ArticleScientific reports2022
Resting state fast brain dynamics predict interindividual variability in motor performance.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- The Dynamic Interaction between Procedural Learning and Resting-State Functional Connectivity in Self-Limited Focal Epilepsies.Brain topography · 2025Article
- Neural signatures of online and offline motor learning: An ALE meta-analysis.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Encoding Manual Dexterity through Modulation of Intrinsic α Band Connectivity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Motor learning- and consolidation-related resting state fast and slow brain dynamics across wake and sleep.Scientific reports · 2024Article
- The dissociative role of bursting and non-bursting neural activity in the oscillatory nature of functional brain networks.Imaging neuroscience (Cambridge, Mass.) · 2024Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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 trait-like relationship between spontaneous transient neural dynamics at rest and interindividual variations in motor abilities. On the other hand, transient RS dynamics seem not to be state-dependent, i.e., modulated by learning experience and reflect neural plasticity, at least on the short timescale.
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Registered trials
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