@article{fafafeedc5a04c4bb495ffecddbd741e,
title = "Predicting future learning from baseline network architecture",
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.",
keywords = "Brain networks, Functional connectivity, Human learning, Motor learning, Network science, fMRI",
author = "Mattar, {Marcelo G.} and Wymbs, {Nicholas F.} and Bock, {Andrew S.} and Aguirre, {Geoffrey K.} and Grafton, {Scott T.} and Bassett, {Danielle S.}",
note = "Funding Information: DSB acknowledges support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, the Army Research Laboratory through contract no. W911NF-10- 2-0022 from the U.S. Army Research Office, the Army Research Office through contract no. W911NF-14-1-0679, the Office of Naval Research Young Investigator Program, the NIH through award R01-HD086888 and 1R01HD086888-01, and the National Science Foundation awards #BCS-1441502, #BCS-1430087, and #PHY-1554488. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies. Funding Information: DSB acknowledges support from the John D. and Catherine T. MacArthur Foundation , the Alfred P. Sloan Foundation , the Army Research Laboratory through contract no. W911NF-10- 2-0022 from the U.S. Army Research Office , the Army Research Office through contract no. W911NF-14-1-0679 , the Office of Naval Research Young Investigator Program , the NIH through award R01-HD086888 and 1R01HD086888-01 , and the National Science Foundation awards #BCS-1441502 , #BCS-1430087 , and #PHY-1554488 . The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies. Publisher Copyright: {\textcopyright} 2018 The Author(s)",
year = "2018",
month = may,
day = "15",
doi = "10.1016/j.neuroimage.2018.01.037",
language = "English (US)",
volume = "172",
pages = "107--117",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",
}