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Prolonged disorders of consciousness (pDOC) are pathological conditions of alterations in consciousness caused by various severe brain injuries, profoundly affecting patients' life ability and leading to a huge burden for both the family and society. Exploring the mechanisms underlying pDOC and accurately assessing the level of consciousness in the patients with pDOC provide the basis of developing therapeutic strategies. Research of non-invasive functional neuroimaging technologies, such as functional magnetic resonance (fMRI) and scalp electroencephalography (EEG), have demonstrated that the generation, maintenance and disorders of consciousness involve functions of multiple cortical and subcortical brain regions, and their networks. Invasive intracranial neuroelectrophysiological technique can directly record the electrical activity of subcortical or cortical neurons with high signal-to-noise ratio and spatial resolution, which has unique advantages and important significance for further revealing the brain function and disease mechanism of pDOC. Here we reviewed the current progress of pDOC research based on two intracranial electrophysiological signals, spikes reflecting single-unit activity and field potential reflecting multi-unit activities, and then discussed the current challenges and gave an outlook on future development, hoping to promote the study of pathophysiological mechanisms related to pDOC and provide guides for the future clinical diagnosis and therapy of pDOC.

Behavioural diagnosis of patients with disorders of consciousness (DOC) is challenging and prone to inaccuracies. Consequently, there have been increased efforts to develop bedside assessment based on EEG and event-related potentials (ERPs) that are more sensitive to the neural factors supporting conscious awareness. However, individual detection of residual consciousness using these techniques is less established. Here, we hypothesize that the cross-state similarity (defined as the similarity between healthy and impaired conscious states) of passive brain responses to auditory stimuli can index the level of awareness in individual DOC patients. To this end, we introduce the global field time-frequency representation-based discriminative similarity analysis (GFTFR-DSA). This method quantifies the average cross-state similarity index between an individual patient and our constructed healthy templates using the GFTFR as an EEG feature. We demonstrate that the proposed GFTFR feature exhibits superior within-group consistency in 34 healthy controls over traditional EEG features such as temporal waveforms. Second, we observed the GFTFR-based similarity index was significantly higher in patients with a minimally conscious state (MCS, 40 patients) than those with unresponsive wakefulness syndrome (UWS, 54 patients), supporting our hypothesis. Finally, applying a linear support vector machine classifier for individual MCS/UWS classification, the model achieved a balanced accuracy and F1 score of 0.77. Overall, our findings indicate that combining discriminative and interpretable markers, along with automatic machine learning algorithms, is effective for the differential diagnosis in patients with DOC. Importantly, this approach can, in principle, be transferred into any ERP of interest to better inform DOC diagnoses.

Background: Accurately diagnosing patients with the vegetative state (VS) and the minimally conscious state (MCS) reached a misdiagnosis of approximately 40%., Methods: A method combined microstate and dynamic functional connectivity (dFC) to study the spatiotemporal variability of the brain in disorders of consciousness (DOC) patients was proposed. Resting-state EEG data were obtained from 16 patients with MCS and 16 patients with VS. Mutual information (MI) was used to assess the EEG connectivity in each microstate. MI-based features with statistical differences were selected as the total feature subset (TFS), then the TFS was utilized to feature selection and fed into the classifier, obtaining the optimal feature subsets (OFS) in each microstate. Subsequently, an OFS-based MI functional connectivity network (MIFCN) was constructed in the cortex., Results: The group-average MI connectivity matrix focused on all channels revealed that all five microstates exhibited stronger information interaction in the MCS when comparing with the VS. While OFS-based MIFCN, which only focused on a few channels, revealed greater MI flow in VS patients than in MCS patients under microstates A, B, C, and E, except for microstate D. Additionally, the average classification accuracy of OFS in the five microstates was 96.2%., Conclusion: Constructing features based on microstates to distinguish between two categories of DOC patients had effectiveness., (© 2024 The Authors. CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

Spontaneous transient states were recently identified by functional magnetic resonance imaging and magnetoencephalography in healthy subjects. They organize and coordinate neural activity in brain networks. How spontaneous transient states are altered in abnormal brain conditions is unknown. Here, we conducted a transient state analysis on resting-state electroencephalography (EEG) source space and developed a state transfer analysis to patients with disorders of consciousness (DOC). They uncovered different neural coordination patterns, including spatial power patterns, temporal dynamics, spectral shifts, and connectivity construction varies at potentially very fast (millisecond) time scales, in groups with different consciousness levels: healthy subjects, patients in minimally conscious state (MCS), and patients with vegetative state/unresponsive wakefulness syndrome (VS/UWS). Machine learning based on transient state features reveal high classification accuracy between MCS and VS/UWS. This study developed methodology of transient states analysis on EEG source space and abnormal brain conditions. Findings correlate spontaneous transient states with human consciousness and suggest potential roles of transient states in brain disease assessment., Competing Interests: Declaration of Competing Interest The other authors declare that they have no conflicts of interest., (Copyright © 2021. Published by Elsevier Inc.)

Objective: The objective of this study was to explore the role for quadratic phase coupling within electroencephalography (EEG) oscillations in the diagnosis of consciousness and consciousness restoration for disorders of consciousness (DOC)., Methods: Fifty-one DOC patients were enrolled in this study. For each patient, a Coma Recovery Scale-Revised (CRS-R) score and 20-min resting-state EEG were recorded. Consciousness recovery was assessed with a CRS-R score at a three-month follow-up. Twenty healthy subjects were included as controls. General harmonic wavelet transform-based bicoherence was used to quantify the quadratic phase coupling characteristics of the EEG oscillations., Results: Quadratic phase self-coupling (QPSC) at the delta (QPSC_delta), theta (QPSC_theta) and alpha (QPSC_alpha) bands were closely correlated with patient CRS-R scores. Particularly, the QPSC_theta value could significantly differentiate between vegetative state (VS) patients, minimally conscious state (MCS) patients and healthy control subjects. As compared to VS patients, patients with MCS had a lower QPSC_theta value on the left as well as a higher QPSC_alpha value in right frontal regions. The frontal QPSC_theta value showed significant differences between recovered and unrecovered patients., Conclusion: QPSC characteristics could differentiate between consciousness states and show a predictive ability for the recovery of consciousness in DOC patients., Significance: Changes in QPSC accompany consciousness injury and restoration in DOC patients. A QPSC assessment is helpful in the diagnosis and prognosis of DOC patients., (Copyright © 2019. Published by Elsevier B.V.)

In recent decades, event-related potentials have been used for the clinical electrophysiological assessment of patients with disorders of consciousness (DOCs). In this paper, an oddball paradigm with two types of frequency-deviant stimulus (standard stimuli were pure tones of 1000 Hz; small deviant stimuli were pure tones of 1050 Hz; large deviant stimuli were pure tones of 1200 Hz) was applied to elicit mismatch negativity (MMN) in 30 patients with DOCs diagnosed using the JFK Coma Recovery Scale-Revised (CRS-R). The results showed that the peak amplitudes of MMN elicited by both large and small deviant stimuli were significantly different from baseline. In terms of the spatial properties of MMN, a significant interaction effect between conditions (small and large deviant stimuli) and electrode nodes was centered at the frontocentral area. Furthermore, correlation coefficients were calculated between MMN amplitudes and CRS-R scores for each electrode among all participants to generate topographic maps. Meanwhile, a significant negative correlation between the MMN amplitudes elicited by large deviant stimuli and the CRS-R scores was also found at the frontocentral area. In consequence, our results combine the above spatial properties of MMN in patients with DOCs, and provide a more precise location (frontocentral area) at which to evaluate the correlation between clinical electrophysiological assessment and the level of consciousness.

Objectives: This study investigated the prognostic effect of electroencephalography (EEG) instant effects of single spinal cord stimulation (SCS) on clinical outcome in disorders of consciousness (DOC) and the time‐dependent brain response during the recovery of consciousness prompted by SCS. Methods: Twenty three patients with DOC underwent short‐term SCS (stSCS) implantation operation. Then, all patients received the postoperative EEG test including EEG record before (T1) and after (T2) single SCS session. Subsequently, 2 weeks stSCS treatment was performed and revised coma recovery scale (CRS‐R) and EEG data were collected. Finally, they were classified into effective and ineffective groups at 3‐month follow‐up (T6). Results: The parietal‐occipital (PO) connectivity and clustering coefficients (CC) in the beta band of the effective group at the 1 week after the treatment (T5) were found to be higher than preoperative assessment (T0). Correlation analysis showed that the change in beta CC at T1/T2 was correlated with the change in CRS‐R at T0/T6. In addition, the change in PO connectivity and CC in the beta at T0/T5 were also correlated with the change in CRS‐R at T0/T5. Conclusion: SCS may facilitate the recovery of consciousness by enhancing local information interaction in posterior brain regions. And the recovery can be predicted by beta CC in the EEG test. [ABSTRACT FROM AUTHOR]

Objective: To compare the EEG changes in vegetative state (VS) patients and non-craniotomy, non-vegetative state (NVS) patients during general anesthesia with low-dose propofol and to find whether it affects the arousal rate of VS patients. Methods: Seven vegetative state patients (VS group: five with traumatic brain injury, two with ischemic–hypoxic VS) and five non-craniotomy, non-vegetative state patients (NVS group) treated in the Department of Neurosurgery, Peking University International Hospital from January to May 2022 were selected. All patients were induced with 0.5 mg/kg propofol, and the Bispectral Index (BIS) changes within 5 min after administration were observed. Raw EEG signals and perioperative EEG signals were collected and analyzed using EEGLAB in the MATLAB software environment, time–frequency spectrums were calculated, and EEG changes were analyzed using power spectrums. Results: There was no significant difference in the general data before surgery between the two groups (p > 0.05); the BIS reduction in the VS group was significantly greater than that in the NVS group at 1 min, 2 min, 3 min, 4 min, and 5 min after 0.5 mg/kg propofol induction (p < 0.05). Time–frequency spectrum analysis showed the following: prominent α band energy around 10 Hz and decreased high-frequency energy in the NVS group, decreased high-frequency energy and main energy concentrated below 10 Hz in traumatic brain injury VS patients, higher energy in the 10–20 Hz band in ischemic–hypoxic VS patients. The power spectrum showed that the brain electrical energy of the NVS group was weakened R5 min after anesthesia induction compared with 5 min before induction, mainly concentrated in the small wave peak after 10 Hz, i.e., the α band peak; the energy of traumatic brain injury VS patients was weakened after anesthesia induction, but no α band peak appeared; and in ischemic–hypoxic VS patients, there was no significant change in low-frequency energy after anesthesia induction, high-frequency energy was significantly weakened, and a clear α band peak appeared slightly after 10 Hz. Three months after the operation, follow-up visits were made to the VS group patients who had undergone SCS surgery. One patient with traumatic brain injury VS was diagnosed with MCS-, one patient with ischemic–hypoxic VS had increased their CRS-R score by 1 point, and the remaining five patients had no change in their CRS scores. Conclusions: Low doses of propofol cause great differences in the EEG of different types of VS patients, which may be the unique response of damaged nerve cell residual function to propofol, and these weak responses may also be the basis of brain recovery [ABSTRACT FROM AUTHOR]

Background and objective: Electroencephalography (EEG) and neuroimaging measurements have been highly encouraged to be applied in clinics of disorders of consciousness (DOC) to improve consciousness detection. We tested the relationships between neural complexity measured on EEG and residual consciousness levels in DOC patients. Methods: Resting-state EEG was recorded from twenty-five patients with DOC. Lempel–Ziv complexity (LZC) and permutation Lempel–Ziv complexity (PLZC) were measured on the EEG, and their relationships were analyzed with the consciousness levels of the patients. Results: PLZC and LZC values significantly distinguished patients with a minimally conscious state (MCS), vegetative state/unresponsive wakefulness syndrome (VS/UWS), and healthy controls. PLZC was significantly correlated with the Coma Recovery Scale-Revised (CRS-R) scores of DOC patients in the global brain, particularly in electrodes locating in the anterior and posterior brain regions. Patients with higher CRS-R scores showed higher PLZC values. The significant difference in PLZC values between MCS and VS/UWS was mainly located in the bilateral frontal and right hemisphere regions. Conclusion: Neural complexity measured on EEG correlates with residual consciousness levels of DOC patients. PLZC showed higher sensitivity than LZC in the classification of consciousness levels. [ABSTRACT FROM AUTHOR]

Aim: 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. Methods: 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. Results: 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. Conclusion: 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. [ABSTRACT FROM AUTHOR]

Objective: Diagnosis and prognosis of patients with disorders of consciousness (DOC) is a challenge for neuroscience and clinical practice. Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) is an effective tool to measure the level of consciousness. However, a scientific and accurate method to quantify TMS-evoked activity is still lacking. This study applied fast perturbational complexity index (PCIst) to the diagnosis and prognosis of DOC patients. Methods: TMS-EEG data of 30 normal healthy participants (NOR) and 181 DOC patients were collected. The PCIst was used to assess the time-space complexity of TMS-evoked potentials (TEP). We selected parameters of PCIst in terms of data length, data delay, sampling rate and frequency band. In addition, we collected Coma Recovery Scale–Revised (CRS-R) values for 114 DOC patients after one year. Finally, we trained the classification and regression model. Results: 1) PCIst shows the differences among NOR, minimally consciousness state (MCS) and unresponsive wakefulness syndrome (UWS) and has low computational cost. 2) Optimal parameters of data length and delay after TMS are 300 ms and 101–300 ms. Significant differences of PCIst at 5–8 Hz and 9–12 Hz bands are found among NOR, MCS and UWS groups. PCIst still works when TEP is down-sampled to 250 Hz. 3) PCIst at 9–12 Hz shows the highest performance in diagnosis and prognosis of DOC. Conclusions: This study confirms that PCIst can quantify the level of consciousness. PCIst is a potential measure for the diagnosis and prognosis of DOC patients. [ABSTRACT FROM AUTHOR]

Objective: Constructing a framework to evaluate consciousness is an important issue in neuroscience research and clinical practice. However, there is still no systematic framework for quantifying altered consciousness along the dimensions of both level and content. This study builds a framework to differentiate the following states: coma, general anesthesia, minimally conscious state (MCS), and normal wakefulness. Methods: This study analyzed electroencephalography (EEG) recorded from frontal channels in patients with disorders of consciousness (either coma or MCS), patients under general anesthesia, and healthy participants in normal waking consciousness (NWC). Four non-linear methods—permutation entropy (PE), sample entropy (SampEn), permutation Lempel-Ziv complexity (PLZC), and detrended fluctuation analysis (DFA)—as well as relative power (RP), extracted features from the EEG recordings. A genetic algorithm-based support vector machine (GA-SVM) classified the states of consciousness based on the extracted features. A multivariable linear regression model then built EEG indices for level and content of consciousness. Results: The PE differentiated all four states of consciousness (p<0.001). Altered contents of consciousness for NWC, MCS, coma, and general anesthesia were best differentiated by the SampEn, and PLZC. In contrast, the levels of consciousness for these four states were best differentiated by RP of Gamma and PE. A multi-dimensional index, combined with the GA-SVM, showed that the integration of PE, PLZC, SampEn, and DFA had the highest classification accuracy (92.3%). The GA-SVM was better than random forest and neural networks at differentiating these four states. The ‘coordinate value’ in the dimensions of level and content were constructed by the multivariable linear regression model and the non-linear measures PE, PLZC, SampEn, and DFA. Conclusions: Multi-dimensional measurements, especially the PE, SampEn, PLZC, and DFA, when combined with GA-SVM, are promising methods for constructing a framework to quantify consciousness. [ABSTRACT FROM AUTHOR]

Transcranial direct current stimulation (tDCS) recently was shown to benefit rehabilitation of patients with disorders of consciousness (DOC). However, high-Definition tDCS (HD-tDCS) has not been applied in DOC. In this study, we tried to use HD-tDCS protocol (2 mA, 20 min, the precuneus, and sustaining 14 days) to rehabilitate 11 patients with DOC. Electroencephalography (EEG) and Coma Recovery Scale–Revised (CRS-R) scores were recorded at before (T0), after a single session (T1), after 7 days' (T2), and 14 days' HD-tDCS (T3) to assess the modulation effects. EEG coherence was measured to evaluate functional connectivity during the experiment. It showed that 9 patients' scores increased compared with the baseline. The central-parietal coherence significantly decreased in the delta band in patients with DOC. EEG coherence might be useful for assessing the effect of HD-tDCS in patients with DOC. Long-lasting HD-tDCS over the precuneus is promising for the treatment of patients with DOC. [ABSTRACT FROM AUTHOR]

• The results suggest that SST is a robust analytical method and outperforms HHT and MWT in studying TMS-evoked oscillations. • The main frequency of TMS-evoked oscillations of DOC was lower than that of NOR. Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) has become a powerful tool to assess cortical properties, such as cortical oscillation. An accurate and robust time–frequency analysis tool with high resolution would facilitate the understanding of TMS-evoked oscillations. The synchrosqueezing transform (SST), the Hilbert-Huang transform (HHT) and the Morlet wavelet transform (MWT) were used to analyze TMS-evoked oscillations. Firstly, we generated simulation data, and compared the performance of three methods to analyze the time–frequency characteristics of simulation data; then, we collected TMS-EEG data from normal people (NOR), patients with minimally conscious state (MCS) and vegetative state (VS). SST was used to analysis time–frequency characteristics of TMS-evoked oscillations in different states of consciousness. In addition, relative power (RP) and spectral entropy (SeEn) were calculated based on SST results. Simulation results showed that SST detected rhythm characteristics of instantaneous signal and background signal more completely. Results of NOR group showed that SST could more accurately detect TMS-evoked oscillations with high frequency resolution than HHT and MWT. The main frequency of TMS-evoked oscillations for DOC patients (MCS: 11.73 ± 1.94 Hz; VS: 2.06 ± 0.25 Hz) was different from that of NOR (22.79 ± 1.42 Hz) based on SST. RP and SeEn further verified the differences of the main frequency of TMS-evoked oscillations between NOR and DOC patients. The results suggest that SST is a robust analytical method and outperforms HHT and MWT in studying TMS-evoked oscillations. The main frequency of TMS-evoked oscillations of DOC was lower than that of NOR. [ABSTRACT FROM AUTHOR]

Spinal cord stimulation (SCS) has been suggested as a therapeutic technique for treating patients with disorder of consciousness (DOC). Although studies have reported its benefits for patients, the underlying pathophysiological mechanisms remain unclear. The aim of this study was to measure the effects of SCS on the EEG of patients in a minimally conscious state (MCS), which would allow us to explore the possible workings underpinning of the approach. Resting state EEG was recorded before and immediately after SCS, using various frequencies (5 Hz, 20 Hz, 50 Hz, 70 Hz and 100 Hz), for 11 patients in MCS. Relative power, coherence, S-estimator and bicoherence were calculated to assess the EEG changes. Five frequency bands (delta, theta, alpha, beta and gamma) and three regions (frontal, central and posterior) were divided in the calculation. The main findings of this study were that: (1) significantly altered relative power and synchronisation was found in delta and gamma bands after one SCS stimulation using 5 Hz, 70 Hz or 100 Hz; (2) bicoherence showed that coupling within delta was significantly decreased after stimulation using 70 Hz, while reduction of coupling between delta and gamma was found when using 5 Hz and 100 Hz. However, SCS of 20 Hz, 50 Hz and sham stimulation did not induce changes in any frequency band at any region. This study showed EEG evidence that SCS can modulate the brain function of MCS patients, speculatively by activating the formation-thalamus-cortex network. [ABSTRACT FROM AUTHOR]