AI 中文总结
该研究提出 Group ICA 2.0 框架下的 CoLiG-ICA 算法，用于分解 fMRI 数据，相比传统方法可更好捕捉个体变异性，在精神分裂症分析中能识别更多额外脑网络。
AI 中文摘要
组独立成分分析（Group Independent Component Analysis, gICA）被广泛用于将高维功能性磁共振成像（fMRI）数据分解为可解释的脑网络。然而，传统 gICA 主要识别跨被试共享的成分，这种组水平假设会限制仅存在于个体或被式子集中的网络的恢复，降低临床神经影像数据集对被试间异质性的敏感性。我们引入 Copula 链接组 ICA（Copula-Linked Group ICA, CoLiG-ICA），它是 Group ICA 2.0 框架中的一种算法，可在统一模型中联合估计模板链接、队列专属和个体专属脑网络。CoLiG-ICA 结合了基于 ICA 的空间分解、基于 Copula 的依赖建模与深度学习优化，在保留模板约束 ICA 的一致性和可解释性的同时，支持参考网络之外的自由成分。通过将个体分解与共享模板链接并联合估计队列专属和个体专属源，CoLiG-ICA 表征了传统组先验未捕捉的个体变异性。我们使用 UCLA-CNP 数据集的静息态 fMRI 数据评估 CoLiG-ICA，并将其与传统约束 ICA 在估计模板链接成分、发现额外自由成分、提升成分独立性及捕捉共享组先验之外的个体水平变异性方面进行比较。与 MOO-ICAR 相比，CoLiG-ICA 表现出显著更低的成分间空间依赖性，表明个体水平成分独立性得到提升，且模板链接成分中与运动相关的方差显著降低。此外，仅针对精神分裂症组的分析中，CoLiG-ICA 在 53 个模板链接的 NeuroMark 成分之外，识别出 3 个额外的静息态网络：1 个感觉运动网络和 2 个视觉网络。
英文摘要
Group Independent Component Analysis (gICA) is widely used to decompose high-dimensional functional MRI data into interpretable brain networks. However, conventional gICA primarily identifies components shared across subjects. This group-level assumption can limit the recovery of networks present only in individuals or subject subsets, reducing sensitivity to intersubject heterogeneity in clinical neuroimaging datasets. We introduce Copula-Linked Group ICA (CoLiG-ICA), an algorithm in the Group ICA 2.0 framework that jointly estimates template-linked, cohort-only, and subject-only brain networks within a unified model. CoLiG-ICA combines ICA-based spatial decomposition, copula-based dependence modeling, and deep learning optimization to preserve the consistency and interpretability of template-constrained ICA while enabling free components beyond the reference networks. By linking subject decompositions to shared templates and jointly estimating cohort-only and subject-only sources, CoLiG-ICA represents individual variability not captured by conventional group priors. We evaluate CoLiG-ICA using resting-state fMRI data from the UCLA-CNP dataset and compare it with conventional constrained ICA in estimating template-linked components, discovering additional free components, improving component independence, and capturing subject-level variability beyond the shared group prior. Compared with MOO-ICAR, CoLiG-ICA showed significantly lower intercomponent spatial dependence, indicating improved subject-level component independence, and significantly reduced motion-related variance in the template-linked components. Additionally, in a schizophrenia-only group analysis, CoLiG-ICA identified three additional resting-state networks beyond the 53 template-linked NeuroMark components: one sensorimotor and two visual networks.
