Esposito Fabrizio, Scarabino Tommaso, Hyvarinen Aapo, Himberg Johan, Formisano Elia, Comani Silvia, Tedeschi Gioacchino, Goebel Rainer, Seifritz Erich, Di Salle Francesco
Second Division of Neurology, Second University of Naples, Italy.
Neuroimage. 2005 Mar;25(1):193-205. doi: 10.1016/j.neuroimage.2004.10.042. Epub 2005 Jan 8.
Independent component analysis (ICA) is a valuable technique for the multivariate data-driven analysis of functional magnetic resonance imaging (fMRI) data sets. Applications of ICA have been developed mainly for single subject studies, although different solutions for group studies have been proposed. These approaches combine data sets from multiple subjects into a single aggregate data set before ICA estimation and, thus, require some additional assumptions about the separability across subjects of group independent components. Here, we exploit the application of similarity measures and a related visual tool to study the natural self-organizing clustering of many independent components from multiple individual data sets in the subject space. Our proposed framework flexibly enables multiple criteria for the generation of group independent components and their random-effects evaluation. We present real visual activation fMRI data from two experiments, with different spatiotemporal structures, and demonstrate the validity of this framework for a blind extraction and selection of meaningful activity and functional connectivity group patterns. Our approach is either alternative or complementary to the group ICA of aggregated data sets in that it exploits commonalities across multiple subject-specific patterns, while addressing as much as possible of the intersubject variability of the measured responses. This property is particularly of interest for a blind group and subgroup pattern extraction and selection.
独立成分分析（ICA）是一种用于功能磁共振成像（fMRI）数据集多变量数据驱动分析的重要技术。ICA的应用主要是针对单受试者研究开发的，尽管也有人提出了用于组研究的不同解决方案。这些方法在进行ICA估计之前，将来自多个受试者的数据集合并为一个单一的聚合数据集，因此需要对组独立成分在受试者之间的可分离性做出一些额外假设。在这里，我们利用相似性度量和相关视觉工具的应用，来研究受试者空间中多个个体数据集的许多独立成分的自然自组织聚类。我们提出的框架灵活地支持用于生成组独立成分及其随机效应评估的多个标准。我们展示了来自两个具有不同时空结构的实验的真实视觉激活fMRI数据，并证明了该框架对于盲目提取和选择有意义的活动和功能连接组模式的有效性。我们的方法对于聚合数据集的组ICA来说，要么是替代方案，要么是补充方案，因为它利用了多个受试者特定模式之间的共性，同时尽可能地处理测量响应中的受试者间变异性。对于盲目组和亚组模式提取与选择而言，这一特性尤其重要。
