A Concept-Driven Disentanglement Framework for Interpretable Graph Neural Networks in Structure-Function Coupling

Abstract

Graph Neural Networks (GNNs) achieve state-of-the-art performance in predicting brain functional connectivity (FC) from structural connectivity (SC), yet their "black-box" nature limits interpretability and scientific utility. We present a concept-driven disentanglement framework that builds inherently interpretable GNNs for quantitative hypothesis testing of structure-function relationships. The framework employs an ensemble of GNN branches, each architecturally biased to learn from a predefined structural concept (e.g., strong vs. weak connections) by processing a filtered version of the SC graph. This design enforces verifiably disentangled node embeddings, ensuring each branch captures a distinct structural feature. Using SHAP (Shapley additive explanations), we quantify the predictive contribution of each concept and assess its statistical significance against null distributions. Our framework demonstrates high predictive accuracy for FC, achieving a group-level correlation coefficient of 0.91 on a public human connectome dataset, while simultaneously yielding interpretable neuroscientific insights. This interpretable-by-design methodology bridges the gap between predictive power and scientific transparency, enabling deep learning models to provide mechanistic insights into the brains network organization.