  The deviation between chronological age and biological age is a
well-recognized biomarker associated with cognitive decline and
neurodegeneration. Age-related and pathology-driven changes to brain structure
are captured by various neuroimaging modalities. These datasets are
characterized by high dimensionality as well as collinearity, hence
applications of graph neural networks in neuroimaging research routinely use
sample covariance matrices as graphs. We have recently studied covariance
neural networks (VNNs) that operate on sample covariance matrices using the
architecture derived from graph convolutional networks, and we showed VNNs
enjoy significant advantages over traditional data analysis approaches. In this
paper, we demonstrate the utility of VNNs in inferring brain age using cortical
thickness data. Furthermore, our results show that VNNs exhibit multi-scale and
multi-site transferability for inferring {brain age}. In the context of brain
age in Alzheimer's disease (AD), our experiments show that i) VNN outputs are
interpretable as brain age predicted using VNNs is significantly elevated for
AD with respect to healthy subjects for different datasets; and ii) VNNs can be
transferable, i.e., VNNs trained on one dataset can be transferred to another
dataset with different dimensions without retraining for brain age prediction.
