Extending Clusterwise ICA (C-ICA to distinguish between common and distinctive components

A challenge in clinical neuroscience is to identify systematic differences between patients’ resting state networks (RSNs) that may relate to phenotypes and/or disease subtypes. Recently, Durieux et al. (2022) proposed a novel unsupervised learning method for multi-subject rs-fMRI data, called Clusterwise Independent Component Analysis (C-ICA), which enables automatic clustering of subjects based on differences and similarities in subjects’ RSNs. A drawback of C-ICA, however, is that the method assumes that subject clusters show large difference in the RSNs that characterizes them. This, however, is a quite restrictive assumption as it can be expected that there exists important RSNs that are common to all subjects (across clusters). Not accounting for these common RSNs may negatively affect C-ICA’s ability to identify the subject clustering and the RSNs for each cluster. Therefore, in order to tackle this problem, two extensions of C-ICA (CCD1 and CCD2) are presented and compared...Show moreA challenge in clinical neuroscience is to identify systematic differences between patients’ resting state networks (RSNs) that may relate to phenotypes and/or disease subtypes. Recently, Durieux et al. (2022) proposed a novel unsupervised learning method for multi-subject rs-fMRI data, called Clusterwise Independent Component Analysis (C-ICA), which enables automatic clustering of subjects based on differences and similarities in subjects’ RSNs. A drawback of C-ICA, however, is that the method assumes that subject clusters show large difference in the RSNs that characterizes them. This, however, is a quite restrictive assumption as it can be expected that there exists important RSNs that are common to all subjects (across clusters). Not accounting for these common RSNs may negatively affect C-ICA’s ability to identify the subject clustering and the RSNs for each cluster. Therefore, in order to tackle this problem, two extensions of C-ICA (CCD1 and CCD2) are presented and compared to each other that allow to distinguish between (and extract from the data) both common and distinctive components. Both extensions differ in the procedure to identify the common components, with CCD2 selecting the common components out of a wider pool of possible components than CCD1. In a simulation study, the two C-ICA extensions are compared to C-ICA while manipulating the following factors: number of clusters, number of common and distinctive components, degree of overlap among clusters and amount of noise. Performance was determined by computing cluster recovery and component recovery, where component recovery was studied separately for the common components, the distinctive components and all components. Both extensions clearly outperformed C-ICA, with CCD2 performing the best. A mixed analysis of variance (ANOVA) showed that, the degree of overlap and the amount of noise in the data were the most influential factors, with large amounts of overlap and noise decreasing cluster and component recovery. Moreover, both extensions showed a slower deterioration of cluster and component recovery -with increasing overlap and noise- than C-ICA. Specifically for intermediate amounts of noise and overlap the differences between both extensions and C-ICA were the largest.Show less

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Extending Clusterwise ICA (C-ICA to distinguish between common and distinctive components