Aim of the study: A transcranial direct current stimulation (tDCS) protocol (20 min, 2 mA, anodal electrode at the left dorsolateral prefrontal cortex and cathodal electrode at the right supraorbital area) was applied in patients with different degrees of disorders of consciousness (DoC). Although previous research indicates that it could improve patients’ coma recovery scale-revised (CRS-R) scores, the brain's electrophysiological responses to tDCS are still unclear. Therefore, the present study was performed to explore the underlying brain responses of patients in a minimally conscious state (MCS) and an unresponsive wakefulness syndrome (UWS) to tDCS modulation. Materials and methods: Seventeen patients with DoC were recruited in a sham controlled crossover study receiving real and sham tDCS. EEG coherence was used to measure functional connectivity changes induced by the tDCS modulation. Results: After real tDCS modulation, the fronto-parietal coherence significantly increased in the theta band and decreased in the gamma band in the MCS group. No significant changes were found in the UWS group. The coherence responses significantly correlated with the patients’ baseline CRS-R scores. No distinct alteration occurred in the sham session for either the MCS or UWS patients. Conclusions: The coherence responses to the present tDCS protocol may be a tool for diagnosing MCS versus UWS, as they may be a crucial cause of the different clinical effects in the two states.

Transcranial magnetic stimulation (TMS) is a non-invasive technique to stimulate the brain, while electroencephalography (EEG) is a non-invasive technique to record its electrical activity. Their combined use (TMS-EEG) has been established only relatively recently, after successful development of TMS-compatible EEG amplifiers. TMS-EEG offers the unparalleled opportunity to directly perturb the brain with TMS and simultaneously record its response with EEG. This allows inferences on causal input-output relationships, therefore going critically beyond purely observational techniques, such as resting-state EEG or functional MRI, in the study of brain dynamics. This consensus review updates the work of Tremblay and coworkers [Clin Neurophysiol 2019; 130: 802-844]. Since then, substantial advances have been made in understanding contamination of TMS-EEG signals by physiological and non-physiological artifacts, as well as in developing strategies to avoid or control them. In parallel, new insights have emerged regarding the physiological mechanisms underlying TMS-EEG responses and their diagnostic and prognostic utility in a broad range of psychiatric and neurological disorders. As such, TMS-EEG is rapidly shaping a dynamic new field in clinical neurophysiology and neuroscience. This review provides a critical and comprehensive synthesis of current knowledge, including practical guidance for implementing TMS-EEG in the clinical setting.

Objective: Spinal cord stimulation (SCS) is a valuable treatment for patients with disorders of consciousness (DOC). This study used permutation entropy (PeEn) of neural activities to quantify brain responses to SCS. Method: We recruited 14 patients with DOC, including seven patients in minimally conscious state (MCS) and seven patients in vegetative state/unawareness state (VS/UWS). All patients received a single session of 20 min’ continuous SCS. We recorded resting state EEG before, during and after SCS. In this study, PeEn was first calculated to describe overall neural activities changes in SCS. The brain was then divided into frontal, central, parietal and occipital regions to explore spatial SCS modulation effects. Finally, a correlation analysis was conducted between CRS-R values and changes in PeEn on each of the four regions. Results: SCS was associated with short-term changes in neural activities in DOC. When SCS was on, PeEn increased as compared to the baseline. When SCS was shut off, PeEn decreased. The PeEn of all patients after SCS was higher than before SCS, and changes of PeEn for MCS were more significant than those for VS, especially in the frontal region. Conclusion: PeEn from EEG data could be used to evaluate SCS modulation effects, and EEG complexity might be a critical index to describe brain responses to SCS in DOC.

Objective : Significant spectral electroencephalogram (EEG) pattern characteristics exist in individual patients during the re-establishment of consciousness after general anesthesia. However, these EEG patterns cannot be quantitatively identified using commercially available depth of anesthesia (DoA) monitors. This study proposes an effective classification method and indices to classify these patterns among patients. Approach : Four types of emergence EEG patterns were identified based on the EEG data set from 52 patients undergoing sevoflurane general anesthesia from two hospitals. Then, the relative power spectrum density (RPSD) of five frequency sub-bands of clinical interest (delta, theta, alpha, beta and gamma) were selected for emergence state analysis. Finally, a genetic algorithm support vector machine (GA-SVM) was used to identify the emergence EEG patterns. The performance was reported in terms of sensitivity (SE), specificity (SP) and accuracy (AC). Main results : The combination of the mean and mode of RPSD in the delta and alpha band ( P (delta)/ P (alpha) performed the best in the GA-SVM classification. The AC indices obtained by GA-SVM across the four patterns were 90.64 ± 7.61, 81.79 ± 5.84, 82.14 ± 7.99 and 72.86 ± 11.11 respectively. Furthermore, the emergence time of the patients with EEG emergence patterns I and III increased as the patients’ age increased. However, for patients with EEG emergence pattern IV, the emergence time positively correlates with the patients’ age when they are under 50, and negatively correlates with it when they are over 50. Significance : The mean and mode of P (delta)/ P (alpha) is a useful index to classify the different emergence EEG patterns. In addition, these patterns may correlate with an underlying neural substrate which is related to the patients’ age. Highlights ► Four emergence EEG patterns were found in γ -amino-butyric acid (GABA)-ergic anesthetic drugs. ► A genetic algorithm combined with a support vector machine (GA-SVM) was proposed to identify the emergence EEG patterns. ► The relative power spectrum density (RPSD) was used as a feature to classify the emergence EEG patterns and good accuracy was achieved. ► The statistics shows that the emergence EEG patterns are age-related and may have value in assessing postoperative brain states.

Background: Electroencephalogram (EEG) studies have established many characteristics relevant to consciousness levels of patients with disorder of consciousness (DOC). Although the frontal and parietal brain regions were often highlighted in DOC studies, their electro-neurophysiological roles in constructing human consciousness remain unclear because of the fragmented information from literatures and the complexity of EEG characteristics.Methods: Existing EEG studies of DOC patients were reviewed and summarized. Relevant findings and results about the frontal and parietal regions were filtered, compared, and concluded to clarify their roles in consciousness classification and outcomes. The evidence covers multi-dimensional EEG characteristics including functional connectivity, non-linear dynamics, spectrum power, transcranial magnetic stimulation-electroencephalography (TMS-EEG), and event-related potential.Results and conclusion: Electroencephalogram characteristics related to frontal and parietal regions consistently showed high relevance with consciousness: enhancement of low-frequency rhythms, suppression of high-frequency rhythms, reduction of dynamic complexity, and breakdown of networks accompanied with decreasing consciousness. Owing to the limitations of EEG, existing studies have not yet clarified which one between the frontal and parietal has priority in consciousness injury or recovery. Source reconstruction with high-density EEG, machine learning with large samples, and TMS-EEG mapping will be important approaches for refining EEG awareness locations.