NeuroBRIDGE: Behavior-Conditioned Koopman Dynamics with Riemannian Alignment for Early Substance Use Initiation Prediction from Longitudinal Functional Connectome

TL;DR

NeuroBRIDGE is a novel graph neural network framework that models longitudinal brain connectomes to predict adolescent substance use initiation, capturing temporal dynamics and behavioral influences for improved accuracy and interpretability.

Contribution

It introduces a Riemannian alignment and Koopman dynamics approach to better model brain network changes over time for early SUI prediction.

Findings

NeuroBRIDGE outperforms baseline models in predicting SUI.

Provides interpretable neural pathway insights.

Enhances understanding of neurodevelopmental risk factors.

Abstract

Early identification of adolescents at risk for substance use initiation (SUI) is vital yet difficult, as most predictors treat connectivity as static or cross-sectional and miss how brain networks change over time and with behavior. We proposed NeuroBRIDGE (Behavior conditioned RIemannian Koopman Dynamics on lonGitudinal connEctomes), a novel graph neural network-based framework that aligns longitudinal functional connectome in a Riemannian tangent space and couples dual-time attention with behavioral-conditioned Koopman dynamics to capture temporal change. Evaluated on ABCD, NeuroBRIDGE improved future SUI prediction over relevant baselines while offering interpretable insights into neural pathways, refining our understanding of neurodevelopmental risk and informing targeted prevention.