Extraction and Automatic Grouping of Joint and Individual Sources in Multisubject fMRI Data Using Higher Order Cumulants

The joint analysis of multiple data sets to extract their interdependency information has wide applications in biomedical and health informatics. In this paper, we propose an algorithm to extract joint and individual sources of multisubject data sets by using a deflation-based procedure, which is referred to as joint/individual thin independent component analysis (JI-ThICA). The proposed algorithm is based on two cost functions utilizing higher order cumulants to extract joint and individual sources. Joint sources are discriminated by fusing signals of all subjects, whereas individual sources are extracted separately for each subject. Furthermore, JI-ThICA algorithm estimates the number of joint sources by applying a simple and efficient strategy to determine the type of sources (joint or individual). The algorithm also categorizes similar sources automatically across data sets through an optimization process. The proposed algorithm is evaluated by analyzing simulated functional magnetic resonance imaging (fMRI) multisubject data sets, and its performance is compared with existing alternatives. We investigate clean and noisy fMRI signals and consider two source models. Our results reveal that the proposed algorithm outperforms its alternatives in terms of the mean joint signal to interference ratio. We also apply the proposed algorithm on a public-available real fMRI multisubject data set, which was acquired during naturalistic auditory experience. The extracted results are in accordance with the previous studies on naturalistic audio listening and results of a recent study investigated this data set, which demonstrates that the JI-ThICA algorithm can be applied to extract reliable and meaningful information from multisubject fMRI data.