用动态脑网络变化预测青少年吸毒风险，更准且可解释。
NeuroBRIDGE: Behavior-Conditioned Koopman Dynamics with Riemannian Alignment for Early Substance Use Initiation Prediction from Longitudinal Functional Connectome
- 在黎曼切空间对纵向脑连接图对齐，捕捉动态变化
- 结合行为条件的科普曼动力学，提升吸毒早期预测准确率
- 适合神经发育研究与青少年预防干预团队使用
早期识别青少年物质使用初发（SUI）风险至关重要，但现有方法多将脑连接视为静态或横断面数据，忽略了随时间演变的脑网络动态及与行为的关联。本文提出NeuroBRIDGE（基于黎曼切空间的纵向功能连接图行为条件科普曼动力学），一种基于图神经网络的新框架：在黎曼切空间对齐纵向功能连接组，并融合双时间注意力机制与行为条件的科普曼动力学，以捕捉时序演化。在ABCD数据集上评估表明，NeuroBRIDGE在预测未来SUI方面优于多个基线模型，同时提供可解释的神经通路洞察，深化了对神经发育风险的理解，为精准预防提供了依据。
原文摘要 · Abstract (English)
Early identification of adolescents at risk for substance use initiation (SUI) is vital yet difficult, as most predictors treat connectivity as static or cross-sectional and miss how brain networks change over time and with behavior. We proposed NeuroBRIDGE (Behavior conditioned RIemannian Koopman Dynamics on lonGitudinal connEctomes), a novel graph neural network-based framework that aligns longitudinal functional connectome in a Riemannian tangent space and couples dual-time attention with behavioral-conditioned Koopman dynamics to capture temporal change. Evaluated on ABCD, NeuroBRIDGE improved future SUI prediction over relevant baselines while offering interpretable insights into neural pathways, refining our understanding of neurodevelopmental risk and informing targeted prevention.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。