Brain functional connectivity characteristics at various levels of inhibitory function in elderly individuals with cognitive impairment
Bibliographic record
Abstract
This study aims to explore the brain network connectivity patterns in elderly individuals with cognitive impairment at varying levels of inhibitory function and to identify key brain connectivity features that influence inhibitory function. The study analyzed data from 120 elderly individuals with cognitive impairment, including the Montreal Cognitive Assessment ( MoCA ), muscle strength, Stroop task performance, and 5-minute resting-state EEG signals. Pearson correlation and analysis of variance were used to identify significant targets. BrainNet Viewer was utilized to create visualizations of EEG-based brain networks to identify connectivity features. (1) Stroop task accuracy under congruent/incongruent conditions showed a significant positive correlation with MoCA scores (r = 0.599, p < 0.01; r = 0.474, p < 0.01), while reaction times under these conditions exhibited a significant negative correlation with MoCA scores (r=-0.475, p < 0.01; r=-0.354, p < 0.01). (2) Significant differences were observed among the four groups of elderly individuals with cognitive impairment in grip strength, 30-second sit-to-stand, and SPPB performance (P < 0.05). In EEG metrics, significant differences were identified among the four groups in Fp1θ, Fp1α1, Fp1α2, Fp2α1, Fp2α2, F3α1, F4α1, F4α2, C3α1, C4α1, C4α2, P3α1, P4α1, P4α2, O1α1, O2α1, O2α2, F7α1, F8α1, F8α2, T4α1, T4α2, T5α1, T6α1, T6α2 (P < 0.05). (3) Higher accuracy in inhibitory function was mainly associated with stronger and denser connectivity in the prefrontal and parietal regions, whereas faster reaction times were linked to the central and occipital regions. The observed balance in connectivity between the left and right hemispheres was associated with differences in inhibitory function and task execution efficiency in elderly individuals with cognitive impairment. Training in physical function and muscle strength may enhance EEG activity in individuals with lower levels of inhibitory function, thereby improving their cognitive abilities.
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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 | 0.001 |
| 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".