Brain Aging in Patients With Cardiovascular Disease From the <scp>UK</scp> Biobank
Bibliographic record
Abstract
The brain undergoes complex but normal structural changes during the aging process in healthy adults, whereas deviations from the normal aging patterns of the brain can be indicative of various conditions as well as an increased risk for the development of diseases. The brain age gap (BAG), which is defined as the difference between the chronological age and the machine learning-predicted biological age of an individual, is a promising biomarker for determining whether an individual deviates from normal brain aging patterns. While the BAG has shown promise for various neurological diseases and cardiovascular risk factors, its utility to quantify brain changes associated with diagnosed cardiovascular diseases has not been investigated to date, which is the aim of this study. T1-weighted MRI scans from healthy participants in the UK Biobank were used to train a convolutional neural network (CNN) model for biological brain age prediction. The trained model was then used to quantify and compare the BAGs for all participants in the UK Biobank with known cardiovascular diseases, as well as healthy controls and patients with known neurological diseases for benchmark comparisons. Saliency maps were computed for each individual to investigate whether brain regions used for biological brain age prediction by the CNN differ between groups. The analyses revealed significant differences in BAG distributions for 10 of the 42 sex-specific cardiovascular disease groups investigated compared to healthy participants, indicating disease-specific variations in brain aging. However, no significant differences were found regarding the brain regions used for brain age prediction as determined by saliency maps, indicating that the model mostly relied on healthy brain aging patterns, even in the presence of cardiovascular diseases. Overall, the findings of this work demonstrate that the BAG is a sensitive imaging biomarker to detect differences in brain aging associated with specific cardiovascular diseases. This further supports the theory of the heart-brain axis by exemplifying that many cardiovascular diseases are associated with atypical brain aging.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".