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

2026-01-16

AI Summary

This study investigates the disruptive effects of prenatal cannabis exposure on structure–function coupling in the adolescent brain and identifies associated neural biomarkers. To this end, we propose a unified graph neural network framework that, for the first time, integrates Kuramoto phase dynamics with a cognition-guided attention mechanism to enable interpretable modeling of multimodal brain connectomes and individualized information routing. Evaluated on the ABCD cohort, our approach significantly outperforms existing baselines, demonstrating enhanced predictive robustness under class imbalance and uncovering aberrant patterns of brain network synchronization linked to early drug exposure. These findings offer a novel perspective on the neurodevelopmental consequences of prenatal cannabis exposure.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• prenatal drug exposure
• structure-function coupling
• neurodevelopment
• brain networks
• neural signatures

Innovation

• Kuramoto dynamics
• graph neural network
• structure-function coupling
• cognition-aware attention
• prenatal drug exposure