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.

翻译：理解产前暴露于精神活性物质（如大麻）如何塑造青少年大脑组织结构仍是一个关键挑战，这一挑战因多模态神经影像数据的复杂性及传统分析方法的局限性而更加棘手。现有方法往往未能充分捕捉结构连接组与功能连接组中嵌入的互补特征，从而限制了生物学洞见和预测性能。为解决此问题，我们提出了NeuroKoop，一种新颖的基于图神经网络的框架，该框架利用神经库普曼算子驱动的潜在空间融合来整合大脑结构网络与功能网络。通过运用库普曼理论，NeuroKoop统一了源自基于源形态测量（SBM）和基于功能网络连接（FNC）的大脑图谱的节点嵌入，从而实现了增强的表征学习以及对产前药物暴露（PDE）状态更鲁棒的分类。在应用于来自ABCD数据集的大型青少年队列时，NeuroKoop的表现优于相关基线方法，并揭示了显著的结构-功能连接，推进了我们对PDE神经发育影响的理解。