A Cautionary Tale of Self-Supervised Learning for Imaging Biomarkers: Alzheimer's Disease Case Study

TL;DR

This paper introduces R-NCE, a novel self-supervised learning framework for MRI-based Alzheimer's biomarkers, outperforming existing methods and showing biological relevance through genetic and cellular associations.

Contribution

We propose R-NCE, a new SSL method that combines auxiliary features with augmentation-invariant learning, improving biomarker discovery for Alzheimer's disease.

Findings

R-NCE outperforms traditional features in disease classification and prediction tasks.

R-NCE-derived Brain Age Gap shows high heritability and disease-related genetic associations.

Biological analysis links R-NCE biomarkers to neurodegenerative and cerebrovascular processes.

Abstract

Discovery of sensitive and biologically grounded biomarkers is essential for early detection and monitoring of Alzheimer's disease (AD). Structural MRI is widely available but typically relies on hand-crafted features such as cortical thickness or volume. We ask whether self-supervised learning (SSL) can uncover more powerful biomarkers from the same data. Existing SSL methods underperform FreeSurfer-derived features in disease classification, conversion prediction, and amyloid status prediction. We introduce Residual Noise Contrastive Estimation (R-NCE), a new SSL framework that integrates auxiliary FreeSurfer features while maximizing additional augmentation-invariant information. R-NCE outperforms traditional features and existing SSL methods across multiple benchmarks, including AD conversion prediction. To assess biological relevance, we derive Brain Age Gap (BAG) measures and perform genome-wide association studies. R-NCE-BAG shows high heritability and associations with MAPT and IRAG1, with enrichment in astrocytes and oligodendrocytes, indicating sensitivity to neurodegenerative and cerebrovascular processes.