Repeat-consistency learning for improving repeatability of brain-age prediction from structural MRI.

Abstract

Structural magnetic resonance imaging (MRI)-based brain-age prediction is increasingly used as an imaging-derived marker of brain ageing, but good predictive accuracy does not guarantee stable estimates when the same individual is scanned repeatedly. This study proposes repeat-consistency learning (RCL), a training framework that uses same-subject, same-session repeated MRI acquisitions as paired training information. A compact three-dimensional convolutional neural network was trained using a chronological-age prediction loss together with output-level and latent feature-level consistency losses. An unweighted formulation treated all repeated-scan pairs equally, whereas a disagreement-weighted formulation reduced the influence of pairs with larger predicted-age differences. The framework was evaluated using the Open Access Series of Imaging Studies cross-sectional dataset with subject-level training, validation and held-out test partitions. Both RCL formulations showed numerically lower scan-rescan disagreement and narrower Bland-Altman limits of agreement than prediction-only training, while held-out age-prediction performance remained broadly comparable. The unweighted formulation provided the most favourable overall balance between repeatability and predictive accuracy. RCL also reduced prediction shifts under several controlled noise and affine perturbations, although substantial sensitivity to image blurring remained. These findings provide proof-of-concept evidence that repeated MRI acquisitions can be incorporated directly into model training to encourage more stable predictions and latent representations while maintaining broadly comparable chronological-age prediction performance. The observed improvements were modest and were demonstrated in a controlled, single-dataset, within-session setting. Validation in larger, multi-site, multi-scanner and longitudinal datasets is therefore required to determine the broader robustness, generalisability and practical utility of the approach.