There is a recent trend to leverage the power of graph neural networks (GNNs) for brain-
network based psychiatric diagnosis, which, in turn, also motivates an urgent need for …

Recent studies proposed the use of Total Correlation to describe functional connectivity
among brain regions as a multivariate alternative to conventional pairwise measures such …

Graph neural networks (GNN) have emerged as a popular tool for modelling functional
magnetic resonance imaging (fMRI) datasets. Many recent studies have reported significant …

Deep neural networks suffer from poor generalization to unseen environments when the
underlying data distribution is different from that in the training set. By learning minimum …

Converging evidence increasingly suggests that psychiatric disorders, such as major
depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases …

Graph Neural Networks (GNNs) have been widely applied to graph-structured data.
However, the lack of interpretability impedes its practical deployment especially in high-risk …

The recently developed matrix-based Rényi's-order entropy enables measurement of
information in data simply using the eigenspectrum of symmetric positive semi-definite …

The wide variation in symptoms of neurological disorders among patients necessitates
uncovering individual pathologies for accurate clinical diagnosis and treatment. Current …

Graph neural networks (GNNs) have recently been applied to develop useful diagnostic
tools for psychiatric disorders. However, due to the lack of interpretability, clinicians are hard …

The biotemporal signal dataset is small, and certain signals (electroencephalogram (EEG),
electrocardiogram (ECG), and other biological data) used for emotion and disease …