Spectral Signatures of Reorganised Brain Networks in Disorders of Consciousness

Abstract

Theoretical advances in the science of consciousness have proposed that it is concomitant with balanced cortical integration and differentiation, enabled by efficient networks of information transfer across multiple scales. Here, we apply graph theory to compare key signatures of such networks in high-density electroencephalographic data from 32 patients with chronic disorders of consciousness, against normative data from healthy controls. Based on connectivity within canonical frequency bands, we found that patient networks had reduced local and global efficiency, and fewer hubs in the alpha band. We devised a novel topographical metric, termed modular span, which showed that the alpha network modules in patients were also spatially circumscribed, lacking the structured long-distance interactions commonly observed in the healthy controls. Importantly however, these differences between graph-theoretic metrics were partially reversed in delta and theta band networks, which were also significantly more similar to each other in patients than controls. Going further, we found that metrics of alpha network efficiency also correlated with the degree of behavioural awareness. Intriguingly, some patients in behaviourally unresponsive vegetative states who demonstrated evidence of covert awareness with functional neuroimaging stood out from this trend: they had alpha networks that were remarkably well preserved and similar to those observed in the controls. Taken together, our findings inform current understanding of disorders of consciousness by highlighting the distinctive brain networks that characterise them. In the significant minority of vegetative patients who follow commands in neuroimaging tests, they point to putative network mechanisms that could support cognitive function and consciousness despite profound behavioural impairment.

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

“…This pattern was further enhanced in emerged from MCS patients, and was strikingly evident in patients with locked-in syndrome and controls. We have previously demonstrated that such frontoparietal patterns of alpha connectivity are neural markers of behaviourally evidenced consciousness, not only in patients with disorders of consciousness (Chennu et al, 2014), but also during propofol sedation (Chennu et al, 2016). The connectivity patterns in Fig.…”

“…Alongside, the cross-spectrum between the spectral decompositions of every pair of channels was used to calculate the debiased weighted phase lag index (dwPLI) measure (see Supplementary material for further details) introduced by Vinck et al (2011). We used this tried-and-tested measure (Chennu et al, 2014(Chennu et al, , 2016Kim et al, 2016) to estimate brain connectivity between pairs of EEG channels in our dataset. Further, we restricted analysis to the delta, alpha and theta bands, as the impact of the considerable electromyographic artefact observed in patients was relatively negligible in these bands.…”

“…These algorithms were used to calculate metrics that captured key topological characteristics of the graphs at multiple scales: the microscale clustering coefficient, macro-scale characteristic path length (Watts and Strogatz, 1998), mesoscale modularity (using the Louvain algorithm; Blondel et al, 2008) and participation coefficient (Guimera and Nunes Amaral, 2005). We also calculated the network-level modular span (Chennu et al, 2014), a metric that captures how the topology of the network is embedded in topographical space over the scalp, by combining the community structure estimated by the Louvain algorithm with the normalized distance between channel pairs (see Supplementary material for details of these metrics). While clustering coefficient and participation coefficient were calculated for each node in a network, characteristic path length, modularity and modular span were calculated for the network as a whole.…”

“…To help address the diagnostic and prognostic challenge in disorders of consciousness, a range of neuroimaging technologies have been proposed for assessing ongoing brain activity with sophisticated analytical techniques. These include MRI (Demertzi et al, 2015), PET (Thibaut et al, 2012;Stender et al, 2014) and high density EEG (Lehembre et al, 2012;King et al, 2013;Lechinger et al, 2013;Chennu et al, 2014;Sitt et al, 2014). EEG in particular is an attractive option in this context as it is portable, cost-effective, and relatively feasible to deploy at the patient's bedside.…”

“…Further work has also shown that the methodology for quantitative analysis of EEG data could be eventually entirely automated, enabling the estimation of the state of consciousness at the patient's bedside using a validated analytical pipeline (Engemann et al, 2015). In this context, we have previously shown that quantitative analysis of high density EEG using network analysis tools developed for brain connectomics research (Rubinov and Sporns, 2010) can identify specific spectral signatures of reorganized brain networks in patients with disorders of consciousness (Chennu et al, 2014).…”