Multiple fMRI system-level baseline connectivity is disrupted in patients with consciousness alterations

Demertzi, Athena; Gómez, Francisco Gómez; Crone, Julia Sophia; Vanhaudenhuyse, Audrey; Tshibanda, Luaba; Noirhomme, Quentin; Thonnard, Marie; Charland‐Verville, Vanessa; Kirsch, Muriëlle; Laureys, Steven; Soddu, Andrea

doi:10.1016/j.cortex.2013.11.005

Cited by 178 publications

(215 citation statements)

References 63 publications

(83 reference statements)

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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%

“…In short, we used ICA and machine learning classification to isolate a set of neuronal components (Demertzi et al., 2014), and then construct weighted graphs for each of these components. As in other functional and structural connectivity mapping methods, we defined our regions based on structural parcellation (Cammoun et al., 2012; Gerhard et al., 2011).…”

Section: Methodsmentioning

confidence: 99%

“…This method extends the single‐template goodness‐of‐fit approach by assigning the independent component that maximizes the goodness‐of‐fit to a binary predefined template while considering all of the RSNs simultaneously. This procedure has been described previously (Demertzi et al., 2014). …”

Section: Methodsmentioning

confidence: 99%

“…The training of the SVM classifier was performed on the fingerprints of the independent components obtained from ICA decomposition with 30 components in 19 independently studied healthy subjects (Demertzi et al., 2014). The classifier with highest overall classification rate was selected and subsequently used to label neuronal independent components.…”

Section: Methodsmentioning

confidence: 99%

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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%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

et al. 2017

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“…As previously introduced in a recent work (Demertzi et al. 2014), in which it was still intended to recognize ten different networks, a support vector machine (SVM) classifier is used to discriminate between neuronal or artifact related independent components (IC). Neuronal ICs are combined in a single scalar map (see Buckner et al.…”

Section: Introductionmentioning

confidence: 99%

Correlation between resting state fMRI total neuronal activity and PET metabolism in healthy controls and patients with disorders of consciousness

et al. 2015

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IntroductionThe mildly invasive 18F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) is a well‐established imaging technique to measure ‘resting state’ cerebral metabolism. This technique made it possible to assess changes in metabolic activity in clinical applications, such as the study of severe brain injury and disorders of consciousness.ObjectiveWe assessed the possibility of creating functional MRI activity maps, which could estimate the relative levels of activity in FDG‐PET cerebral metabolic maps. If no metabolic absolute measures can be extracted, our approach may still be of clinical use in centers without access to FDG‐PET. It also overcomes the problem of recognizing individual networks of independent component selection in functional magnetic resonance imaging (fMRI) resting state analysis.MethodsWe extracted resting state fMRI functional connectivity maps using independent component analysis and combined only components of neuronal origin. To assess neuronality of components a classification based on support vector machine (SVM) was used. We compared the generated maps with the FDG‐PET maps in 16 healthy controls, 11 vegetative state/unresponsive wakefulness syndrome patients and four locked‐in patients.ResultsThe results show a significant similarity with ρ = 0.75 ± 0.05 for healthy controls and ρ = 0.58 ± 0.09 for vegetative state/unresponsive wakefulness syndrome patients between the FDG‐PET and the fMRI based maps. FDG‐PET, fMRI neuronal maps, and the conjunction analysis show decreases in frontoparietal and medial regions in vegetative patients with respect to controls. Subsequent analysis in locked‐in syndrome patients produced also consistent maps with healthy controls.ConclusionsThe constructed resting state fMRI functional connectivity map points toward the possibility for fMRI resting state to estimate relative levels of activity in a metabolic map.

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