Towards Generalisable Brain Age Prediction Across Heterogeneous Biomechanical Imaging Data

Abstract

Brain age prediction from biomechanical brain imaging is a promising approach for identifying early signs of neurodegeneration. By estimating the deviation between predicted and chronological age, it is possible to quantify accelerated brain aging, which can serve as a sensitive biomarker for disease risk. Biome chanical properties derived from Magnetic Resonance Elastography (MRE) offer greater age sensitivity than anatomical measures and can reveal early neurodegenerative changes in multicentre datasets. Consistent performance across heterogeneous datasets poses a challenge, as demographic variability and acquisition-site differences can introduce confounding effects that limit generalisation. To address this, we investigate con trastive learning approaches tailored for regression, enabling models to capture fine-grained biomechanical patterns associated with ageing. Furthermore, these are combined with and compared to different domain adaptation strategies, including an adversarial classifier and distribution measure penalties such as Maximum Mean Discrepancy (MMD) and Hilbert Schmidt independence criterion (HSIC), designed to reduce site- and demographic-specific biases while preserving biologically meaningful variation. Our results show that the model combining contrastive regression with MMD achieved the most accurate brain age estimation. How ever, improvements in predictive accuracy did not consistently correspond to reductions in domain-specific confounding. This underscores the importance of evaluating both accuracy and robustness when developing generalisable biomarkers for brain ageing. Through this integration and systematic comparison, the work aims to identify representation learning strategies that improve generalisation in brain age prediction, sup porting the development of biomechanical imaging as a reliable biomarker in multi-centre studies.