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。本文提出KOCOBrain（KuramotO COupled Brain Graph Network）——一种通过基于Kuramoto模型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的统一图神经网络框架。Kuramoto层在解剖连接上模拟神经同步过程，生成能捕捉结构-功能耦合的相位感知嵌入；认知评分则以被试特异性方式调节信息传递路径，后续通过联合优化目标增强类别不平衡场景下的模型鲁棒性。在ABCD队列数据上的应用表明，KOCOBrain在产前药物暴露预测任务上优于相关基线方法，并揭示了可解释的结构-功能耦合模式，这些模式反映了早期暴露所导致的脑网络协调性紊乱。