Examining the Reliability of Brain Age Algorithms Under Varying Degrees of Subject Motion

Abstract

AbstractBrain age, defined as the predicted age of an individual’s brain based on neuroimaging data, shows promise as a biomarker for healthy aging and age-related neurodegenerative conditions. However, noise and motion artifacts during MRI scanning may introduce systematic bias into brain age estimates. This study leveraged a novel dataset with repeated structural MRI scans from participants during no motion, low motion, and high motion conditions. This allowed us to evaluate the impact of motion artifacts for brain age derived from 5 commonly used algorithms. Intraclass correlation coefficients, Bland-Altman analyses, and linear mixed-effect models were used to assess reliability. Results demonstrated variable resilience to motion artifacts depending on the algorithm utilized. The DeepBrainNet and pyment algorithms showed the greatest invariance to motion conditions, with high intraclass correlations and minimal mean differences on Bland-Altman plots between motion and no motion scans. In contrast, the brainageR algorithm was most affected by motion, with lower intraclass correlations and a high degree of bias. Findings elucidate the critical need for careful benchmarking of brain age algorithms on datasets with controlled motion artifacts in order to rigorously assess suitability for clinical deployment. Moreover, targeted efforts to improve model robustness to image quality and motion are warranted to strengthen the validity of brain age as a predictive biomarker. Overall, this study highlights open questions regarding the sensitivity of different brain age algorithms to noise and movement and motivates future optimization to derive biologically-meaningful brain aging metrics.