Revisiting Graph Neural Networks for Brain Disorders: A Neuroscience-Informed Perspective

Brain disorders exhibit abnormal neural activity patterns and present significant challenges for accurate and objective diagnosis. Boasting a strong capacity for learning relationships, Graph Neural Networks (GNNs) are increasingly adopted to model multi-channel neural signals such as Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) for the diagnosis of brain disorders. However, existing methods suffer from two fundamental limitations from a neuroscientific perspective on brain activity analysis. First, existing methods are path-agnostic and fail to capture the directed propagation flow of neural signals and overlook essential causal mechanisms of brain activity. Second, multi-scale modeling introduces message-passing conflict that prevents effective semantic unification across hierarchical representations of brain regions and the entire brain network, and leads to unstable feature fusion. To address these challenges, we propose **CPHU**, a neuroscience-informed and path-sensitive framework for recognizing brain disorders. CPHU introduces **C**ausal **P**ath Signal Encoding (CPSE) to extract path-aware node representations via predefined and learnable propagation operators, and **H**ierarchical Brain Semantic **U**nification (HBSU) to resolve message-passing conflicts by aligning semantic features across intra-regional, global, and fused graphs. Extensive experiments demonstrate the effectiveness and superiority of CPHU in brain disorder diagnosis. The code is available at <https://anonymous.4open.science/r/CPHU-3686/>.