Brain Age: A Promising Biomarker for Understanding Aging in the Context of Cognitive Reserve
Bibliographic record
Abstract
ABSTRACT INTRODUCTION Cognitive decline is a major concern in aging populations. Detecting it before clinical symptoms emerge remains a significant challenge. A precise, reliable, and non-invasive biomarker for cognitive health could revolutionize how we monitor normal aging and lifestyle impacts. Such a tool would not only identify individuals at risk of cognitive decline years before symptoms manifest but also aid in early interventions and treatments. METHODS Longitudinal MRI data from 121 high cognitive reserve (HCR) individuals were compared to matched low cognitive reserve (LCR) individuals to evaluate four biomarkers for early cognitive decline and disease progression: brain age delta, cortical thickness, AD cortical signature, and hippocampal volume. Cross-sectional analyses were conducted at baseline, alongside longitudinal assessments spanning 1 to 12 years, to compare the performance and properties of these biomarkers. RESULTS The brain age metric emerged as the most reliable biomarker, demonstrating a significant ability to differentiate between groups at baseline ( β = 1.250, t = 3.521, p = 0.0009; linear regression model; AUC = 0.73). Furthermore, this biomarker maintained its robustness as the strongest predictor of group membership over a follow-up period of up to 12 years ( β = 0.409, p = 0.025; mixed-effects model), underscoring its potential for longitudinal monitoring of cognitive decline. DISCUSSION The brain age biomarker demonstrates potential as an effective indicator for early cognitive decline, capable of detecting changes years before clinical symptoms appear and tracking age-related brain and cognitive changes over time. These findings suggest that integrating MRI biomarkers with machine learning approaches could yield more accurate and reliable tools for assessing cognitive health, surpassing the limitations of relying solely on MRI biomarkers. Key Points Question: Which T1-weighted MRI biomarkers are most effective in predicting longitudinal cognitive deterioration in the aging population? Highlights Four commonly used MRI biomarkers were assessed in the context of cognitive aging. Brain age is validated as a promising biomarker for aging and cognitive reserve. Machine learning boosts cognitive biomarker accuracy beyond neuroimaging alone. The findings underscore a direct association between structural brain reserve and cognitive reserve. Key factors in brain preservation may support high cognitive reserve in aging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".