KOCOBrain: Kuramoto-Guided Graph Network for Uncovering Structure-Function Coupling in Adolescent Prenatal Drug Exposure
By: Badhan Mazumder , Lei Wu , Sir-Lord Wiafe and more
Potential Business Impact:
Finds brain changes from drug use in pregnancy.
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.
Similar Papers
NeuroKoop: Neural Koopman Fusion of Structural-Functional Connectomes for Identifying Prenatal Drug Exposure in Adolescents
Neurons and Cognition
Finds how drug use before birth affects teen brains.
ABFR-KAN: Kolmogorov-Arnold Networks for Functional Brain Analysis
CV and Pattern Recognition
Finds autism in brain scans better.
Multi-Sensory Cognitive Computing for Learning Population-level Brain Connectivity
Machine Learning (CS)
Maps how brains process information from senses.