Shaping the structural dynamics of motor learning through cueing during sleep
Bibliographic record
Abstract
Enhancing the retention of recent memory traces through sleep reactivation is possible via Targeted memory reactivation (TMR), involving cueing learned material during posttraining sleep. Evidence indicates detectable short-term microstructural changes in the brain within an hour after motor sequence learning, and posttraining sleep is believed to contribute to the consolidation of these motor memories, potentially leading to enduring microstructural changes. In this study, we explored how TMR during posttraining sleep affects performance gains and delayed microstructural remodeling, using both standard diffusion tensor imaging and advanced neurite orientation dispersion and density imaging. Sixty healthy young adults participated in a 5 days protocol, undergoing five diffusion-weighted imaging sessions, pre- and post-two motor sequence training sessions, and after a posttraining night of either regular sleep (RS) or TMR. Results demonstrated rapid skill acquisition on day 1, followed by performance stabilization on day 2, and improvement on day 5, in both RS and TMR groups. (Re)training induced widespread microstructural changes in motor-related areas, initially involving the hippocampus, followed by a delayed engagement of the caudate nucleus. Mean Diffusivity changes were accompanied by increased neurite density index in the putamen, suggesting increased neurite density, while free water fraction reduction indicated glial reorganization. TMR-related structural differences emerged in the dorsolateral prefrontal cortex on day 2 and the right cuneus on day 5, suggesting unique sleep TMR-related neural reorganization patterns. Persistence of practice-related structural changes, although moderated over time, suggests a lasting neural network reorganization, partially mediated by sleep TMR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".