnostic imaging), Brain (physiology), Brain Mapping (MeSH), Female (MeSH), Humans (MeSH), Image Processing, Computer-Assisted (MeSH), Learning (physiology), Magnetic Resonance Imaging (MeSH), Male (MeSH), Music (MeSH), Neural Pathways (diagnostic imaging), Neural Pathways (physiology), Neuronal Plasticity (physiology), Psychomotor Performance (physiology), Time Factors (MeSH), Young Adult (MeSH).
- MESH :
- diagnostic imaging : Brain, Neural Pathways.
- physiology : Auditory Perception, Brain, Learning, Neural Pathways, Neuronal Plasticity, Psychomotor Performance.
- Acoustic Stimulation, Adult, Analysis of Variance, Brain Mapping, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Music, Time Factors, Young Adult.
Abstract
Playing music requires a strong coupling of perception and action mediated by multimodal integration of brain regions, which can be described as network connections measured by anatomical and functional correlations between regions. However, the structural and functional connectivities within and between the auditory and sensorimotor networks after long-term musical training remain largely uninvestigated. Here, we compared the structural connectivity (SC) and resting-state functional connectivity (rs-FC) within and between the two networks in 29 novice healthy young adults before and after musical training (piano) with those of another 27 novice participants who were evaluated longitudinally but with no intervention. In addition, a correlation analysis was performed between the changes in FC or SC with practice time in the training group. As expected, participants in the training group showed increased FC within the sensorimotor network and increased FC and SC of the auditory-motor network after musical training. Interestingly, we further found that the changes in FC within the sensorimotor network and SC of the auditory-motor network were positively correlated with practice time. Our results indicate that musical training could induce enhanced local interaction and global integration between musical performance-related regions, which provides insights into the mechanism of brain plasticity in young adults.
DOI: 10.1002/hbm.23989
PubMed: 29400420
PubMed Central: PMC6866316
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Li</name><affiliation wicri:level="1"><nlm:affiliation>School of Biological Science & Medical Engineering, Beihang University, Beijing, 100083, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Biological Science & Medical Engineering, Beihang University, Beijing, 100083</wicri:regionArea><wicri:noRegion>100083</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:affiliation>Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 102402, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 102402</wicri:regionArea><wicri:noRegion>102402</wicri:noRegion></affiliation></author><author><name sortKey="Wang, Xuetong" sort="Wang, Xuetong" uniqKey="Wang X" first="Xuetong" last="Wang">Xuetong Wang</name><affiliation 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Science & Medical Engineering, Beihang University, Beijing, 100083, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Biological Science & Medical Engineering, Beihang University, Beijing, 100083</wicri:regionArea><wicri:noRegion>100083</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:affiliation>Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 102402, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 102402</wicri:regionArea><wicri:noRegion>102402</wicri:noRegion></affiliation></author><author><name sortKey="Xie, Yongqi" sort="Xie, Yongqi" uniqKey="Xie Y" first="Yongqi" last="Xie">Yongqi Xie</name><affiliation wicri:level="1"><nlm:affiliation>School of Biological Science & Medical Engineering, Beihang University, Beijing, 100083, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Biological Science & Medical Engineering, Beihang University, Beijing, 100083</wicri:regionArea><wicri:noRegion>100083</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:affiliation>Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 102402, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 102402</wicri:regionArea><wicri:noRegion>102402</wicri:noRegion></affiliation></author><author><name sortKey="Li, Xinwei" sort="Li, Xinwei" uniqKey="Li X" first="Xinwei" last="Li">Xinwei Li</name><affiliation wicri:level="1"><nlm:affiliation>School of Biological Science & Medical Engineering, Beihang University, Beijing, 100083, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Biological Science & Medical Engineering, Beihang University, Beijing, 100083</wicri:regionArea><wicri:noRegion>100083</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:affiliation>Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 102402, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 102402</wicri:regionArea><wicri:noRegion>102402</wicri:noRegion></affiliation></author><author><name sortKey="Xie, Yachao" sort="Xie, Yachao" uniqKey="Xie Y" first="Yachao" last="Xie">Yachao Xie</name><affiliation wicri:level="1"><nlm:affiliation>State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875</wicri:regionArea><wicri:noRegion>100875</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:affiliation>Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, 100875, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, 100875</wicri:regionArea><wicri:noRegion>100875</wicri:noRegion></affiliation></author><author><name sortKey="Li, Shuyu" sort="Li, Shuyu" uniqKey="Li S" first="Shuyu" last="Li">Shuyu Li</name><affiliation wicri:level="1"><nlm:affiliation>School of Biological Science & Medical Engineering, Beihang University, Beijing, 100083, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>School of Biological Science & Medical Engineering, Beihang University, Beijing, 100083</wicri:regionArea><wicri:noRegion>100083</wicri:noRegion></affiliation><affiliation wicri:level="1"><nlm:affiliation>Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 102402, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 102402</wicri:regionArea><wicri:noRegion>102402</wicri:noRegion></affiliation></author></analytic><series><title level="j">Human brain mapping</title><idno type="eISSN">1097-0193</idno><imprint><date when="2018" type="published">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Acoustic Stimulation (MeSH)</term><term>Adult (MeSH)</term><term>Analysis of Variance (MeSH)</term><term>Auditory Perception (physiology)</term><term>Brain (diagnostic imaging)</term><term>Brain (physiology)</term><term>Brain Mapping (MeSH)</term><term>Female (MeSH)</term><term>Humans (MeSH)</term><term>Image Processing, Computer-Assisted (MeSH)</term><term>Learning (physiology)</term><term>Magnetic Resonance Imaging (MeSH)</term><term>Male (MeSH)</term><term>Music (MeSH)</term><term>Neural Pathways (diagnostic imaging)</term><term>Neural Pathways (physiology)</term><term>Neuronal Plasticity (physiology)</term><term>Psychomotor Performance (physiology)</term><term>Time Factors (MeSH)</term><term>Young Adult (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte (MeSH)</term><term>Analyse de variance (MeSH)</term><term>Apprentissage (physiologie)</term><term>Cartographie cérébrale (MeSH)</term><term>Encéphale (imagerie diagnostique)</term><term>Encéphale (physiologie)</term><term>Facteurs temps (MeSH)</term><term>Femelle (MeSH)</term><term>Humains (MeSH)</term><term>Imagerie par résonance magnétique (MeSH)</term><term>Jeune adulte (MeSH)</term><term>Musique (MeSH)</term><term>Mâle (MeSH)</term><term>Perception auditive (physiologie)</term><term>Performance psychomotrice (physiologie)</term><term>Plasticité neuronale (physiologie)</term><term>Stimulation acoustique (MeSH)</term><term>Traitement d'image par ordinateur (MeSH)</term><term>Voies nerveuses (imagerie diagnostique)</term><term>Voies nerveuses (physiologie)</term></keywords><keywords scheme="MESH" qualifier="diagnostic imaging" xml:lang="en"><term>Brain</term><term>Neural Pathways</term></keywords><keywords scheme="MESH" qualifier="imagerie diagnostique" xml:lang="fr"><term>Encéphale</term><term>Voies nerveuses</term></keywords><keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Apprentissage</term><term>Encéphale</term><term>Perception auditive</term><term>Performance psychomotrice</term><term>Plasticité neuronale</term><term>Voies nerveuses</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Auditory Perception</term><term>Brain</term><term>Learning</term><term>Neural Pathways</term><term>Neuronal Plasticity</term><term>Psychomotor Performance</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Acoustic Stimulation</term><term>Adult</term><term>Analysis of Variance</term><term>Brain Mapping</term><term>Female</term><term>Humans</term><term>Image Processing, Computer-Assisted</term><term>Magnetic Resonance Imaging</term><term>Male</term><term>Music</term><term>Time Factors</term><term>Young Adult</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte</term><term>Analyse de variance</term><term>Cartographie cérébrale</term><term>Facteurs temps</term><term>Femelle</term><term>Humains</term><term>Imagerie par résonance magnétique</term><term>Jeune adulte</term><term>Musique</term><term>Mâle</term><term>Stimulation acoustique</term><term>Traitement d'image par ordinateur</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Playing music requires a strong coupling of perception and action mediated by multimodal integration of brain regions, which can be described as network connections measured by anatomical and functional correlations between regions. However, the structural and functional connectivities within and between the auditory and sensorimotor networks after long-term musical training remain largely uninvestigated. Here, we compared the structural connectivity (SC) and resting-state functional connectivity (rs-FC) within and between the two networks in 29 novice healthy young adults before and after musical training (piano) with those of another 27 novice participants who were evaluated longitudinally but with no intervention. In addition, a correlation analysis was performed between the changes in FC or SC with practice time in the training group. As expected, participants in the training group showed increased FC within the sensorimotor network and increased FC and SC of the auditory-motor network after musical training. Interestingly, we further found that the changes in FC within the sensorimotor network and SC of the auditory-motor network were positively correlated with practice time. Our results indicate that musical training could induce enhanced local interaction and global integration between musical performance-related regions, which provides insights into the mechanism of brain plasticity in young adults.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">29400420</PMID><DateCompleted><Year>2019</Year><Month>02</Month><Day>12</Day></DateCompleted><DateRevised><Year>2020</Year><Month>06</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1097-0193</ISSN><JournalIssue CitedMedium="Internet"><Volume>39</Volume><Issue>5</Issue><PubDate><Year>2018</Year><Month>05</Month></PubDate></JournalIssue><Title>Human brain mapping</Title><ISOAbbreviation>Hum Brain Mapp</ISOAbbreviation></Journal><ArticleTitle>Musical training induces functional and structural auditory-motor network plasticity in young adults.</ArticleTitle><Pagination><MedlinePgn>2098-2110</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1002/hbm.23989</ELocationID><Abstract><AbstractText>Playing music requires a strong coupling of perception and action mediated by multimodal integration of brain regions, which can be described as network connections measured by anatomical and functional correlations between regions. However, the structural and functional connectivities within and between the auditory and sensorimotor networks after long-term musical training remain largely uninvestigated. Here, we compared the structural connectivity (SC) and resting-state functional connectivity (rs-FC) within and between the two networks in 29 novice healthy young adults before and after musical training (piano) with those of another 27 novice participants who were evaluated longitudinally but with no intervention. In addition, a correlation analysis was performed between the changes in FC or SC with practice time in the training group. As expected, participants in the training group showed increased FC within the sensorimotor network and increased FC and SC of the auditory-motor network after musical training. Interestingly, we further found that the changes in FC within the sensorimotor network and SC of the auditory-motor network were positively correlated with practice time. Our results indicate that musical training could induce enhanced local interaction and global integration between musical performance-related regions, which provides insights into the mechanism of brain plasticity in young adults.</AbstractText><CopyrightInformation>© 2018 Wiley Periodicals, Inc.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Qiongling</ForeName><Initials>Q</Initials><AffiliationInfo><Affiliation>School of Biological Science & Medical Engineering, Beihang University, Beijing, 100083, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 102402, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Xuetong</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>School of Biological Science & Medical Engineering, Beihang University, Beijing, 100083, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 102402, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Shaoyi</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>School of Biological Science & Medical Engineering, Beihang University, Beijing, 100083, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 102402, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Xie</LastName><ForeName>Yongqi</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>School of Biological Science & Medical Engineering, Beihang University, Beijing, 100083, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 102402, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Xinwei</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>School of Biological Science & Medical Engineering, Beihang University, Beijing, 100083, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 102402, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Xie</LastName><ForeName>Yachao</ForeName><Initials>Y</Initials><AffiliationInfo><Affiliation>State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, 100875, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Shuyu</ForeName><Initials>S</Initials><Identifier Source="ORCID">0000-0002-3459-6821</Identifier><AffiliationInfo><Affiliation>School of Biological Science & Medical Engineering, Beihang University, Beijing, 100083, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, 102402, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2018</Year><Month>02</Month><Day>05</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Hum Brain Mapp</MedlineTA><NlmUniqueID>9419065</NlmUniqueID><ISSNLinking>1065-9471</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000161" MajorTopicYN="N">Acoustic Stimulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000704" MajorTopicYN="N">Analysis of Variance</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001307" MajorTopicYN="N">Auditory Perception</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D001921" MajorTopicYN="N">Brain</DescriptorName><QualifierName UI="Q000000981" MajorTopicYN="N">diagnostic imaging</QualifierName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D001931" MajorTopicYN="N">Brain 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