  Stroke-induced disturbances of large-scale cortical networks are known to be
associated with the extent of motor deficits. We argue that identifying brain
networks representative of motor behavior in the resting brain would provide
significant insights for current neurorehabilitation approaches. Particularly,
we aim to investigate the global configuration of brain rhythms and their
relation to motor skill, instead of learning performance as broadly studied. We
empirically approach this problem by conducting a three-dimensional physical
space visuomotor learning experiment during electroencephalographic (EEG) data
recordings with thirty-seven healthy participants. We demonstrate that
across-subjects variations in average movement smoothness as the quantified
measure of subjects' motor skills can be predicted from the global
configuration of resting-state EEG alpha-rhythms (8-14 Hz) recorded prior to
the experiment. Importantly, this neural signature of motor skill was found to
be orthogonal to (independent of) task -- as well as to learning-related
changes in alpha-rhythms, which we interpret as an organizing principle of the
brain. We argue that disturbances of such configurations in the brain may
contribute to motor deficits in stroke, and that reconfiguring stroke patients'
brain rhythms by neurofeedback may enhance post-stroke neurorehabilitation.
