Robust brain age estimation from structural MRI with contrastive learning

2025-07-02

AI Summary

Structural MRI-based brain age estimation suffers from poor cross-site generalizability and severe scanner-induced bias. Method: We propose an unsupervised contrastive learning framework for brain age modeling, centered on a novel exponential contrastive loss ℒ^exp that enhances representation robustness and cross-center generalization. Contribution/Results: This is the first study to validate clinical consistency of contrastive learning models in brain age gap (BAG) analysis, longitudinal tracking, and Alzheimer’s disease (AD) detection. Pretraining scale strongly correlates with both brain age prediction accuracy and downstream diagnostic performance. External validation demonstrates nearly 50% reduction in mean absolute error (MAE) and substantial attenuation of scanner predictability. The model accurately identifies accelerated aging in cohorts with cognitive impairment and AD. This work establishes the first self-supervised, clinically oriented foundation model paradigm for neuroimaging-based diagnosis.

Technology Category

• Machine Learning: Unsupervised & Self-Supervised Learning
• Computer Vision: Large Vision Models
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Robust brain age estimation from structural MRI scans
• Overcoming site-related confounds in neuroimaging data
• Detecting accelerated aging in cognitive impairment patients

Innovation

• Contrastive learning for brain age estimation
• Novel contrastive loss function L_exp
• Scalable pre-training on diverse datasets