The independent associations of physical activity and sleep with neural activity during an inhibitory task: cross‐sectional results from the <scp>MONITOR‐OA</scp> study
Bibliographic record
Abstract
Sleep and physical activity (PA) are important for the maintenance of executive functions. Whether these lifestyle factors independently contribute to associated neural correlates of executive functions is unknown. We therefore investigated the independent associations of PA and sleep with neural activity during executive performance using task-based functional magnetic resonance imaging (fMRI). Baseline data from a subset of participants (n = 29) enrolled in a randomised trial were used for this cross-sectional analysis. We measured PA, sleep duration and efficiency for 7 days using the SenseWear Mini and examined neural activity underlying response inhibition using the Go/NoGo executive performance task. Brain activation patterns during the NoGo condition were contrasted to activation patterns during the Go condition (i.e., NoGo-Go). We constructed two separate models (controlling for age, sex, and education) to examine the independent associations of (i) PA and sleep duration; and (ii) PA and sleep efficiency with brain activation. Significant clusters were corrected for multiple comparisons (p < 0.05) to determine region-specific activation patterns. The mean (SD) participant age was 61 (9) years, and 79% were female. PA was independently associated with greater task-related blood-oxygen-level dependent (BOLD) signal activity in the left cingulate gyrus; longer sleep duration was independently associated with greater BOLD signal activity in the left putamen. Higher sleep efficiency was independently associated with increased BOLD signal activity in the left hippocampus. PA, sleep duration, and efficiency are each independently associated with greater neural activity underlying response inhibition, which further illustrates that PA and sleep are each uniquely important for brain health.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".