KOCOBrain: Kuramoto-Guided Graph Network for Uncovering Structure-Function Coupling in Adolescent Prenatal Drug Exposure

TL;DR

KOCOBrain is a novel graph neural network framework that models neural synchronization and integrates structural and functional brain connectomes to identify neural signatures of prenatal drug exposure.

Contribution

The paper introduces KOCOBrain, a unified graph neural network leveraging Kuramoto dynamics and cognition-aware attention to uncover structure-function coupling in the brain.

Findings

Improved prediction of prenatal drug exposure using KOCOBrain.

Revealed disrupted brain network patterns associated with early exposure.

Enhanced robustness in class-imbalanced scenarios.

Abstract

Exposure to psychoactive substances during pregnancy, such as cannabis, can disrupt neurodevelopment and alter large-scale brain networks, yet identifying their neural signatures remains challenging. We introduced KOCOBrain: KuramotO COupled Brain Graph Network; a unified graph neural network framework that integrates structural and functional connectomes via Kuramoto-based phase dynamics and cognition-aware attention. The Kuramoto layer models neural synchronization over anatomical connections, generating phase-informed embeddings that capture structure-function coupling, while cognitive scores modulate information routing in a subject-specific manner followed by a joint objective enhancing robustness under class imbalance scenario. Applied to the ABCD cohort, KOCOBrain improved prenatal drug exposure prediction over relevant baselines and revealed interpretable structure-function patterns that reflect disrupted brain network coordination associated with early exposure.