Correlation of muscle strength, information processing speed and cognitive function in the elderly with cognitive impairment——evidence from EEG
Bibliographic record
Abstract
Objective This study investigates the interplay between muscle strength, information processing speed, EEG-specific biomarkers, and cognitive function in elderly individuals with cognitive impairments, emphasizing the mediating roles of information processing speed and EEG-specific biomarkers. Method A cross-sectional study design was employed to recruit 151 elderly participants. The participants underwent grip strength and 30-s sit-to-stand tests to assess muscle strength, completed the Trail Making Test part A (TMT-A) and the Symbol Digit Modality Test (SDMT) to evaluate information processing speed, and utilized the Montreal Cognitive Assessment (MOCA) to gauge cognitive function. Additionally, EEG signals were recorded for 5 min to capture neural activity. Results The difference in information processing speed among elderly individuals with varying degrees of cognitive impairment was statistically significant (p < 0.001). A significant negative correlation was observed between the MoCA score and the time consumption of TMT-A (r = −0.402, p < 0.01), and a significant positive correlation was found between the MoCA score and the SDMT score (r = 0.609, p < 0.01). Grip strength was negatively correlated with the time consumption of TMT-A (r = −0.336, p < 0.01) and positively correlated with the SDMT score (r = 0.336, p < 0.01). A significant negative correlation was found between the 30-s sit-to-stand test and the time consumption of TMT-A (r = −0.273, p < 0.01), and a significant positive correlation was observed between the 30-s sit-to-stand test and the SDMT score (r = 0.372, p < 0.01). Additionally, we observed that the α1 power value indicators were significantly correlated with the MoCA score, the time consumption of TMT-A, and the SDMT score (all p < 0.01). The α1 power values at F7 + F8 and T5 + T6 were identified as sensitive EEG indicators for muscle strength and information processing speed. The EEG-specific indicators (B = 0.019, 95% CI: 0.003, 0.047) and information processing speed (B = 0.137, 95% CI: 0.096, 0.292) were found to partially mediate the relationship between grip strength and MoCA scores, with information processing speed exerting a stronger mediating effect. Conclusion Specific patterns were observed in the EEG of elderly individuals with cognitive impairments, which could objectively assess the risk of cognitive decline in this population. Muscle strength, information processing speed, and EEG-specific biomarkers were closely associated with cognitive function in elderly individuals. The potential pathway of interaction—muscle strength → EEG-specific biomarkers → information processing speed → cognitive function—provides valuable insights into advancing the field of cognitive research in the elderly.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".