Predicting future learning from baseline network architecture.

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TitlePredicting future learning from baseline network architecture.
Publication TypeJournal Article
Year of Publication2018
AuthorsMattar, MG, Wymbs, NF, Bock, AS, Aguirre, GK, Grafton, ST, Bassett, DS
JournalNeuroimage
Volume172
Pagination107-117
Date Published2018 05 15
ISSN1095-9572
KeywordsAdult, Brain, Brain Mapping, Female, Humans, Individuality, Learning, Magnetic Resonance Imaging, Male, Motor Skills, Nerve Net
Abstract

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.

DOI10.1016/j.neuroimage.2018.01.037
Alternate JournalNeuroimage
PubMed ID29366697
PubMed Central IDPMC5910215
Grant ListR01 HD086888 / HD / NICHD NIH HHS / United States
R01 NS099348 / NS / NINDS NIH HHS / United States
R21 MH106799 / MH / NIMH NIH HHS / United States