Sex Differences in Brain Age Gap Estimation Across Alzheimer's Disease Diagnostic Groups
Bibliographic record
Abstract
BACKGROUND: Brain Age Gap (BAG), the difference between age estimated from brain MRI and chronological age, is a potential feature for quantifying an individual's overall level of neurodegeneration. As a global measure, BAG can be used to examine differences in the extent of neurodegeneration across various groups. In this study, we apply BAG to amyloid positive subjects and investigate potential sex differences in different diagnostic groups. METHOD: We trained five different models on UK Biobank (UKBB) and the Mayo Clinic Study of Aging (MCSA) data, with an age range of [45, 89] and nearly balanced sex distribution, to build an ensemble model for predicting brain age from T1w images. To ensure the model's generalization within the training age range, we used robust image preprocessing methods, massive data augmentation, and model regularization techniques (Rajabli 2024). Using our brain age prediction model, without further fine-tuning, we estimated BAG on Alzheimer's Disease Neuroimaging Initiative (ADNI) samples. RESULT: We estimated the brain age gap for all cognitively normal subjects, regardless of amyloid status, and found no significant sex difference (-0.24 ± 3.85 for males, -0.05 ± 4.04 for females), indicating that our model is not biased toward either sex. As shown in previous studies and reaffirmed by our model, BAG increases along the AD trajectory (Figure 1). After correcting for age and the ADAS13 cognitive score, we found a residual statistically significant sex difference (p < 0.05) in BAG estimations, with amyloid-positive female brains appearing older than their male counterparts across all diagnostic groups except MCI, as determined by an ANCOVA test. Table 1 summarizes the statistical analysis. CONCLUSION: We showed that female brains appear older than male brains in most diagnostic groups. While we corrected for age and ADAS13 within each group, this finding may suggest that females have greater cognitive reserve than males.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".