Robust brain age estimation from structural MRI with contrastive learning
Journal:
arXiv
Published Date:
Jul 2, 2025
Abstract
Estimating brain age from structural MRI has emerged as a powerful tool for
characterizing normative and pathological aging. In this work, we explore
contrastive learning as a scalable and robust alternative to supervised
approaches for brain age estimation. We introduce a novel contrastive loss
function, $\mathcal{L}^{exp}$, and evaluate it across multiple public
neuroimaging datasets comprising over 20,000 scans. Our experiments reveal four
key findings. First, scaling pre-training on diverse, multi-site data
consistently improves generalization performance, cutting external mean
absolute error (MAE) nearly in half. Second, $\mathcal{L}^{exp}$ is robust to
site-related confounds, maintaining low scanner-predictability as training size
increases. Third, contrastive models reliably capture accelerated aging in
patients with cognitive impairment and Alzheimer's disease, as shown through
brain age gap analysis, ROC curves, and longitudinal trends. Lastly, unlike
supervised baselines, $\mathcal{L}^{exp}$ maintains a strong correlation
between brain age accuracy and downstream diagnostic performance, supporting
its potential as a foundation model for neuroimaging. These results position
contrastive learning as a promising direction for building generalizable and
clinically meaningful brain representations.
