Patch-wise brain age longitudinal reliability

Abstract

We recently introduced a patch-wise technique to estimate brain age from anatomical T1-weighted magnetic resonance imaging (T1w MRI) data. Here, we sought to assess its longitudinal reliability by leveraging a unique dataset of 99 longitudinal MRI scans from a single, cognitively healthy volunteer acquired over a period of 17 years (aged 29–46 years) at multiple sites. We built a robust patch-wise brain age estimation framework on the basis of 100 cognitively healthy individuals from the MindBoggle dataset (aged 19–61 years) using the Desikan-Killiany-Tourville atlas, then applied the model to the volunteer dataset. The results show a high prediction accuracy on the independent test set (R2 =.94, mean absolute error of 0.63 years) and no statistically significant difference between manufacturers, suggesting that the patch-wise technique has high reliability and can be used for longitudinal multi-centric studies.