A method for independent component graph analysis of resting‐state fMRI. Issue 3 (16th February 2017)

• Record Type: Journal Article
• Authors: Ribeiro de Paula, Demetrius; Ziegler, Erik; Abeyasinghe, Pubuditha M.; Das, Tushar K.; Cavaliere, Carlo; Aiello, Marco; Heine, Lizette; di Perri, Carol; Demertzi, Athena; Noirhomme, Quentin; Charland‐Verville, Vanessa; Vanhaudenhuyse, Audrey; Stender, Johan; Gomez, Francisco; Tshibanda, Jean‐Flory L.; Laureys, Steven; Owen, Adrian M.; Soddu, Andrea
• Is Part Of: Brain and behavior. Volume 7:Issue 3(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-02-16
• Subjects: BOLD fMRI -- graph theory -- independent component analysis -- resting state; Neurology -- Periodicals; Neurosciences -- Periodicals; Psychology -- Periodicals; Psychiatry -- Periodicals; 616.8005
• DOI: 10.1002/brb3.626
• Languages: English
• ISSNs: 2162-3279

Abstract

Abstract: Introduction: Independent component analysis (ICA) has been extensively used for reducing task‐free BOLD fMRI recordings into spatial maps and their associated time‐courses. The spatially identified independent components can be considered as intrinsic connectivity networks (ICNs) of non‐contiguous regions. To date, the spatial patterns of the networks have been analyzed with techniques developed for volumetric data. Objective: Here, we detail a graph building technique that allows these ICNs to be analyzed with graph theory. Methods: First, ICA was performed at the single‐subject level in 15 healthy volunteers using a 3T MRI scanner. The identification of nine networks was performed by a multiple‐template matching procedure and a subsequent component classification based on the network "neuronal" properties. Second, for each of the identified networks, the nodes were defined as 1, 015 anatomically parcellated regions. Third, between‐node functional connectivity was established by building edge weights for each networks. Group‐level graph analysis was finally performed for each network and compared to the classical network. Results: Network graph comparison between the classically constructed network and the nine networks showed significant differences in the auditory and visual medial networks with regard to the average degree and the number of edges, while the visual lateral network showed a significant difference in the small‐worldness. Conclusions: This novel approach permits us to take advantage of the well‐recognized power of ICA in BOLD signal decomposition and, at the same time, to make use of well‐established graph measures to evaluate connectivity differences. Moreover, by providing a graph for each separate network, it can offer the possibility to extract graph measures in a specific way for each network. This increased specificity could be relevant for studying pathological brain activity or altered states of consciousness as induced by anesthesia or sleep, where specific networks are known to be altered in different strength. Abstract : We presented an approach for the analysis of resting state networks carried out by dissecting the connectivity patterns of task‐free fMRI data by using independent component analysis (ICA). We detail a graph building technique that allows these intrinsic connectivity networks to be analyzed with graph theory. This approach permits us to take full advantage of the well‐recognized power of ICA in BOLD signal decomposition and, at the same time, to make use of well‐established graph measures to evaluate connectivity differences.