Regional patch-based MRI brain age modeling with an interpretable cognitive reserve proxy
Bibliographic record
Abstract
Accurate brain age prediction from MRI is a promising biomarker for brain health and neurodegenerative disease risk, but current deep learning models often lack anatomical specificity and clinical insight. We present a regional patch-based ensemble framework that uses 3D Convolutional Neural Networks (CNNs) trained on bilateral patches from ten subcortical structures, enhancing anatomical sensitivity. Ensemble predictions are combined with cognitive assessments to derive a cognitively informed proxy for cognitive reserve (CR-Proxy), quantifying resilience to age-related brain changes. We train our framework on a large, multi-cohort dataset of healthy controls and test it on independent samples that include individuals with Alzheimer’s disease and mild cognitive impairment. The results demonstrate that our method achieves robust brain age prediction and provides a practical, interpretable CR-Proxy capable of distinguishing diagnostic groups and identifying individuals with high or low cognitive reserve. This pipeline offers a scalable, clinically accessible tool for early risk assessment and personalized brain health monitoring. • Regional patch-based ensemble model enhances brain age prediction using 3D CNNs on 10 subcortical structures. • Cognitive Reserve Proxy (CR-Proxy) combines brain age estimates with MMSE scores for resilience assessment. • CR-Proxy distinguishes diagnostic groups: AD, MCI, and cognitively normal with high significance. • Ensemble model achieves MAE of 2.93 years, outperforming individual regional predictors. • Framework provides scalable biomarker for early neurodegenerative risk stratification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".