计算机应用研究2026，Vol.43Issue(6)：1810-1817,8.DOI:10.19734/j.issn.1001-3695.2025.10.0415
一种嵌入群体先验的功能磁共振时空双流脑网络分析方法
Functional magnetic resonance spatio-temporal dual-stream brain network analysis method incorporating group prior information
摘要
Abstract
Brain network analysis methods based on functional magnetic resonance imaging(fMRI)serve as a critical tool for diagnosing brain disorders.However,existing deep learning approaches predominantly focus on spatial structures while neglec-ting temporal dynamics and group-level prior information among subjects,resulting in insufficient exploration of temporal fea-tures and utilization of inter-subject similarity relationships.To address these limitations,this paper proposed an STDSM incor-porating group-level prior information.Firstly,it established a parallel spatio-temporal feature learning framework based on Graph Isomorphism Networks(GIN)and Mamba,which jointly captured the spatial and temporal characteristics of brain activity.Simultaneously,it incorporated group graph structures to embed prior similarity relationships among subjects,achie-ving representation optimization at the group level through neighborhood feature aggregation.Experiments on the public autism(ABIDE)and depression(REST-Meta-MDD)datasets demonstrate that STDSM exhibits outstanding disease prediction capa-bilities.By synergistically modeling spatio-temporal information and explicitly leveraging group priors,it effectively enhances the discriminative and generalization performance of brain disease prediction.关键词
脑功能网络/静息态功能磁共振成像/图同构网络/Mamba/群体图/脑疾病识别/诊断Key words
brain functional network/resting-state functional magnetic resonance imaging/graph isomorphism network/Mamba/population graph/brain disease identification/diagnosis分类
信息技术与安全科学引用本文复制引用
吴燕棣,王晨,王俊泽,刘雷,张丽梅..一种嵌入群体先验的功能磁共振时空双流脑网络分析方法[J].计算机应用研究,2026,43(6):1810-1817,8.基金项目
国家自然科学基金面上项目(62176112,62476155) （62176112,62476155）
山东省自然科学基金面上项目(ZR2024MF063) （ZR2024MF063）
