提出新方法融合脑结构与功能动态变化，更好捕捉状态相关性。
MSR-IVA: Masked Structural Residual Independent Vector Analysis for State-Aware Fusion of Structural MRI and Dynamic Functional Network Connectivity
- 用共享结构+状态特异残差+掩码，实现状态感知的多模态融合
- 在阿尔茨海默症数据集上提升耦合匹配率6.5%，降低非匹配依赖15.7%
- 适合研究脑疾病中结构-功能关系的学者，尤其关注动态状态变化
结构磁共振成像（sMRI）与动态功能网络连通性（dFNC）的多模态融合可揭示脑结构如何关联功能状态变化。当同一结构潜在表示与多个状态耦合时，对各状态独立应用独立向量分析（IVA）会导致结构分解不一致；强制相同分解又会抑制状态特异性关系。此外，并非所有受试者都表达所有动态状态。本文提出掩码结构残差独立向量分析（MSR-IVA），一种状态感知框架，结合共享结构表示与状态特异残差适应及掩码机制以应对状态表达不全问题。在阿尔茨海默病神经影像倡议（ADNI）队列上，相较于独立配对IVA基线，MSR-IVA提升了6.5%的匹配源耦合率，降低了15.7%的非匹配依赖。在表达两种状态的受试者中，平均绝对跨状态结构源相关性为0.9177（MSR-IVA） vs 0.2978（无共享），证明了可控的结构共享，在保留源对应性的同时支持状态特异性适应。
原文摘要 · Abstract (English)
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.
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