Explainable Brain Age Prediction using coVariance Neural Networks.

ABSTRACT: In computational neuroscience, there has been an increased interest in developing machine learning algorithms that leverage brain imaging data to provide estimates of "brain age" for an individual. Importantly, the discordance between brain age and chronological age (referred to as "brain age gap") can capture accelerated aging due to adverse health conditions and therefore, can reflect increased vulnerability towards neurological disease or cognitive impairments. However, widespread adoption of brain age for clinical decision support has been hindered due to lack of transparency and methodological justifications in most existing brain age prediction algorithms. In this paper, we leverage coVariance neural networks (VNN) to propose an explanation-driven and anatomically interpretable frame

SUBMITTER: Sihag S 

PROVIDER: S-EPMC10557794 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature