Sleep strengthens resting-state functional communication between brain areas involved in the consolidation of problem-solving skills
Bibliographic record
Abstract
Sleep consolidates procedural memory for motor skills, and this process is associated with strengthened functional connectivity in hippocampal-striatal-cortical areas. It is unknown whether similar processes occur for procedural memory that requires cognitive strategies needed for problem-solving. It is also unclear whether a full night of sleep is indeed necessary for consolidation to occur, compared with a daytime nap. We examined how resting-state functional connectivity within the hippocampal-striatal-cortical network differs after offline consolidation intervals of sleep, nap, or wake. Resting-state fMRI data were acquired immediately before and after training on a procedural problem-solving task that requires the acquisition of a novel cognitive strategy and immediately prior to the retest period (i.e., following the consolidation interval). ROI to ROI and seed to whole-brain functional connectivity analyses both specifically and consistently demonstrated strengthened hippocampal-prefrontal functional connectivity following a period of sleep versus wake. These results were associated with task-related gains in behavioral performance. Changes in functional communication were also observed between groups using the striatum as a seed. Here, we demonstrate that at the behavioral level, procedural strategies benefit from both a nap and a night of sleep. However, a full night of sleep is associated with enhanced functional communication between regions that support problem-solving skills.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.002 | 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".