发表机构
Georgia State University; Georgia Institute of Technology; Emory University（佐治亚州立大学; 佐治亚理工学院; 埃默里大学）
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sMRI与dFNC融合时IVA方法的缺陷，提出MSR-IVA框架，在ADNI队列实验中提升匹配源耦合、降低非匹配依赖，实现受控结构共享以适配状态差异。
AI 中文摘要
结构磁共振成像（sMRI）与动态功能连接（dFNC）的多模态融合可揭示脑结构如何与变化的功能状态相关联。当同一结构潜在表征与多个状态耦合时，对每个状态分别应用独立向量分析（IVA）会产生不相关的结构分解，而强制使用相同分解可能会抑制特定状态的关系。此外，并非每个受试者都表现出所有动态状态。我们提出 masked 结构残差IVA（MSR-IVA），这是一种状态感知框架，将共享结构表征与特定状态的残差适配、针对不完整状态表达的掩码相结合。在阿尔茨海默病神经影像倡议队列中，与独立成对IVA基线相比，MSR-IVA使匹配源耦合提高了6.5%，非匹配依赖性降低了15.7%。在同时表现出两种状态的受试者中，MSR-IVA的跨状态结构源平均绝对相关系数为0.9177，而无共享的情况为0.2978，这表明其实现了受控的结构共享，在保持源对应关系的同时允许特定状态适配。
英文摘要
Multimodal fusion of structural MRI (sMRI) and dynamic functional network connectivity (dFNC) can reveal how brain structure relates to changing functional states. When the same structural latent representation is coupled with multiple states, applying independent vector analysis (IVA) separately to each state can produce unrelated structural decompositions, while forcing identical decompositions may suppress state-specific relationships. In addition, not every subject expresses every dynamic state. We propose masked structural residual IVA (MSR-IVA), a state-aware framework that combines a shared structural representation with state-specific residual adaptations and masks for incomplete state expression. On an Alzheimer's Disease Neuroimaging Initiative cohort, MSR-IVA improved matched source coupling by 6.5% and reduced unmatched dependence by 15.7% relative to the independent pairwise IVA baseline. Among subjects expressing both states, mean absolute cross-state structural source correlation was 0.9177 for MSR-IVA versus 0.2978 for no sharing, demonstrating controlled structural sharing that preserves source correspondence while allowing state-specific adaptation.
CommentsAccepted at the 2026 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2026), Atlanta, GA, USA
