Consciousness Indexing and Outcome Prediction with Resting-State EEG in Severe Disorders of Consciousness

Abstract

We applied the following methods to resting-state EEG data from patients with disorders of consciousness (DOC) for consciousness indexing and outcome prediction: microstates, entropy (i.e. approximate, permutation), power in alpha and delta frequency bands, and connectivity (i.e. weighted symbolic mutual information, symbolic transfer entropy, complex network analysis). Patients with unresponsive wakefulness syndrome (UWS) and patients in a minimally conscious state (MCS) were classified into these two categories by fitting and testing a generalised linear model. We aimed subsequently to develop an automated system for outcome prediction in severe DOC by selecting an optimal subset of features using sequential floating forward selection (SFFS). The two outcome categories were defined as UWS or dead, and MCS or emerged from MCS. Percentage of time spent in microstate D in the alpha frequency band performed best at distinguishing MCS from UWS patients. The average clustering coefficient obtained from thresholding beta coherence performed best at predicting outcome. The optimal subset of features selected with SFFS consisted of the frequency of microstate A in the 2-20 Hz frequency band, path length obtained from thresholding alpha coherence, and average path length obtained from thresholding alpha coherence. Combining these features seemed to afford high prediction power. Python and MATLAB toolboxes for the above calculations are freely available under the GNU public license for non-commercial use ( https://qeeg.wordpress.com ).

Electrophysiological and Neuroimaging Studies – During Resting State and Sensory Stimulation in Disorders of Consciousness: A Review

“…Thus, a higher value of entropy indicates that the subject is awake, while lower values indicate deeper unconsciousness (Bruhn et al, 2003;Gosseries et al, 2011;Wu et al, 2011;Thul et al, 2016). It has been found that the mean values of approximate entropy (ApEn) were significantly lower in patients than controls (Sarà and Pistoia, 2010;Sarà et al, 2011;Wu et al, 2011;Stefan et al, 2018) and also the mean EEG entropy values for DOC patients had a positive linear correlation with CRS-R scores (Bruhn et al, 2003). Further, Lempel-Ziv complexity (LZC) and cross-approximate entropy values were significantly lower for the PVS, followed by MCS, and the highest for controls (Wu et al, 2011).…”

“…Non-linear analysis of resting EEG using indices like complexity and entropy has also been used in several studies (Sarà and Pistoia, 2010;Gosseries et al, 2011;Sarà et al, 2011;Wu et al, 2011;Sitt et al, 2014;Stefan et al, 2018;Zhu et al, 2019) to quantify the degree of consciousness in DOC patients. Entropy of EEG is a measure of its regularity.…”

“…Functional connectivity has also been employed to assess the level of integration and connection of brain networks (Vanhaudenhuyse et al, 2010;Lehembre et al, 2012;Höller et al, 2014;Sitt et al, 2014;Schorr et al, 2016;Chennu et al, 2017). The most commonly used measure of connectivity is coherence (C), but it can be contaminated by volume conduction or artifactual correlation between the electrodes (Nunez et al, 1997;Srinivasan et al, 1998;Lehembre et al, 2012;Schorr et al, 2016;Stefan et al, 2018). Other approaches such as phase locking index, phase coherence, imaginary part of coherence (IC) and phase lag index (PLI) have been used very frequently in most of the studies.…”

Decoding the circuitry of consciousness: From local microcircuits to brain-scale networks

“…Thus, GBI processes suggest that the role of inhibition is more complex than preventing excessive activation of brain networks, contributing instead to shaping anatomical brain networks into functional networks (Avena-Koenigsberger et al, 2017). Possible alterations of GBI were reported in studies showing that EEG alpha power is lower in UWS than in MCS patients (Lehembre et al, 2012;Stefan et al, 2018), hinting that the neurobiological mechanisms underlying alpha oscillations generation and associated GBI are profoundly altered in unresponsive patients. Moreover, alpha activity was highly synchronized and clustered in central and posterior cortical regions in UWS patients (Lehembre et al, 2012;Stefan et al, 2018), suggesting a possible failure of GBI in the most severe disorders of consciousness.…”

Toward Improving Diagnostic Strategies in Chronic Disorders of Consciousness: An Overview on the (Re-)Emergent Role of Neurophysiology

“…This is a more advanced step in the differential diagnosis, as it requires denser EEG recording electrodes (e.g., [63,64,73,74]) and specific analytic and statistical procedures. In particular, EEG-based connectivity assessment is useful to differentiate and prognosticate patients with DoC, especially using active tasks [79].…”