UNDERSTANDING THE INTERPLAY OF BRAIN HEALTH, COGNITION, AND AUTONOMIC FUNCTION
Bibliographic record
Abstract
Abstract Research in clinical populations focused on various age-related diseases report multi-system difficulties in autonomic regulation, motor performance, cognition, and brain health. My research investigated the relationship between brain health, chronological age, and cognition. To assess these domains, I cross analyzed Compass 31, MoCA, and Brain Age Technology. The study population included 158 participants, both healthy and unhealthy. The collected data evaluated the relationship between brain age disparity, chronological age, and autonomic function. Correlation analysis, regression modeling, and subgroup comparisons were employed to explore potential associations, age-related patterns, and autonomic function variations. The findings from this research study contributes to a deeper understanding of the complex interplay between brain health, chronological age, and autonomic function. Age, White Matter Hyperintensities, Brain Age Gap, and Brain Age Variance demonstrate high significance when correlated with the MoCA total score. MoCA and all Compass 31 domains were found to be predictive of total white matter hyperintensity volume. This finding highlights a strong relationship between all three domains and suggests that when participants score high on both assessments, they should follow up with neuroimaging to discover potential brain damage. Future studies should identify risk factors for cognitive impairment and brain health that are also symptoms for autonomic dysregulation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".