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

TLDR

Numerous electrophysiological signatures of consciousness have been proposed in recent years. We systematically analyze EEG markers to quantify their ability to differentiate vegetative from minimally conscious or conscious patients. We identified four dimensions of EEG measures—event‑related versus ongoing activity, local dynamics versus inter‑electrode exchange, spectral patterns versus information complexity, and average versus fluctuations—and applied them to 181 high‑density EEG recordings collected over 30‑minute sessions. Low‑frequency power, EEG complexity, and information exchange are the most reliable signatures, and when combined they enable automatic classification of patients’ consciousness 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.