A Cautionary Tale of Self-Supervised Learning for Imaging Biomarkers: Alzheimer's Disease Case Study
This work addresses the need for better biomarkers for Alzheimer's disease detection and monitoring, but it is incremental as it builds on existing self-supervised learning methods by integrating auxiliary features.
The study tackled the problem of discovering more powerful imaging biomarkers for Alzheimer's disease using self-supervised learning, and found that their new method, Residual Noise Contrastive Estimation, outperformed traditional features and existing SSL methods in tasks like disease classification and conversion prediction, with concrete improvements in benchmarks and biological relevance through associations with specific genes.
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.