Outcome prediction in prolonged disorders of consciousness (DOC) remains challenging. This can result in either inappropriate withdrawal of treatment or unnecessary prolongation of treatment. Electroencephalography (EEG) is a cheap, portable, and non-invasive device with various opportunities for complex signal analysis. Computational EEG measures, such as EEG connectivity and network metrics, might be ideal candidates for the investigation of DOC, but their capacity in prognostication is still undisclosed. We conducted a meta-analysis aiming to compare the prognostic power of the widely used clinical scale, Coma Recovery Scale-Revised - CRS-R and EEG connectivity and network metrics. We found that the prognostic power of the CRS-R scale was moderate (AUC: 0.67 (0.60-0.75)), but EEG connectivity and network metrics predicted outcome with significantly (p = 0.0071) higher accuracy (AUC:0.78 (0.70-0.86)). We also estimated the prognostic capacity of EEG spectral power, which was not significantly (p = 0.3943) inferior to that of the EEG connectivity and graph-theory measures (AUC:0.75 (0.70-0.80)). Multivariate automated outcome prediction tools seemed to outperform clinical and EEG markers.

OBJECTIVE: High-definition transcranial direct current stimulation (HD-tDCS) has been an effective neurostimulation method in the treatment of disorders of consciousness (DOC). However, the effects and mechanism of HD-tDCS are still unclear.
METHODS: This study recruited 8 DOC patients and applied 20-min sessions of 2 mA HD-tDCS (central anode electrode at Pz) for 14 consecutive days. We record DOC patients\' EEG data and Coma Recovery Scale-Revised (CRS-R) values at four time point: baseline (T0), after 1 day\'s and 7,14 days\' parietal HD-tDCS treatment (T1, T2, T3). Power spectral density (PSD), relative power (RP), spectral entropy and spectral exponent were calculated to evaluate the EEG dynamic changes of DOC patients during long-term parietal HD-tDCS. At last, we calculated the correlation between changes of EEG features and changes of CRS-R values.
RESULTS: After 1 day\'s parietal HD-tDCS, DOC patients\' CRS-R value had not changed (8.25 ± 1.91). HD-tDCS improved DOC patients\' CRS-R value at T2 (9.75 ± 1.91, p < 0.05) and at T3 (11.38 ± 2.77, p < 0.05), compared with that at T0 (8.25 ± 1.91). As the treatment time increased, the EEG PSD decayed more slowly. Specifically, the delta frequency band RP decreased, while the alpha, beta, and gamma frequency bands RP increased. EEG oscillation characteristics changed but not significant at T1 (p > 0.05), and showed significant changes at T2 and T3 (p < 0.05). The spectral entropy continuously increased and the spectral exponent continuously decreased from T0 to T3. Specifically, the spectral entropy and spectral exponent of the parietal and occipital regions were significantly higher at T2 and T3 than that at T0 (p < 0.05). In addition, The changes in EEG features of the parietal and occipital lobes were correlated with changes in CRS-R value, especially between T2 and T0.
CONCLUSIONS: Long-term parietal HD-tDCS can improve the consciousness level and brain activity in DOC patients. Resting-state EEG can evaluate the dynamic changes of brain activity in DOC patients during HD-tDCS. EEG oscillation and non-oscillatory activity might be used to explain the mechanism of HD-tDCS on DOC patients.

Introduction: The Wessex Head Injury Matrix (WHIM) was developed to assess patients with disorders of consciousness (DOC) and was tested in terms of inter-rater reliability (IRR) and test-retest reliability (TRR) in the year 2000. The American Congress of Rehabilitation and Medicine reported that IRR and TRR were unproven. We aim to assess the reliability of the WHIM in prolonged DOC patients (PDOC). Methods: A total of 51 PDOC patients (32 unresponsive wakefulness syndrome (UWS/VS) and 19 minimally conscious state (MCS)) who were hosted in a dedicated unit for long-term brain injury care were enrolled. The time from injury ranged from 182 to 3325 days. Two raters administered the Coma Recovery Scale-Revised (CRS-R) and the WHIM to test the IRR and TRR. The TRR was administered two weeks after the first assessment. Results: For the CRS-R, the agreement in IRR and TRR was perfect between the two raters. The agreement for the WHIM ranged from substantial to almost perfect for IRR and from fair to substantial for the TRR. Conclusions: The WHIM showed a strong IRR when administered by expert raters and strongly correlated with the CRS-R. This study provides further evidence of the psychometric qualities of the WHIM and the importance of its use in PDOC patients.

OBJECTIVE: The application of spinal cord stimulation (SCS) in the treatment of disorders of consciousness (DOC) has attracted attention, but its effect on brain activity is still unknown. Transcranial magnetic stimulation combined with EEG (TMS-EEG) can measure cortical activity, which can evaluate the effect of SCS on DOC.
METHODS: We record 20 DOC patients\' CRS-R values and TMS-EEG data before and after one-session SCS (Pre-SCS and Post-SCS). 20 DOC patients including 10 patients with unresponsive wakefulness syndrome (UWS) and 10 patients with minimally conscious states (MCS). TMS evoked potential (TEP) was used to measure the changes of cortical activity in DOC patients between Pre-SCS and Post-SCS. Firstly, we used the global mean field potential (GMFP) and fast perturbational complexity index (PCIst) to compare the temporal changes of patients\' cortical activity. Then, we obtained the frequency feature (natural frequency, NF) based on the TEP time-frequency analysis, and compared the changes of natural frequency between Pre-SCS and Post-SCS. Finally, the study explored the relationship between the patient\'s baseline CRS-R values and changes of TMS evoked cortical activity in time and frequency domains.
RESULTS: After SCS, MCS and UWS groups almost have no changes of CRS-R values (MCS: 9.9 ± 1.52 at Pre-SCS, 10.2 ± 1.48 at Post-SCS; UWS: 5.6 ± 1.26 at Pre-SCS, 5.7 ± 1.34 at Post-SCS). MCS group showed significant increases of GMFP amplitude (around 100 ms and 300 ms) and PCIst values at Post-SCS (p < 0.05). UWS group had no significant changes (p > 0.05). Besides, SCS induced the significant increases of natural frequency for MCS group(p < 0.05), but not for UWS group. At last, the study found that all patient\'s baseline CRS-R values were significantly correlated with ΔPCIst (r = 0.67, p < 0.005), and ΔNF (r = 0.72, p < 0.001).
CONCLUSIONS: SCS can modulate cortical activity of DOC patient, including temporal complexity and natural frequency. The changes of cortical activity caused by SCS are related to patients\' consciousness level. TMS-EEG can evaluate the effect of SCS on DOC patients.

BACKGROUND: The principal conditions differentiating disorders of consciousness (DOC) patients are the unresponsive wakefulness syndrome/vegetative state (UWS/VS) and the minimally conscious state (MCS). Many individuals who suffer from sudden-onset severe brain injury move through stages of UWS/VS and MCS before regaining full awareness. In some patients, the DOC condition is protracted for years (PDOC). In this study, we observed PDOC patients for 6 months to assess possible changes in their level of consciousness.
METHODS: We enrolled 40 PDOC patients, 23 UWS/VS and 17 MCS hosted in a dedicated unit for long-term brain injury care. The time from injury was 472 ± 533 days for UWS/VS and 1090 ± 1079 days for MCS. The Wessex Head Injury Matrix (WHIM), Coma Recovery Scale-R (CRS-R), and Nociception Coma Scale were administered monthly for 6 months.
RESULTS: During the period of assessment, the percentage of UWS/VS shifted from 58 to 45%, while for the MCS, from 42 to 55%. A positive correlation was found for the UWS/VS patients between the months of observation with the CRS-R total score and WHIM total numbers of behaviors (TNB). In the UWS/VS group, the CRS-R auditive and visual subscales correlated positively with the observation time. During the whole period of observation, 8 patients had constant CRS-R total scores while the WHIM TNB changed in 7 of them.
CONCLUSIONS: Our findings demonstrated that the monthly assessment of PDOC by means of the CRS-R and WHIM was able to detect also subtle changes in consciousness level.

Prolonged Disorders of Consciousness (PDOC) resulting from severe acquired brain injury can lead to complex disabilities that make diagnosis challenging. The role of machine learning (ML) in diagnosing PDOC states and identifying intervention strategies is relatively under-explored, having focused on predicting mortality and poor outcome. This study aims to: (a) apply ML techniques to predict PDOC diagnostic states from variables obtained from two non-invasive neurobehavior assessment tools; and (b) apply network analysis for guiding possible intervention strategies.
The Coma Recovery Scale-Revised (CRS-R) is a well-established tool for assessing patients with PDOC. More recently, music has been found to be a useful medium for assessment of coma patients, leading to the standardization of a music-based assessment of awareness: Music Therapy Assessment Tool for Awareness in Disorders of Consciousness (MATADOC). CRS-R and MATADOC data were collected from 74 PDOC patients aged 16-70 years at three specialist centers in the USA, UK and Ireland. The data were analyzed by three ML techniques (neural networks, decision trees and cluster analysis) as well as modelled through system-level network analysis.
PDOC diagnostic state can be predicted to a relatively high level of accuracy that sets a benchmark for future ML analysis using neurobehavioral data only. The outcomes of this study may also have implications for understanding the role of music therapy in interdisciplinary rehabilitation to help patients move from one coma state to another.
This study has shown how ML can derive rules for diagnosis of PDOC with data from two neurobehavioral tools without the need to harvest large clinical and imaging datasets. Network analysis using the measures obtained from these two non-invasive tools provides novel, system-level ways of interpreting possible transitions between PDOC states, leading to possible use in novel, next-generation decision-support systems for PDOC.

For patients with disorders of consciousness, such as unresponsive wakefulness syndrome (UWS) patients and minimally conscious state (MCS) patients, their long treatment cycle and high cost commonly put a heavy burden on the patient\'s family and society. Therefore, it is vital to accurately diagnose and predict consciousness recovery for such patients. In this paper, we explored the role of the P300 signal based on an audiovisual BCI in the classification and prognosis prediction of patients with disorders of consciousness. This experiment included 18 patients: 10 UWS patients and 8 MCS- patients. At the three-month follow-up, we defined patients with an improved prognosis (from UWS to MCS-, from UWS to MCS+, or from MCS- to MCS+) as \"improved patients\" and those who stayed in UWS/MCS as \"not improved patients\". First, we compared and analyzed different types of patients, and the results showed that the P300 detection accuracy rate of \"improved\" patients was significantly higher than that of \"not improved\" patients. Furthermore, the P300 detection accuracy of traumatic brain injury (TBI) patients was significantly higher than that of non-traumatic brain injury (NTBI, including acquired brain injury and cerebrovascular disease) patients. We also found that there was a positive linear correlation between P300 detection accuracy and CRS-R score, and patients with higher P300 detection accuracy were likely to achieve higher CRS-R scores. In addition, we found that the patients with higher P300 detection accuracies tend to have better prognosis in this audiovisual BCI. These findings indicate that the detection accuracy of P300 is significantly correlated with the level of consciousness, etiology, and prognosis of patients. P300 can be used to represent the preservation level of consciousness in clinical neurophysiology and predict the possibility of recovery in patients with disorders of consciousness.

Deep brain stimulation (DBS) is a potential neuromodulatory therapy that enhances recovery from disorders of consciousness, especially minimally conscious state (MCS). This study measured the effects of DBS on the brain and explored the underlying mechanisms of DBS on MCS.
Nine patients with MCS were recruited for this study. The neuromodulation effects of 100 Hz DBS were explored via cross-control experiments. Coma Recovery Scale-Revised (CRS-R) and EEG were recorded, and corresponding functional connectivity and network parameters were calculated.
Our results showed that 100 Hz DBS could improve the functional connectivity of the whole, local and local-local brain regions, while no significant change in EEG functional connectivity was observed in sham DBS. The whole brain\'s network parameters (clustering coefficient, path length, and small world characteristic) were significantly improved. In addition, a significant increase in the CRS-R and functional connectivity of three MCS patients who received 100 Hz DBS for 6 months were observed.
This study showed that DBS improved EEG functional connectivity and brain networks, indicating that the long-term use of DBS could improve the level of consciousness of MCS patients.

OBJECTIVE: To evaluate the feasibility of using a wearable eye-tracker when assessing patients with prolonged disorders of consciousness using the Coma Recovery Scale Revised (CRS-R), focusing on technical challenges.
METHODS: A methodological investigation with descriptive and analytical elements.
METHODS: Four patients with prolonged disorders of consciousness were recruited from the rehabilitation clinic of a regional rehabilitation unit.
METHODS: A selection of subtests in the CRS-R were performed while recording eye movements with a wearable eye-tracker.
RESULTS: No major adverse reactions were observed, suggesting likely patient acceptability. Calibration was not always possible. However, distinct eye movements were discernible from the recorded data even without calibration, and analysis of these produced results with the potential to support clinical assessment.
CONCLUSIONS: Eye tracking was feasible during clinical assessment for this patient group. Recording eye movement responses in these easily fatigued patients has the potential to add sensitivity for detection of conscious responses and to complement clinical examination. Further study is merited. Current hardware and software limitations can be overcome with manual data processing and analysis; however, significant developments in automating data processing will be required for broader clinical application.

Persons diagnosed with disorders of consciousness (DOC) typically suffer from motor and cognitive disabilities. Recent research has shown that non-invasive brain-computer interface (BCI) technology could help assess these patients\' cognitive functions and command following abilities. 20 DOC patients participated in the study and performed 10 vibro-tactile P300 BCI sessions over 10 days with 8-12 runs each day. Vibrotactile tactors were placed on the each patient\'s left and right wrists and one foot. Patients were instructed, via earbuds, to concentrate and silently count vibrotactile pulses on either their left or right wrist that presented a target stimulus and to ignore the others. Changes of the BCI classification accuracy were investigated over the 10 days. In addition, the Coma Recovery Scale-Revised (CRS-R) score was measured before and after the 10 vibro-tactile P300 sessions. In the first run, 10 patients had a classification accuracy above chance level (>12.5%). In the best run, every patient reached an accuracy ≥60%. The grand average accuracy in the first session for all patients was 40%. In the best session, the grand average accuracy was 88% and the median accuracy across all sessions was 21%. The CRS-R scores compared before and after 10 VT3 sessions for all 20 patients, are showing significant improvement (p = 0.024). Twelve of the twenty patients showed an improvement of 1 to 7 points in the CRS-R score after the VT3 BCI sessions (mean: 2.6). Six patients did not show a change of the CRS-R and two patients showed a decline in the score by 1 point. Every patient achieved at least 60% accuracy at least once, which indicates successful command following. This shows the importance of repeated measures when DOC patients are assessed. The improvement of the CRS-R score after the 10 VT3 sessions is an important issue for future experiments to test the possible therapeutic applications of vibro-tactile and related BCIs with a larger patient group.