  Brain age is the estimate of biological age derived from neuroimaging
datasets using machine learning algorithms. Increasing brain age with respect
to chronological age can reflect increased vulnerability to neurodegeneration
and cognitive decline. In this paper, we study NeuroVNN, based on coVariance
neural networks, as a paradigm for foundation model for the brain age
prediction application. NeuroVNN is pre-trained as a regression model on
healthy population to predict chronological age using cortical thickness
features and fine-tuned to estimate brain age in different neurological
contexts. Importantly, NeuroVNN adds anatomical interpretability to brain age
and has a `scale-free' characteristic that allows its transference to datasets
curated according to any arbitrary brain atlas. Our results demonstrate that
NeuroVNN can extract biologically plausible brain age estimates in different
populations, as well as transfer successfully to datasets of dimensionalities
distinct from that for the dataset used to train NeuroVNN.
