用神经柯普曼融合脑结构功能网络，更准识别青少年产前药物暴露
NeuroKoop: Neural Koopman Fusion of Structural-Functional Connectomes for Identifying Prenatal Drug Exposure in Adolescents
- 通过神经柯普曼算子融合结构与功能脑网络嵌入
- 在ABCD数据集上分类准确率显著优于基线模型
- 可揭示药物暴露相关的关键脑连接，适合神经发育研究
理解产前接触大麻等精神活性物质如何影响青少年脑组织结构仍是重大挑战，受限于多模态神经影像数据的复杂性及传统分析方法的局限。现有方法难以充分捕捉结构与功能连接组间的互补特征，制约了生物学洞见和预测性能。为此，我们提出NeuroKoop——一种基于图神经网络的框架，利用神经柯普曼算子驱动的潜在空间融合，整合基于源基形态学（SBM）和功能网络连通性（FNC）的脑图谱节点嵌入，实现更优的表征学习与产前药物暴露（PDE）状态分类。在来自ABCD数据集的大规模青少年队列上，NeuroKoop超越多个基线方法，并揭示了显著的结构-功能连接模式，深化了对产前药物暴露神经发育影响的理解。
原文摘要 · Abstract (English)
Understanding how prenatal exposure to psychoactive substances such as cannabis shapes adolescent brain organization remains a critical challenge, complicated by the complexity of multimodal neuroimaging data and the limitations of conventional analytic methods. Existing approaches often fail to fully capture the complementary features embedded within structural and functional connectomes, constraining both biological insight and predictive performance. To address this, we introduced NeuroKoop, a novel graph neural network-based framework that integrates structural and functional brain networks utilizing neural Koopman operator-driven latent space fusion. By leveraging Koopman theory, NeuroKoop unifies node embeddings derived from source-based morphometry (SBM) and functional network connectivity (FNC) based brain graphs, resulting in enhanced representation learning and more robust classification of prenatal drug exposure (PDE) status. Applied to a large adolescent cohort from the ABCD dataset, NeuroKoop outperformed relevant baselines and revealed salient structural-functional connections, advancing our understanding of the neurodevelopmental impact of PDE.
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