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.

翻译：孕期接触精神活性物质（如大麻）可能干扰神经发育并改变大规模脑网络，但其神经特征的识别仍具挑战性。我们提出了KOCOBrain：一种基于Kuramoto相位动力学的耦合脑图网络；这是一个统一的图神经网络框架，通过基于Kuramoto的相位动力学与认知感知注意力机制整合结构与功能连接组。Kuramoto层在解剖连接上模拟神经同步，生成捕获结构-功能耦合的相位感知嵌入；认知评分则以受试者特异性方式调节信息路由，随后通过联合目标函数增强类别不平衡场景下的鲁棒性。在ABCD队列中的应用表明，KOCOBrain在产前药物暴露预测上优于相关基线方法，并揭示了可解释的结构-功能模式，这些模式反映了与早期暴露相关的脑网络协调性破坏。