Fully exploratory network ICA (FENICA) on resting-state fMRI data

Schöpf, Veronika; Kasess, Christian H.; Lanzenberger, Rupert; Fischmeister, Florian Ph. S.; Windischberger, Christian; Moser, Ewald

doi:10.1016/j.jneumeth.2010.07.028

Cited by 57 publications

(56 citation statements)

References 40 publications

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“…Classical approaches are based on the recognition of RSNs from different ICA processing results, such as multiple template‐matching (Demertzi et al., 2014), high dimensional ICA (Dipasquale et al., 2015) and fully exploratory network ICA (Schöpf et al., 2010); once each RSN is estimated, their spatial distributions are usually compared voxel‐wise between subjects or groups for the assessment of within‐network differences. On the other hand, at the best of our knowledge, there is not yet an effective procedure for the analysis and comparison of graph properties between network components derived from spatial ICA procedures.…”

Section: Discussionmentioning

confidence: 99%

A method for independent component graph analysis of resting‐state fMRI

et al. 2017

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IntroductionIndependent 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.ObjectiveHere, we detail a graph building technique that allows these ICNs to be analyzed with graph theory.MethodsFirst, 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.ResultsNetwork 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.ConclusionsThis 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.

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Section: Discussionmentioning

confidence: 99%

A method for independent component graph analysis of resting‐state fMRI

et al. 2017

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“…Forty independent components were suggested as of interest by the GIFT software, each of which was assessed under a z > 3.33 threshold (p < 0.001). The selection of the number of components is always arbitrary, but similar to those of previously published papers (e.g., grouped ICA of resting-state data) [Schöpf et al, 2010]. Noisy and artefactual components were eliminated, based on a visual comparison with human resting-state data obtained in healthy subjects (30 in total) [Biswal et al, 1995;Damoiseaux et al, 2006].…”

Section: Animal Studymentioning

confidence: 99%

Brain Connectivity in Ateles geoffroyi: Resting-State Functional Magnetic Resonance Imaging of Working Memory and Executive Control

et al. 2019

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The objective of this research was to describe the organization and connectivity of the working memory (WM) and executive control (EC) networks in Ateles geoffroyi in resting-state conditions. Recent studies have shown that resting-state activity may underlie rudimentary brain functioning, showing that several brain regions can be tonically active at rest, maximizing the efficiency of information transfer while preserving a low physical connection cost. Whole-brain resting-state images were acquired from three healthy adult Ateles monkeys (2 females, 1 male; mean age 10.5 ± SD 2.5 years). Data were analyzed with independent component analysis, and results were grouped together using the GIFT software. The present study compared the EC and WM networks obtained with human data and with results found in the literature in other primate species. Nine resting-state networks were found, which were similar to resting networks found in healthy human adults in the prefrontal basal portion and frontopolar area. Additionally, components of the WM network were found to be extending into the hypothalamus and the olfactory areas. A key finding was the discovery of connections in the WM and EC networks to the hypothalamus, the motor cortex, and the entorhinal cortex, suggesting that information is integrated from larger brain areas. The correlated areas suggest that many elements of WM and EC may be conserved across primate species. Characterization of these networks in resting-state conditions in nonhuman primate brains is a fundamental prerequisite for understanding of the neural bases underlying the evolution and function of this cognitive system.

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“…The QC of pre-and post-processing software should be performed by using mathematical phantoms (with varying noise levels) and in vivo data taken from international databases [23][24][25][26]. The latter typically also provides results from established data analyses performed by experienced scientists using established software packages [27][28][29].…”

Section: Some Notes Concerning Quality Control In (F)mri Mrs and Nmmentioning

confidence: 99%