Brain Resting-state Functional Connectivity Mediates the Age-associated Decline in Physical Activity Engagement
Bibliographic record
Abstract
BACKGROUND: Physical activity (PA) engagement declines with age in late adulthood. Therefore, understanding factors underlying PA engagement is needed for PA promotion in older adults. Executive function is a potential key neurocognitive resource that supports PA engagement. The current study aims to provide neurobiological evidence for this hypothesis by examining the role of the executive function networks in PA engagement. METHODS: Resting-state functional magnetic resonance imaging data and self-reported PA engagement were obtained from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN; age range 18-81). The frontoparietal network and salience network were chosen as networks of interest. RESULTS: We found that PA engagement began to decline at the age of 49 via piecewise regression. Meanwhile, functional connectivity within frontoparietal network connecting posterior cingulate, parietal area, and precuneus, and functional connectivity within salience network connecting right temporo-parieto-occipital area, anterior and middle cingulate, and bilateral fronto-operculum and insula were associated with PA. The PA-associated functional connectivity within salience network mediated the age-related decline of PA engagement, which was not observed for the frontoparietal network. CONCLUSIONS: Physical activity engagement begins to decrease in middle-age, while functional connectivity between key regions related to inhibitory control and behavior regulation is a potential neural mechanism underlying this age-related decline. These findings provide neurobiological evidence for the hypothesis that aspects of executive function support PA engagement. Moreover, it also identifies potential neural targets for future PA promotion interventions.
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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 | 0.002 |
| 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".