MRI-Based Brain Age Estimation with Supervised Contrastive Learning of Continuous Representation

TL;DR

This paper introduces a novel supervised contrastive learning approach using Rank-N-Contrast loss for more accurate MRI-based brain age estimation, outperforming traditional methods and providing insights into neurodegenerative disease biomarkers.

Contribution

It is the first to apply supervised contrastive learning with RNC loss to brain age estimation from MRI, improving accuracy and interpretability over existing deep regression models.

Findings

Achieved MAE of 4.27 years and R^2 of 0.93 with limited data.

Outperformed conventional deep regression methods.

Revealed age-related features and disease correlations using Grad-RAM.

Abstract

MRI-based brain age estimation models aim to assess a subject's biological brain age based on information, such as neuroanatomical features. Various factors, including neurodegenerative diseases, can accelerate brain aging and measuring this phenomena could serve as a potential biomarker for clinical applications. While deep learning (DL)-based regression has recently attracted major attention, existing approaches often fail to capture the continuous nature of neuromorphological changes, potentially resulting in sub-optimal feature representation and results. To address this, we propose to use supervised contrastive learning with the recent Rank-N-Contrast (RNC) loss to estimate brain age based on widely used T1w structural MRI for the first time and leverage Grad-RAM to visually explain regression results. Experiments show that our proposed method achieves a mean absolute error (MAE) of 4.27 years and an $R^2$ of 0.93 with a limited dataset of training samples, significantly outperforming conventional deep regression with the same ResNet backbone while performing better or comparably with the state-of-the-art methods with significantly larger training data. Furthermore, Grad-RAM revealed more nuanced features related to age regression with the RNC loss than conventional deep regression. As an exploratory study, we employed the proposed method to estimate the gap between the biological and chronological brain ages in Alzheimer's Disease and Parkinson's disease patients, and revealed the correlation between the brain age gap and disease severity, demonstrating its potential as a biomarker in neurodegenerative disorders.