Latent Koopman Dynamics of Brain Structure-Function Coupling for Identifying Adolescent Prenatal Drug Exposure.

OBJECTIVE: Prenatal drug exposure (PDE) has been linked to persistent alterations in adolescent brain development, yet the mechanisms by which structural and functional networks jointly contribute to later neurodevelopmental vulnerability remain unclear. Existing approaches typically analyze brain connectivity as static or treat modalities independently, limiting insight into how structure-function interactions are jointly characterized in relation to individual cognitive profiles. METHODS: We present NeuroKoop++, a graph neural network-based multimodal framework that characterizes brain structure-function coupling through subject-level latent dynamical modeling. Modality-specific graph neural network (GNN) encoders extract structural connectivity (SC) and functional network connectivity (FNC) representations, which are integrated via bidirectional cross-attention and subsequently advanced through a spectrally constrained Koopman operator conditioned on subject-specific cognitive scores. RESULTS: Applied to the ABCD cohort of 10,199 adolescents, NeuroKoop++ outperformed leading state-of-the-art multimodal approaches with statistically significant improvements and revealed interpretable signatures of altered large-scale brain network organization associated with PDE history. CONCLUSION: Modeling structure-function coupling as a cognition-modulated latent dynamical process yields robust and interpretable gains for PDE classification over existing fusion approaches. SIGNIFICANCE: This work offers a principled computational pathway toward identifying neurodevelopmental biomarkers of PDE, with broader applicability to multimodal neuroimaging in pediatric and adolescent brain health research.