CNN-derived brain age gaps of different neurological and cardiovascular diseases in the UK Biobank and identifying affected brain regions
Bibliographic record
Abstract
The difference between chronological age and predicted biological brain age, the so-called “brain age gap”, is a promising biomarker for assessment of overall brain health. It has also been suggested as a biomarker for early detection of neurological and cardiovascular conditions. The aim of this work is to identify group-level variability in the brain age gap between healthy subjects and patients with neurological and cardiovascular diseases. Therefore, a deep convolutional neural network was trained on UK Biobank T1-weighted-MRI datasets of healthy subjects (n=6860) to predict brain age. After training, the model was used to determine the brain age gap for healthy hold-out test subjects (n=344), and subjects with neurological (n=2327) or cardiovascular (n=6467) diseases. Next, saliency maps were analyzed to identify brain regions used by the model to render decisions. Linear bias correction was implemented to correct for the bias of age predictions made by the model. The trained model after bias correction achieved an average brain age gap of 0.05 years for the healthy test cohort while the neurological disease test cohort had an average brain age gap of 0.7 years, and the cardiovascular disease test cohort had an average brain age gap of 0.25 years. The average saliency maps appear similar for the three test group, suggesting that the model mostly uses brain areas associated with general brain aging patterns. This works results indicate potential in the brain age gap for differentiation of neurologic and cardiac patients from healthy aging patterns supporting its use as a novel biomarker.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".