The correlation between muscle strength, inhibitory function, and cognitive function in older adults with cognitive impairment: evidence from resting-state electroencephalography
Bibliographic record
Abstract
Objective This study explored the associations among muscle strength, inhibitory control, and cognitive function in older adults with cognitive impairment, focusing on related EEG changes. Methods Among 247 adults aged 70 and above, 120 with cognitive impairment were included. Assessments included the Montreal Cognitive Assessment (MoCA), Stroop task, grip strength, and resting-state EEG. Results Inhibitory control was significantly related to cognitive function: accuracy in congruent (r = 0.599, p < 0.001) and incongruent (r = 0.474, p < 0.001) conditions correlated positively with MoCA scores, while response times in both conditions showed negative correlations (r = –0.475 and –0.354, respectively, p < 0.001). EEG alpha1 (C3, P3, F7), alpha2 (P3, F8), and beta1 (P3, F7) power were significantly associated with both cognitive and inhibitory performance. Muscle strength was significantly positively correlated with specific EEG indicators, particularly alpha1 power at C4 (r = 0.212, p < 0.05), O2 (r = 0.204, p < 0.05), F8 (r = 0.225, p < 0.05), and T6 (r = 0.206, p < 0.05), as well as alpha2 power at C3 (r = 0.216, p < 0.05), P3 (r = 0.222, p < 0.05), P4 (r = 0.268, p < 0.001), F8 (r = 0.284, p < 0.001), and T5 (r = 0.218, p < 0.05). Conclusion Muscle strength may support cognitive and inhibitory function by influencing specific EEG activities. These findings highlight the neurophysiological links among muscle strength, cognition, and brain activity, offering potential biomarkers for early detection and intervention in cognitive decline.
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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 | 0.001 | 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".