par Roshchupkina, Liliia 
Président du jury Destrebecqz, Arnaud
Promoteur Peigneux, Philippe
Publication Non publié, 2023-12-13
Président du jury Destrebecqz, Arnaud
Promoteur Peigneux, Philippe
Publication Non publié, 2023-12-13
Thèse de doctorat
| Résumé : | Motor skills dynamically evolve during practice and after training. We investigated, using magnetoencephalography, the neural dynamics underpinning motor learning and its consolidation in relation to sleep during resting-state periods after the end of learning (boost window, within 30 min) and at delayed time scales (silent 4h and next day 24h windows) with an intermediate nap or wakefulness. Resting-state neural dynamics (NDs) were investigated at fast (sub-second) and slower (minutes) timescales using Hidden Markov modelling (HMM) and functional connectivity (FC), respectively, with their relationship to the evolution of motor performance. HMM results show that fast dynamic activities in a Temporal/Sensorimotor state network predict individual motor performance, suggesting a trait-like association between rapidly recurrent neural patterns and motor behaviour. Short, post-training task re-exposure modulated slow NDs neural network characteristics during the boost but not in the silent window. Furthermore, the induction effects re-emerged on the next day, but to a lesser extent than during the boost window. Daytime naps did not modulate memory consolidation at behavioural and neural levels. These results emphasise the critical role of the transient boost window in motor learning and memory consolidation and provide further insights into the relationship between the multiscale neural dynamics of brain networks, motor learning, and consolidation. |
