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Predicting future learning from baseline network architecture
Human behavior and cognition result from a complex pattern of interactions between brain regions. The flexible reconfiguration of these patterns enables behavioral adaptation, such as the acquisition of a new motor skill. Yet, the degree to which these reconfigurations depend on the brain's bas...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5910215/ https://www.ncbi.nlm.nih.gov/pubmed/29366697 http://dx.doi.org/10.1016/j.neuroimage.2018.01.037 |
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author | Mattar, Marcelo G. Wymbs, Nicholas F. Bock, Andrew S. Aguirre, Geoffrey K. Grafton, Scott T. Bassett, Danielle S. |
author_facet | Mattar, Marcelo G. Wymbs, Nicholas F. Bock, Andrew S. Aguirre, Geoffrey K. Grafton, Scott T. Bassett, Danielle S. |
author_sort | Mattar, Marcelo G. |
collection | PubMed |
description | Human behavior and cognition result from a complex pattern of interactions between brain regions. The flexible reconfiguration of these patterns enables behavioral adaptation, such as the acquisition of a new motor skill. Yet, the degree to which these reconfigurations depend on the brain's baseline sensorimotor integration is far from understood. Here, we asked whether spontaneous fluctuations in sensorimotor networks at baseline were predictive of individual differences in future learning. We analyzed functional MRI data from 19 participants prior to six weeks of training on a new motor skill. We found that visual-motor connectivity was inversely related to learning rate: sensorimotor autonomy at baseline corresponded to faster learning in the future. Using three additional scans, we found that visual-motor connectivity at baseline is a relatively stable individual trait. These results suggest that individual differences in motor skill learning can be predicted from sensorimotor autonomy at baseline prior to task execution. |
format | Online Article Text |
id | pubmed-5910215 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
record_format | MEDLINE/PubMed |
spelling | pubmed-59102152019-05-15 Predicting future learning from baseline network architecture Mattar, Marcelo G. Wymbs, Nicholas F. Bock, Andrew S. Aguirre, Geoffrey K. Grafton, Scott T. Bassett, Danielle S. Neuroimage Article Human behavior and cognition result from a complex pattern of interactions between brain regions. The flexible reconfiguration of these patterns enables behavioral adaptation, such as the acquisition of a new motor skill. Yet, the degree to which these reconfigurations depend on the brain's baseline sensorimotor integration is far from understood. Here, we asked whether spontaneous fluctuations in sensorimotor networks at baseline were predictive of individual differences in future learning. We analyzed functional MRI data from 19 participants prior to six weeks of training on a new motor skill. We found that visual-motor connectivity was inversely related to learning rate: sensorimotor autonomy at baseline corresponded to faster learning in the future. Using three additional scans, we found that visual-motor connectivity at baseline is a relatively stable individual trait. These results suggest that individual differences in motor skill learning can be predicted from sensorimotor autonomy at baseline prior to task execution. 2018-01-28 2018-05-15 /pmc/articles/PMC5910215/ /pubmed/29366697 http://dx.doi.org/10.1016/j.neuroimage.2018.01.037 Text en This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Mattar, Marcelo G. Wymbs, Nicholas F. Bock, Andrew S. Aguirre, Geoffrey K. Grafton, Scott T. Bassett, Danielle S. Predicting future learning from baseline network architecture |
title | Predicting future learning from baseline network architecture |
title_full | Predicting future learning from baseline network architecture |
title_fullStr | Predicting future learning from baseline network architecture |
title_full_unstemmed | Predicting future learning from baseline network architecture |
title_short | Predicting future learning from baseline network architecture |
title_sort | predicting future learning from baseline network architecture |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5910215/ https://www.ncbi.nlm.nih.gov/pubmed/29366697 http://dx.doi.org/10.1016/j.neuroimage.2018.01.037 |
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