Motor learning- and consolidation-related resting state fast and slow brain dynamics across wake and sleep

Abstract

Learning and consolidation of motor skills dynamically evolve both online during practice and offline after training. We investigated, using magnetoencephalography, the neural dynamics underpinning motor learning and its consolidation in relation to sleep during resting-state periods shortly after the end of learning (short-term boost window, within 30 min) and at more delayed time scales (silent 4h and next day 24h windows) with an intermediate nap or wakefulness after the boost window. Resting-state neural dynamics in brain networks were investigated at fast (sub-second) and slower (supra-second) timescales using Hidden Markov modelling (HMM) and resting-state functional connectivity (rsFC), respectively, as well as their relationship with the evolution of motor performance. HMM results show that fast dynamic activities in a Temporal/Sensorimotor state network predict individual motor performance achievements, suggesting a trait-like association between rapidly recurrent neural patterns and motor behaviour. Short, post-training re-exposure to the task modulated fast and slow network characteristics during the boost but not in the silent window. These short practice-related induction effects were observed again on the next day, to a reduced extent as compared to the boost window. Daytime naps did not significantly modulate memory consolidation both at behavioural and neural levels. These results emphasise the critical role of the transient boost window in motor learning and subsequent memory consolidation processes and provide further insights into the relationship between the multiscale neural dynamics of brain networks, motor learning, and consolidation.