Refined Causal Graph Structure Learning via Curvature for Brain Disease Classification

The field of neuroscience has been revolutionized by the advent of brain imaging technologies, particularly functional magnetic resonance imaging in the resting state (rest fMRI) (Khalilullah et al, 2023; Vasilkovska et al, 2023; Liu et al, 2024). This powerful tool allows the measurement of blood-oxygen-level-dependent (BOLD) signals in predefined Regions of Interest (ROIs) within the brain, offering an unprecedented avenue for revealing information about potential diseases such as autism spectrum disorder (ASD) and schizophrenia (Philiastides et al, 2021; Kocak, 2021). Various brain atlases, including Harvard-Oxford (Makris et al, 2006) and Craddock 200 (Craddock et al, 2012) parcellations, have been used to define these ROIs. Furthermore, ROIs can be interestingly modelled as graph data, where the ROIs themselves represent nodes, and the connections between ROIs represent edges of graphs (Cui et al, 2022b). This graph-based data structure, inheriting the graph theory technique, has been instrumental in revealing meaningful relationships between ROIs in brain networks to diagnose brain diseases more effectively (Alsubaie et al, 2024; Ren and Xia, 2024). With the current popularity of deep learning, recent frameworks have developed graph neural networks (GNNs) (Xia et al, 2021; Febrinanto et al, 2023c) to extend the merits of modelling graph-structured data for detecting brain diseases with brain networks based on fMRI signals as input (Kan et al, 2022b; Li et al, 2021; Kan et al, 2022a; Cui et al, 2022a; ElGazzar et al, 2022; Febrinanto et al, 2023a). These techniques perform more accurately than typical machine learning or deep learning techniques. However, there is still a high consensus on how to construct or define an appropriate graph structure in brain networks in terms of two processes: 1) how do we generate the graphs?