Brain plasticity underlying sleep-dependent motor consolidation after motor imagery
Bibliographic record
Abstract
Motor imagery can, similarly to physical practice, improve motor performance through experience-based plasticity. Using magnetoencephalography, we investigated changes in brain activity associated with offline consolidation of motor sequence learning through physical practice or motor imagery. After an initial training session with either physical practice or motor imagery, participants underwent overnight consolidation. As control condition, participants underwent wake-related consolidation after training with motor imagery. Behavioral analyses revealed that overnight consolidation of motor learning through motor imagery outperformed wake-related consolidation (95% CI [0.02, 0.07], P < 0.001, RP2 = 0.05). As regions of interest, we selected the generators of event-related synchronization/desynchronization of alpha (8-12 Hz) and beta (15-30 Hz) oscillations, which predicted the level of performance on the motor sequence. This yielded a primary sensorimotor-premotor network for alpha oscillations and a cortico-cerebellar network for beta oscillations. The alpha network exhibited increased neural desynchronization after overnight consolidation compared to wake-related consolidation. By contrast, the beta network exhibited an increase in neural synchronization after wake-related consolidation compared to overnight consolidation. We provide the first evidence of parallel brain plasticity underlying behavioral changes associated with sleep-dependent consolidation of motor skill learning through motor imagery and physical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".