  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 framework for brain age
prediction using cortical thickness features. Specifically, our brain age
prediction framework extends beyond the coarse metric of brain age gap in
Alzheimer's disease (AD) and we make two important observations: (i) VNNs can
assign anatomical interpretability to elevated brain age gap in AD by
identifying contributing brain regions, (ii) the interpretability offered by
VNNs is contingent on their ability to exploit specific eigenvectors of the
anatomical covariance matrix. Together, these observations facilitate an
explainable and anatomically interpretable perspective to the task of brain age
prediction.
