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

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 consc...

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Bibliographic Details
Published in:Brain (London, England : 1878) Vol. 137; no. Pt 8; p. 2258
Main Authors: Sitt, Jacobo Diego, King, Jean-Remi, El Karoui, Imen, Rohaut, Benjamin, Faugeras, Frederic, Gramfort, Alexandre, Cohen, Laurent, Sigman, Mariano, Dehaene, Stanislas, Naccache, Lionel
Format: Journal Article
Language:English
Published: England 01.08.2014
Subjects:
Adolescent
Adult
Aged
Aged, 80 and over
Biomarkers
Brain - physiopathology
Brain Mapping - classification
Brain Mapping - methods
Brain Mapping - standards
Clinical Protocols
Consciousness Disorders - classification
Consciousness Disorders - etiology
Consciousness Disorders - physiopathology
Electroencephalography - classification
Electroencephalography - methods
Electroencephalography - standards
Evoked Potentials - physiology
Female
Humans
Male
Middle Aged
Persistent Vegetative State - classification
Persistent Vegetative State - etiology
Persistent Vegetative State - physiopathology
Trauma Severity Indices
Young Adult
unresponsive wakefulness syndrome
consciousness
EEG
minimally conscious state
vegetative state
ISSN:1460-2156, 1460-2156
Online Access:Get more information
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Summary: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.
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ISSN:1460-2156
1460-2156
DOI:10.1093/brain/awu141