Prognostic Value of Resting-State Electroencephalography Structure in Disentangling Vegetative and Minimally Conscious States

Abstract

Background. Patients in a vegetative state pose problems in diagnosis, prognosis, and treatment. Currently, no prognostic markers predict the chance of recovery, which has serious consequences, especially in end-of-life decision making. Objective. We aimed to assess an objective measurement of prognosis using advanced electroencephalography (EEG). Methods. EEG data (19 channels) were collected in 14 patients who were diagnosed to be persistently vegetative based on repeated clinical evaluations at 3 months following brain damage. EEG structure parameters (amplitude, duration, and variability within quasi-stationary segments, as well as the spatial synchrony between such segments and the strength of this synchrony) were used to predict recovery of consciousness 3 months later. Results. The number and strength of cortical functional connections between EEG segments were higher in patients who recovered consciousness (P < .05 to P < .001) compared with those who did not recover. Linear regression analysis confirms that EEG structure parameters are capable of predicting (P = .0025) recovery of consciousness 6 months postinjury, whereas the same analysis failed to significantly predict patient outcome based on aspects of their clinical history alone (P = .629) or conventional EEG spectrum power (P = .473). Conclusions. The result of this preliminary study demonstrates that structural strategy of EEG analysis is better suited for providing prognosis of consciousness recovery than existing methods of clinical assessment and of conventional EEG. Our results may be a starting point for developing reliable prognosticators in patients who are in a vegetative state, with the potential to improve their day-to-day management, quality of life, and access to early interventions.