We applied the following methods to resting-state EEG data from patients with disorders of consciousness (DOC) for consciousness indexing and outcome prediction: microstates, entropy (i.e. approximate, permutation), power in alpha and delta frequency bands, and connectivity (i.e. weighted symbolic mutual information, symbolic transfer entropy, complex network analysis). Patients with unresponsive wakefulness syndrome (UWS) and patients in a minimally conscious state (MCS) were classified into these two categories by fitting and testing a generalised linear model. We aimed subsequently to develop an automated system for outcome prediction in severe DOC by selecting an optimal subset of features using sequential floating forward selection (SFFS). The two outcome categories were defined as UWS or dead, and MCS or emerged from MCS. Percentage of time spent in microstate D in the alpha frequency band performed best at distinguishing MCS from UWS patients. The average clustering coefficient obtained from thresholding beta coherence performed best at predicting outcome. The optimal subset of features selected with SFFS consisted of the frequency of microstate A in the 2-20 Hz frequency band, path length obtained from thresholding alpha coherence, and average path length obtained from thresholding alpha coherence. Combining these features seemed to afford high prediction power. Python and MATLAB toolboxes for the above calculations are freely available under the GNU public license for non-commercial use ( https://qeeg.wordpress.com ).

We investigated differences of EEG coherence within (short-range), and between (long-range) specified brain areas as diagnostic markers for different states in disorders of consciousness (DOC), and their predictive value for recovery from unresponsive wakefulness syndrome (UWS). EEGs of 73 patients and 24 controls were recorded and coma recovery scale- revised (CRS-R) scores were assessed. CRS-R of UWS patients was collected after 12 months and divided into two groups (improved/unimproved). Frontal, parietal, fronto-parietal, fronto-temporal, and fronto-occipital coherence was computed, as well as EEG power over frontal, parietal, occipital, and temporal areas. Minimally conscious patients (MCS) and UWS patients could not be differentiated based on their coherence patterns or on EEG power. Fronto-parietal and parietal coherence could positively predict improvement of UWS patients, i.e. recovery from UWS to MCS. Parietal coherence was significantly higher in delta and theta frequencies in the improved group, as well as the coherence between frontal and parietal regions in delta, theta, alpha, and beta frequencies. High parietal delta and theta, and high fronto-parietal theta and alpha coherence appear to provide strong early evidence for recovery from UWS with high predictive sensitivity and specificity. Short and long-range coherence can have a diagnostic value in the prognosis of recovery from UWS.

Previous studies could demonstrate that functional magnetic resonance imaging (fMRI), fludeoxyglucose positron emission tomography (FDG-PET), and electroencephalography (EEG) measures contain information about patients suffering from disorders of consciousness (DOC) and thus improve the clinical diagnosis. Additionally, the technical modalities were able to predict the outcome of patients. However, most studies lack proven reproducibility in a clinical setting. We here applied a standardized combined EEG/fMRI/FDG-PET measurement to a cohort of 20 patients suffering from DOC and focused on parameters that have been demonstrated to contain information about diagnosis and prognosis of these patients. We evaluated EEG band power, fMRI connectivity in networks associated with consciousness and sensory networks, as well as absolute glucose uptake in the brain as potential markers of preserved consciousness or favorable outcome. Acquired data were analyzed by a principal component analysis to identify the most important markers in a hypothesis-free manner. These were then analyzed with statistical group comparisons. Absolute FDG-PET could prove that glucose metabolism in the occipital lobe is significantly higher in minimally conscious than in vegetative state patients. Delta band power showed to be prognostic marker for a favorable outcome. We conclude that absolute FDG-PET is a suitable tool to evaluate the level consciousness in DOC patients. Additionally, we propose delta band power as marker of a favorable outcome in DOC patients. We suggest that these findings promote a standardized technical evaluation of DOC patients to improve diagnosis and prognosis.

Patients with unresponsive wakefulness syndrome (UWS) or in minimally conscious state (MCS) after brain injury show significant fluctuations in their behavioural abilities over time. As the importance of event-related potentials (ERPs) in the detection of traces of consciousness increases, we investigated the retest reliability of ERPs with repeated tests at four different time points. Twelve healthy controls and 12 inpatients (8 UWS, 4 MCS; 6 traumatic, 6 non-traumatic) were tested twice a day (morning, afternoon) for 2 days with an auditory oddball task. ERPs were recorded with a 256-channel-EEG system, and correlated with behavioural test scores in the Coma Recovery Scale-revised (CRS-R). The number of identifiable P300 responses varied between zero and four in both groups. Reliabilities varied between Krippendorff's α = 0.43 for within-day comparison, and α = 0.25 for between-day comparison in the patient group. Retest reliability was strong for the CRS-R scores for all comparisons (α = 0.83-0.95). The stability of auditory information processing in patients with disorders of consciousness is the basis for other, even more demanding tasks and cognitive potentials. The relatively low ERP-retest reliability suggests that it is necessary to perform repeated tests, especially when probing for consciousness with ERPs. A single negative ERP test result may be mistaken for proof that a UWS patient truly is unresponsive.