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Structural Covariance Analysis Reveals Differences Between Dancers and Untrained Controls
Dancers and musicians differ in brain structure from untrained individuals. Structural covariance (SC) analysis can provide further insight into training-associated brain plasticity by evaluating interregional relationships in gray matter (GM) structure. The objectives of the present study were to c...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167617/ https://www.ncbi.nlm.nih.gov/pubmed/30319377 http://dx.doi.org/10.3389/fnhum.2018.00373 |
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author | Karpati, Falisha J. Giacosa, Chiara Foster, Nicholas E. V. Penhune, Virginia B. Hyde, Krista L. |
author_facet | Karpati, Falisha J. Giacosa, Chiara Foster, Nicholas E. V. Penhune, Virginia B. Hyde, Krista L. |
author_sort | Karpati, Falisha J. |
collection | PubMed |
description | Dancers and musicians differ in brain structure from untrained individuals. Structural covariance (SC) analysis can provide further insight into training-associated brain plasticity by evaluating interregional relationships in gray matter (GM) structure. The objectives of the present study were to compare SC of cortical thickness (CT) between expert dancers, expert musicians and untrained controls, as well as to examine the relationship between SC and performance on dance- and music-related tasks. A reduced correlation between CT in the left dorsolateral prefrontal cortex (DLPFC) and mean CT across the whole brain was found in the dancers compared to the controls, and a reduced correlation between these two CT measures was associated with higher performance on a dance video game task. This suggests that the left DLPFC is structurally decoupled in dancers and may be more strongly affected by local training-related factors than global factors in this group. This work provides a better understanding of structural brain connectivity and training-induced brain plasticity, as well as their interaction with behavior in dance and music. |
format | Online Article Text |
id | pubmed-6167617 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-61676172018-10-12 Structural Covariance Analysis Reveals Differences Between Dancers and Untrained Controls Karpati, Falisha J. Giacosa, Chiara Foster, Nicholas E. V. Penhune, Virginia B. Hyde, Krista L. Front Hum Neurosci Neuroscience Dancers and musicians differ in brain structure from untrained individuals. Structural covariance (SC) analysis can provide further insight into training-associated brain plasticity by evaluating interregional relationships in gray matter (GM) structure. The objectives of the present study were to compare SC of cortical thickness (CT) between expert dancers, expert musicians and untrained controls, as well as to examine the relationship between SC and performance on dance- and music-related tasks. A reduced correlation between CT in the left dorsolateral prefrontal cortex (DLPFC) and mean CT across the whole brain was found in the dancers compared to the controls, and a reduced correlation between these two CT measures was associated with higher performance on a dance video game task. This suggests that the left DLPFC is structurally decoupled in dancers and may be more strongly affected by local training-related factors than global factors in this group. This work provides a better understanding of structural brain connectivity and training-induced brain plasticity, as well as their interaction with behavior in dance and music. Frontiers Media S.A. 2018-09-25 /pmc/articles/PMC6167617/ /pubmed/30319377 http://dx.doi.org/10.3389/fnhum.2018.00373 Text en Copyright © 2018 Karpati, Giacosa, Foster, Penhune and Hyde. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Karpati, Falisha J. Giacosa, Chiara Foster, Nicholas E. V. Penhune, Virginia B. Hyde, Krista L. Structural Covariance Analysis Reveals Differences Between Dancers and Untrained Controls |
title | Structural Covariance Analysis Reveals Differences Between Dancers and Untrained Controls |
title_full | Structural Covariance Analysis Reveals Differences Between Dancers and Untrained Controls |
title_fullStr | Structural Covariance Analysis Reveals Differences Between Dancers and Untrained Controls |
title_full_unstemmed | Structural Covariance Analysis Reveals Differences Between Dancers and Untrained Controls |
title_short | Structural Covariance Analysis Reveals Differences Between Dancers and Untrained Controls |
title_sort | structural covariance analysis reveals differences between dancers and untrained controls |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167617/ https://www.ncbi.nlm.nih.gov/pubmed/30319377 http://dx.doi.org/10.3389/fnhum.2018.00373 |
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