Physical Activity Moderates The Association Between Response Inhibition And Anterior Cingulate Cortex In Older Adults
Bibliographic record
Abstract
Aging is generally associated with a decline in executive function, a cognitive domain that is critical to functional independence. Response inhibition is an executive process that contributes to anticipation, planning, and goal setting. The anterior cingulate cortex (ACC) is a key brain structure that supports response inhibition and shows grey matter loss with age. Evidence suggests that both response inhibition performance and the ACC are responsive to exercise training. Whether physical activity moderates the relationship between response inhibition and the ACC in older adults is unknown. PURPOSE To determine if physical activity level moderates the association between response inhibition performance and ACC grey matter volume in community-dwelling older adults. METHODS A cross-sectional analysis of a subset of community-dwelling older adults enrolled in a 3-arm, 8-week proof of concept randomized control trial of computerized cognitive training, with or without exercise, who completed T1 magnetic resonance imaging (MRI) at baseline. Of the 68 MRI participants, 11 were removed from this analysis due to poor scan quality. Response inhibition was measured using the Flanker Task from the NIH Toolbox Cognitive Battery. Participants were split into either a high or low physical activity groups determined by their median Physical Activity Scale for the Elderly (PASE) score. Caudal and rostral ACC volumes were segmented and quantified using FreeSurfer. To assess for moderation, we performed linear regressions with age-adjusted Flanker Task score as the dependent variable, and ACC volume, high/low PASE, and the interaction of ACC volume * high/low PASE as independent variables. RESULTS Physical activity level significantly moderated the association between left caudal anterior cingulate cortex (LCACC) volume and age-adjusted Flanker Task score, (β = -6.428, t(53) = -2.075, p < .05). Among those with low physical activity levels (n = 30), greater LCACC volume was associated with better response inhibition (r = 0.44, p = .01) while there was no association among those with high physical activity levels (n = 27; r = -0.13; p = .52). CONCLUSION Higher physical activity may mitigate the negative effects of age-related gray matter loss in the ACC on response inhibition performance in community-dwelling older adults.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".