Predicting Brain Age using Transferable coVariance Neural Networks
This addresses brain age prediction for neuroimaging research, offering a transferable method that could aid in studying cognitive decline and Alzheimer's disease, though it appears incremental as an application of an existing VNN framework to a new domain.
The paper tackled predicting brain age from cortical thickness data using covariance neural networks (VNNs), showing that VNNs achieve interpretable results with brain age significantly elevated for Alzheimer's disease patients and exhibit multi-site transferability without retraining.
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.