Changes occur in resting state network of motor system during 4 weeks of motor skill learning

We tested whether the resting state functional connectivity of the motor system changed during 4 weeks of motor skill learning using functional magnetic resonance imaging (fMRI). Ten healthy volunteers learned to produce a sequential finger movement by daily practice of the task over a 4 week period. Changes in the resting state motor network were examined before training (Week 0), two weeks after the onset of training (Week 2), and immediately at the end of the training (Week 4). The resting state motor system was analyzed using group independent component analysis (ICA). Statistical Parametric Mapping (SPM) second-level analysis was conducted on independent z-maps generated by the group ICA. Three regions, namely right postcentral gyrus, and bilateral supramarginal gyri were found to be sensitive to the training duration. Specifically, the strength of resting state functional connectivity in the right postcentral gyrus and right supramarginal gyrus increased from Week 0 to Week 2, during which the behavioral performance improved significantly, and decreased from Week 2 to Week 4, during which there was no more significant improvement in behavioral performance. The strength of resting state functional connectivity in left supramarginal gyrus increased throughout the training. These results confirm changes in the resting state network during slow-learning stage of motor skill learning, and support the premise that the resting state networks play a role in improving performance.

Research highlights
► We found marked changes in resting state network of motor system during slow learning stage.
► The strength of resting state functional connectivity in the right precentral & supramaginal gyri waxed then waned.
► The strength of resting state functional connectivity in the left supramaginal gyrus kept increasing.
► The data support the premise that the resting state networks play a role in improving performance.
► The data support the hypothesis that resting state networks are crucial for offline memory consolidation.