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

Abstract

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

A fast and general method to empirically estimate the complexity of brain responses to transcranial and intracranial stimulations

“…Measures of brain complexity have recently begun to move from the realm of theoretical neuroscience [1][2][3][4][5][6] into the field of experimental neurophysiology to study differences between global brain states, from wakefulness to sleep and anesthesia [7][8][9][10]. Further, measures of brain complexity have been considered as useful paraclinical indices to assess consciousness at the bedside of brain-injured patients [11][12][13][14][15]. In this spirit, a novel strategy based on quantifying the global effects of direct cortical perturbations was recently introduced [16].…”

Decreased integration of EEG source-space networks in disorders of consciousness

“…Previous EEG network-based studies in the context of DOC have been performed at the scalp level (Chennu et al, 2014;Chennu et al, 2017) with satisfactory accuracies in classifying UWS and MCS patients (Sitt et al, 2014;Chennu et al, 2017;Engemann et al, 2018).…”

“…However, the biological interpretation of corresponding network alterations is not straightforward, since scalp EEG signals are corrupted by the volume conduction due to the head electrical conduction properties (Brunner et al, 2016;Van de Steen et al, 2016). Several studies have indeed reported the limitations of computing connectivity at the EEG scalp level (see for review (Schoffelen and Gross, 2009;Hassan and Wendling, 2018) even if this can be compensated by methods removing zero-lag components, (Vinck et al, 2011;Chennu et al, 2017). More essentially, scalp analysis does not allow making inferences about interacting brain regions.…”

“…It provides complementary information to source connectivity methods by quantifying functional and/or statistical aspects of identified brain networks. Among the few studies using graph theory in DOC, a common finding is the identification of disturbances in overall network integration, usually computed by modularitybased approaches (Crone et al, 2014;Demertzi et al, 2015;Chennu et al, 2017). However, to what extent the balance between EEG frequency-dependent network segregation (local information processing) and integration (global information processing) is altered in DOC remains elusive, which is the main objective of this paper.…”

Intrinsic network reactivity differentiates levels of consciousness in comatose patients

“…Our goal is to devise a strategy that can offer an EEG-based assessment of brain responsiveness in resting state, without the need for extrinsic stimulation. Although there have been previous attempts to reach this goal (Bai et al, 2017), these attempts have been mostly based on information-theoretic (Pollonini et al, 2010; Sarà & Pistoia, 2010; Gosseries et al, 2011; King et al, 2013; Marinazzo et al, 2014; Thul et al, 2016) or graph-theoretic (Lehembre et al, 2012; Leon-Carrion et al, 2012; Chennu et al, 2014; Höller et al, 2014; Varotto et al, 2014; Chennu et al, 2017; Numan et al, 2017) metrics, which do not provide direct information about the underlying neuronal dynamics. Furthermore, the majority of these studies focus on distinguishing minimally conscious patients from patients in vegetative state.…”