Information Bottleneck-Guided Heterogeneous Graph Learning for Interpretable Neurodevelopmental Disorder Diagnosis
Abstract：Developing interpretable models for neurodevelopmental disorder (NDD) diagnosis presents significant challenges in effectively encoding, decoding, and integrating multimodal neuroimaging data. While existing machine learning approaches have shown promise in brain network analysis, they typically suffer from limited interpretability, particularly in extracting clinically meaningful biomarkers from functional magnetic resonance imaging (fMRI) and establishing clear relationships between imaging features and demographic characteristics. Current graph neural network methodologies face fundamental limitations in capturing both local and global functional connectivity patterns while simultaneously achieving theoretically principled multimodal data fusion. To address these challenges, we propose the Interpretable Information Bottleneck Heterogeneous Graph Neural Network (I²B-HGNN), a unified framework that applies information bottleneck principles to guide both brain connectivity modeling and cross-modal feature integration. This framework comprises two complementary components. The first is the Information Bottleneck Graph Transformer (IBGraphFormer), which combines transformer-based global attention mechanisms with graph neural networks through information bottleneck-guided pooling to identify minimal sufficient biomarkers. The second is the Information Bottleneck Heterogeneous Graph Attention Network (IB-HGAN), which employs meta-path-based heterogeneous graph learning with structural consistency constraints to achieve interpretable fusion of neuroimaging and demographic data. The results of the experiments demonstrate that I²B-HGNN excels in diagnosing NDDs with high accuracy, providing interpretable biomarker identification and effective analysis of non-imaging data.