Hubs of brain functional networks are radically reorganized in comatose patients

Achard, Sophie; Delon‐Martin, Chantal; Vértes, Petra E.; Renard, Félix; Schenck, Maleka; Schneider, Francis; Heinrich, Christian; Kremer, Stéphane; Bullmore, Edward T.

doi:10.1073/pnas.1208933109

Cited by 272 publications

(370 citation statements)

References 40 publications

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“…We have shown that the progressive re-emergence of connectivity hubs in EEG brain networks, as measured by participation coefficients, tracks the consistency with which consciousness can be measured with the CRS-R, with accuracy comparable to PET-based assessment by an expert. Indeed, the notion that connectivity hubs in specific frontal and parietal loci are important for the recovery of consciousness after brain injury is consistent with evidence from both PET (Stender et al, 2014(Stender et al, , 2015(Stender et al, , 2016 and functional MRI (Vanhaudenhuyse et al, 2010b;Achard et al, 2012). Further, as patients recover beyond MCS, it appears that both positive and negative correlations of activity within and between networks also reappear (Thibaut et al, 2012;Di Perri et al, 2016).…”

Section: Discussionsupporting

confidence: 53%

Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness

Chennu

Annen²,

Wannez³

et al. 2017

Brain

232

255

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Recent advances in functional neuroimaging have demonstrated novel potential for informing diagnosis and prognosis in the unresponsive wakeful syndrome and minimally conscious states. However, these technologies come with considerable expense and difficulty, limiting the possibility of wider clinical application in patients. Here, we show that high density electroencephalography, collected from 104 patients measured at rest, can provide valuable information about brain connectivity that correlates with behaviour and functional neuroimaging. Using graph theory, we visualize and quantify spectral connectivity estimated from electroencephalography as a dense brain network. Our findings demonstrate that key quantitative metrics of these networks correlate with the continuum of behavioural recovery in patients, ranging from those diagnosed as unresponsive, through those who have emerged from minimally conscious, to the fully conscious locked-in syndrome. In particular, a network metric indexing the presence of densely interconnected central hubs of connectivity discriminated behavioural consciousness with accuracy comparable to that achieved by expert assessment with positron emission tomography. We also show that this metric correlates strongly with brain metabolism. Further, with classification analysis, we predict the behavioural diagnosis, brain metabolism and 1-year clinical outcome of individual patients. Finally, we demonstrate that assessments of brain networks show robust connectivity in patients diagnosed as unresponsive by clinical consensus, but later rediagnosed as minimally conscious with the Coma Recovery Scale-Revised. Classification analysis of their brain network identified each of these misdiagnosed patients as minimally conscious, corroborating their behavioural diagnoses. If deployed at the bedside in the clinical context, such network measurements could complement systematic behavioural assessment and help reduce the high misdiagnosis rate reported in these patients. These metrics could also identify patients in whom further assessment is warranted using neuroimaging or conventional clinical evaluation. Finally, by providing objective characterization of states of consciousness, repeated assessments of network metrics could help track individual patients longitudinally, and also assess their neural responses to therapeutic and pharmacological interventions. Keywords: disorders of consciousness; electroencephalography; positron emission tomography; resting state; brain networks Abbreviations: AUC = area under the curve; CRS-R = Coma Recovery Scale-Revised; dwPLI = debiased weighted phase lag index;GOS-E = Glasgow Outcome Scale-Extended; MCS = minimally conscious state; SVM = support vector machine; UWS = unresponsive wakefulness syndrome

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Section: Discussionsupporting

confidence: 53%

Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness

Chennu

Annen²,

Wannez³

et al. 2017

Brain

232

255

View full text Add to dashboard Cite

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“…It was also notable that although both networks had a rich club, the anatomical locations of the rich-club nodes did not overlap between the two networks. We hypothesize that the anatomical locations of high-degree hubs (and rich clubs) can change dynamically in parallel to changes in type and levels of cognitive processing, whereas the global network topological properties of the brain functional network are relatively conserved under different cognitive conditions (39). During cognitive effort, nodes such as those classically described as part of attention networks become highly connected, whereas nodes that are more highly connected during rest, such as regions of the default-mode network, become less hub-like under conditions of cognitive stress.…”

Section: Discussionmentioning

confidence: 99%

Cognitive relevance of the community structure of the human brain functional coactivation network

Crossley

Mechelli

Vértes

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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423

335

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There is growing interest in the complex topology of human brain functional networks, often measured using resting-state functional MRI (fMRI). Here, we used a meta-analysis of the large primary literature that used fMRI or PET to measure task-related activation (>1,600 studies; 1985–2010). We estimated the similarity (Jaccard index) of the activation patterns across experimental tasks between each pair of 638 brain regions. This continuous coactivation matrix was used to build a weighted graph to characterize network topology. The coactivation network was modular, with occipital, central, and default-mode modules predominantly coactivated by specific cognitive domains (perception, action, and emotion, respectively). It also included a rich club of hub nodes, located in parietal and prefrontal cortex and often connected over long distances, which were coactivated by a diverse range of experimental tasks. Investigating the topological role of edges between a deactivated and an activated node, we found that such competitive interactions were most frequent between nodes in different modules or between an activated rich-club node and a deactivated peripheral node. Many aspects of the coactivation network were convergent with a connectivity network derived from resting state fMRI data ( n = 27, healthy volunteers); although the connectivity network was more parsimoniously connected and differed in the anatomical locations of some hubs. We conclude that the community structure of human brain networks is relevant to cognitive function. Deactivations may play a role in flexible reconfiguration of the network according to cognitive demand, varying the integration between modules, and between the periphery and a central rich club.

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“…For example, it has been shown that central nodes in healthy subjects are good predictors of atrophy for several neurodegenerative diseases [132] and that subregions of the cingulate cortex (belonging to the default-mode network) are less connected to other resting-state networks (lower participation coefficient) for more severely demented patients [133]. A recent study has also pointed out the importance of nodal centrality metrics, over larger scale metrics, in characterizing the brain graph reorganization in comatose patients and proving network-based neuromarkers that can be used to evaluate consciousness states [134].…”

Section: Topological Metricsmentioning

confidence: 99%

Graph analysis of functional brain networks: practical issues in translational neuroscience

Fallani

Richiardi

Chávez

et al. 2014

Phil. Trans. R. Soc. B

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337

252

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The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective, communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunc- tions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires the know-how of all the methodological steps of the pipeline that manipulate the input brain signals and extract the functional network prop- erties. On the other hand, knowledge of the neural phenomenon under study is required to perform physiologically relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

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Hubs of brain functional networks are radically reorganized in comatose patients

Cited by 272 publications

References 40 publications

Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness

Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness

Cognitive relevance of the community structure of the human brain functional coactivation network

Graph analysis of functional brain networks: practical issues in translational neuroscience

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