Abstract
The prolonged disorders of consciousness (PDOC) pose a challenge for an accurate clinical diagnosis, mainly due to patients' scarce or ambiguous behavioral responsiveness. Measurement of brain activity can support better diagnosis, independent of motor restrictions. Methods based on spectral analysis of resting-state EEG appear as a promising path, revealing specific changes within the internal brain dynamics in PDOC patients. In this study we used a robust method of resting-state EEG power spectrum parameter extraction to identify distinct spectral properties for different types of PDOC. Sixty patients and 37 healthy volunteers participated in this study. Patient group consisted of 22 unresponsive wakefulness patients, 25 minimally conscious patients and 13 patients emerging from the minimally conscious state. Ten minutes of resting EEG was acquired during wakefulness and transformed into individual power spectra. For each patient, using the spectral decomposition algorithm, we extracted maximum peak frequency within 1-14 Hz range in the centro-parietal region, and the antero-posterior (AP) gradient of the maximal frequency peak. All patients were behaviorally diagnosed using coma recovery scale-revised (CRS-R). The maximal peak frequency in the 1-14 Hz range successfully predicted both neurobehavioral capacity of patients as indicated by CRS-R total score and PDOC diagnosis. Additionally, in patients in whom only one peak within the 1-14 Hz range was observed, the AP gradient significantly contributed to the accuracy of prediction. We have identified three distinct spectral profiles of patients, likely representing separate neurophysiological modes of thalamocortical functioning. Etiology did not have significant influence on the obtained results.