Method for quantifying arousal and consciousness in healthy states and severe brain injury via EEG-based measures of corticothalamic physiology.

A method using corticothalamic neural field theory (NFT) fits to EEG spectra can distinguish conscious from unconscious states in healthy and brain-injured subjects. Healthy subjects in wake and REM sleep, those in deep sleep, and brain-injured patients (unresponsive wakefulness syndrome, minimally conscious state, emerged from MCS) cluster into three groups based on two parameters: the difference between corticocortical and corticothalamic feedbacks (X-Y) and mean neural response rates (α and β). X-Y is smaller in conscious states (wake/REM) than in sleep but does not differentiate among brain injuries. The method can be automated on a personal computer, unlike laborious clinical assessments or measures like Φ from integrated information theory.

Study at a glance

Abstract

Characterization of normal arousal states has been achieved by fitting predictions of corticothalamic neural field theory (NFT) to electroencephalographic (EEG) spectra to yield relevant physiological parameters. A prior fitting method is extended to distinguish conscious and unconscious states in healthy and brain injured subjects by identifying additional parameters and clusters in parameter space. Fits of NFT predictions to EEG spectra are used to estimate neurophysiological parameters in healthy and brain injured subjects. Spectra are used from healthy subjects in wake and sleep and from patients with unresponsive wakefulness syndrome, in a minimally conscious state (MCS), and emerged from MCS. Subjects cluster into three groups in parameter space: conscious healthy (wake and REM), sleep, and brain injured. These are distinguished by the difference X-Y between corticocortical (X) and corticothalamic (Y) feedbacks, and by mean neural response rates α and β to incoming spikes. X-Y tracks consciousness in healthy individuals, with smaller values in wake/REM than sleep, but cannot distinguish between brain injuries. Parameters α and β differentiate deep sleep from wake/REM and brain injury. Other methods typically rely on laborious clinical assessment, manual EEG scoring, or evaluation of measures like Φ from integrated information theory, for which no efficient method exists. In contrast, the present method can be automated on a personal computer. The method provides a means to quantify consciousness and arousal in healthy and brain injured subjects, but does not distinguish subtypes of brain injury.