Resting state fast brain dynamics predict interindividual variability in motor performance.

ABSTRACT: Motor learning features rapid enhancement during practice then offline post-practice gains with the reorganization of related brain networks. We hypothesised that fast transient, sub-second variations in magnetoencephalographic (MEG) network activity during the resting-state (RS) reflect early learning-related plasticity mechanisms and/or interindividual motor variability in performance. MEG RS activity was recorded before and 20 min after motor learning. Hidden Markov modelling (HMM) of MEG power envelope signals highlighted 8 recurrent topographical states. For two states, motor performance levels were associated with HMM temporal parameters both in pre- and post-learning resting-state sessions. However, no association emerged with offline changes in performance. These results suggest a

SUBMITTER: Roshchupkina L 

PROVIDER: S-EPMC8964712 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature