Clusterwise Independent Component Analysis (C-ICA): Using fMRI resting state networks to cluster subjects and find neurofunctional subtypes

• Published in: Journal of neuroscience methods 2022-12, Vol.382, p.109718-109718, Article 109718
• Main Authors: Durieux, Jeffrey, Rombouts, Serge A.R.B., de Vos, Frank, Koini, Marisa, Wilderjans, Tom F.
• Format: Article
• Language: English
• Subjects: Aged; Alzheimer Disease - diagnostic imaging; Brain - diagnostic imaging; Brain Mapping - methods; Cluster Analysis; Clustering; Computer Simulation; Heterogeneity; Humans; ICA; Individual differences; Magnetic Resonance Imaging - methods; Neurofunctional subtypes; Patient clusters; Resting-state fMRI
• ISSN: 0165-0270; 1872-678X
• DOI: 10.1016/j.jneumeth.2022.109718

FMRI resting state networks (RSNs) are used to characterize brain disorders. They also show extensive heterogeneity across patients. Identifying systematic differences between RSNs in patients, i.e. discovering neurofunctional subtypes, may further increase our understanding of disease heterogeneity. Currently, no methodology is available to estimate neurofunctional subtypes and their associated RSNs simultaneously. We present an unsupervised learning method for fMRI data, called Clusterwise Independent Component Analysis (C-ICA). This enables the clustering of patients into neurofunctional subtypes based on differences in shared ICA-derived RSNs. The parameters are estimated simultaneously, which leads to an improved estimation of subtypes and their associated RSNs. In five simulation studies, the C-ICA model is successfully validated using both artificially and realistically simulated data (N = 30–40). The successful performance of the C-ICA model is also illustrated on an empirical data set consisting of Alzheimer’s disease patients and elderly control subjects (N = 250). C-ICA is able to uncover a meaningful clustering that partially matches (balanced accuracy = .72) the diagnostic labels and identifies differences in RSNs between the Alzheimer and control cluster. Both in the simulation study and the empirical application, C-ICA yields better results compared to competing clustering methods (i.e., a two step clustering procedure based on single subject ICA’s and a Group ICA plus dual regression variant thereof) that do not simultaneously estimate a clustering and associated RSNs. Indeed, the overall mean adjusted Rand Index, a measure for cluster recovery, equals 0.65 for C-ICA and ranges from 0.27 to 0.46 for competing methods. The successful performance of C-ICA indicates that it is a promising method to extract neurofunctional subtypes from multi-subject resting state-fMRI data. This method can be applied on fMRI scans of patient groups to study (neurofunctional) subtypes, which may eventually further increase understanding of disease heterogeneity. •C-ICA is a new method for discovering neurofunctional subtypes from rs-fMRI data.•Patients are clustered based on differences in ICA-derived resting state networks.•Neurofunctional subtypes may increase the understanding of disease heterogeneity.•We successfully validated C-ICA in simulation studies and an empirical study.•C-ICA shows a better clustering performance compared to competing clustering met