用脑网络同步机制识别孕早期药物暴露的脑结构功能关联
KOCOBrain: Kuramoto-Guided Graph Network for Uncovering Structure-Function Coupling in Adolescent Prenatal Drug Exposure
- 基于柯尔莫哥洛夫相位动力学建模神经同步，融合结构与功能连接
- 在ABCD队列上预测孕期用药暴露准确率优于基线模型
- 可解释性强，适合神经发育研究与临床早期筛查
孕期使用大麻等精神活性物质会干扰神经发育并改变大规模脑网络，但其神经表征难以识别。本文提出KOCOBrain：一种统一的图神经网络框架，通过柯尔莫哥洛夫相位动力学整合结构与功能连接组，并引入认知感知注意力机制。柯尔莫哥洛夫层基于解剖连接建模神经同步，生成反映结构-功能耦合的相位嵌入；认知评分调节个体化信息路由，联合目标提升类别不平衡下的鲁棒性。在ABCD队列上的应用显示，该模型在预测孕期药物暴露方面优于相关基线方法，并揭示了与早期暴露相关的脑网络协调性破坏的可解释模式。
原文摘要 · Abstract (English)
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.
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