Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state

Abstract

In recent years, numerous electrophysiological signatures of consciousness have been proposed. Here, we perform a systematic analysis of these electroencephalography markers by quantifying their efficiency in differentiating patients in a vegetative state from those in a minimally conscious or conscious state. Capitalizing on a review of previous experiments and current theories, we identify a series of measures that can be organized into four dimensions: (i) event-related potentials versus ongoing electroencephalography activity; (ii) local dynamics versus inter-electrode information exchange; (iii) spectral patterns versus information complexity; and (iv) average versus fluctuations over the recording session. We analysed a large set of 181 high-density electroencephalography recordings acquired in a 30 minutes protocol. We show that low-frequency power, electroencephalography complexity, and information exchange constitute the most reliable signatures of the conscious state. When combined, these measures synergize to allow an automatic classification of patients' state of consciousness.

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

“…This engenders confidence in the reliability of EEG as a valuable tool, as it suggests that different analytical methods could be used to deliver similarly capable diagnostic capabilities. Further, the strength of the relationship between the best brain network metrics we use here and the CRS-R based diagnosis is comparable to that reported in previous literature that has employed EEG-based analysis (King et al, 2013;Sitt et al, 2014). PET (Stender et al, 2016) and TMS-EEG (Casarotto et al, 2016) have been shown to perform better, but both require much more complex technology that is either impossible or difficult to deploy at the patient's bedside.…”

“…This has been shown with PET (Stender et al, 2014) and hinted in previous research with EEG (Sitt et al, 2014). If verified, it would speak to the value of repeatable EEG assessments in not only tracking the recovery of behaviourally evidenced awareness, but also their ability to detect progressive improvements in the underlying neurological functions that support such recovery before they can be observed at the bedside.…”

“…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.…”

“…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. Recent research has shown that both qualitative assessment by experts (Forgacs et al, 2014;Bagnato et al, 2016;Estraneo et al, 2016;Piarulli et al, 2016) and quantitative assessment using quasi-automated machine learning (Sitt et al, 2014) can be effective for identifying the state of consciousness based on ongoing electrical brain activity measured non-invasively from the scalp. 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).…”

Stratification of unresponsive patients by an independently validated index of brain complexity

“…This check is important because the reliable detection of MCS patients represents a general challenge for bedside, brain‐based indices of consciousness. For example, the P3b potential elicited by global violations of auditory regularities, a candidate signature of the presence of consciousness, can be found in only up to 31% of MCS patients,30 measures of resting EEG connectivity such as weighted symbolic mutual information result in a 71% sensitivity, and the best combination of 92 quantitative measures derived from both resting and evoked EEG achieves a sensitivity of 78% 31. Besides quantitative analysis, the clinical qualitative assessment of the EEG background offers a practical tool at the bedside that, if properly interpreted, may outperform some of the above indices 26.…”