The negative relationship between brain-age gap and psychological resilience defines the age-related neurocognitive status in older people
Bibliographic record
Abstract
Biological brain age is a brain-predicted age using machine learning to indicate brain health and its associated conditions. The presence of an older predicted brain age relative to the actual chronological age is indicative of accelerated aging processes. Consequently, the disparity between the brain's chronological age and its predicted age (brain-age gap) and the factors influencing this disparity provide critical insights into cerebral health dynamics during aging. In this study, we employed a Lasso regression model and analyzed multimodal imaging data from 124 participants aged 53 to 76 to formulate and predict brain age. Additionally, we conducted partial correlation analyses to explore the complex relationship between the brain-age gap and network metrics, cognitive assessments, and emotional evaluations, while controlling for chronological age, gender, and education. Our findings highlight psychological resilience as a significant mitigating factor against premature brain aging. It is established that psychological resilience significantly influences the modulation of the brain-age gap. Moreover, psychological resilience and the brain-age gap exhibit a high accuracy (above 0.72) in segregating Montreal Cognitive Assessment score-based cohorts. This observation underscores significant insight into the potential of utilizing the brain-age gap as a diagnostic tool for the early detection of accelerated aging. It advocates for the timely application of interventions, including the development of programs aimed at bolstering psychological resilience.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".