发表机构
Yale University; Robert Wood Johnson Medical School; Rutgers University; Pratt Institute（耶鲁大学; 罗伯特·伍德·约翰逊医学院; 罗格斯大学; 普拉特学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对fMRI脑疾病分类中主体间变异性被忽略的问题，提出MPP-GNN模型，通过自适应图划分与双层优化实现主体特定社区检测，在AD分类公开数据集上取得最优AUC，且与脑图谱组织一致并揭示AD网络模式。
AI 中文摘要
功能磁共振成像（fMRI）是研究大脑的广泛应用技术。近期利用图神经网络（GNN）分析大脑功能连接的方法在脑疾病（如阿尔茨海默病，AD）分类中展现出巨大潜力，但这些方法通常假设所有主体的功能模块数量预设，忽略了主体间变异性，且发现的模块很少被用于直接指导学习到的连接模式。为解决这些问题，本文提出元概率池化图神经网络（MPP-GNN），将模型任务构建为耦合的双层优化，分层执行自适应图划分以发现主体特定模块，再将发现的大脑模块作为显式先验指导边优化与表征学习。在两个用于AD分类的公开数据集上验证MPP-GNN，与既定基线相比，在两个数据集均取得最高AUC；进一步分析显示，MPP-GNN与Yeo脑图谱定义的经典功能网络组织具有显著一致性，并揭示了AD的网络水平去分化模式。
英文摘要
Functional magnetic resonance imaging (fMRI) is a widely used technique for studying the brain. Recent methods that utilize graph neural networks (GNNs) for analysis of brain functional connectivity have shown great potential for the classification of brain disorders, such as Alzheimer's disease (AD). However, these methods often assume a preset number of functional modules across all subjects, which overlooks inter-subject variability. In addition, the discovered modules are rarely used to directly guide the learned connectivity patterns. Here, to address these issues, we propose a Meta Probabilistic Pooling GNN (MPP-GNN). We frame the model's task as a coupled, bilevel optimization that performs adaptive graph partitioning hierarchically to discover subject-specific modules and then uses the discovered brain modules as an explicit prior to guide edge refinement and representation learning. We validate MPP-GNN on two public datasets for AD classification, achieving the highest AUC in comparison to established baselines for both datasets. Furthermore, our analysis demonstrates that MPP-GNN shows significant alignment with the canonical functional-network organization defined by the Yeo brain atlas and reveals a network-level dedifferentiation pattern for AD.
CommentsSubmitted to IEEE Transactions on Medical Imaging
