Predicting Brain Age using Transferable coVariance Neural Networks

TL;DR

This paper introduces a novel application of covariance neural networks (VNNs) to predict brain age from neuroimaging data, demonstrating their transferability across datasets and interpretability in Alzheimer's disease research.

Contribution

The study presents the first use of VNNs for brain age prediction, showing their multi-scale, multi-site transferability and interpretability in neuroimaging analysis.

Findings

VNNs accurately predict brain age from cortical thickness data.

VNNs trained on one dataset transfer effectively to others without retraining.

VNN outputs are elevated in Alzheimer's disease, indicating potential clinical relevance.

Abstract

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.