Connectivity biomarkers can differentiate patients with different levels of consciousness

Abstract

Objective: In the present study, we searched for resting-EEG biomarkers that distinguish different levels of consciousness on a single subject level with an accuracy that is significantly above chance.

Methods: We assessed 44 biomarkers extracted from the resting EEG with respect to their discriminative value between groups of minimally conscious (MCS, N=22) patients, vegetative state patients (VS, N=27), and - for a proof of concept - healthy participants (N=23). We applied classification with support vector machines.

Results: Partial coherence, directed transfer function, and generalized partial directed coherence yielded accuracies that were significantly above chance for the group distinction of MCS vs. VS (.88, .80, and .78, respectively), as well as healthy participants vs. MCS (.96, .87, and .93, respectively) and VS (.98, .84, and .96, respectively) patients.

Conclusions: The concept of connectivity is crucial for determining the level of consciousness, supporting the view that assessing brain networks in the resting state is the golden way to examine brain functions such as consciousness.

Significance: The present results directly show that it is possible to distinguish patients with different levels of consciousness on the basis of resting-state EEG.

Keywords: Classification; Coherence; DOC; DTF; GPDC; MCS; VS.