Functional connectivity across large-scale networks is crucial for the regulation of conscious states. Nonetheless, our understanding of potential alterations in the temporal dynamics of dynamic functional connectivity (dFC) among patients with disorders of consciousness (DOC) remains limited. The present study aimed to examine different time-scale spatiotemporal dynamics of electroencephalogram oscillation amplitudes recorded in different consciousness states. Resting-state electroencephalograms were collected from a cohort of 90 patients with DOC. The sliding window approach was used to create dFC matrices, which were subsequently subjected to k-means clustering to identify distinct states. Finally, we performed state analysis and developed a decoding model to predict consciousness. There was significantly lower dFC within the forebrain network in patients with unresponsive wakefulness syndrome than in those with a minimally conscious state. Moreover, there were significant differences in temporal properties, mean dwell time, and the number of transitions in the high-frequency band at different time scales between the unresponsive wakefulness syndrome and minimally conscious state groups. Using the multi-band and multi-range temporal dynamics of dFC approach, satisfactory classification accuracy (approximately 83.3%) was achieved. Loss of consciousness is accompanied by an imbalance of complex dynamics within the brain. Both transitions between states at short and medium time scales in high-frequency bands and the forebrain are important in consciousness recovery. Together, our findings contribute to a better understanding of brain network alterations in patients with DOC.
Abstract Introduction Combining transcranial magnetic stimulation with electroencephalography (TMS‐EEG), oscillatory reactivity can be measured, allowing us to investigate the interaction between local and distant cortical oscillations. However, the extent to which human consciousness is related to these oscillatory effective networks has yet to be explored. Aims We tend to investigate the link between oscillatory effective networks and brain consciousness, by monitoring the global transmission of TMS‐induced oscillations in disorders of consciousness (DOC). Results A cohort of DOC patients was included in this study, which included 28 patients with a minimally conscious state (MCS) and 20 patients with vegetative state/unresponsive wakefulness syndrome (VS/UWS). Additionally, 25 healthy controls were enrolled. The oscillatory reactivity to single‐pulse TMS of the frontal, sensorimotor and parietal cortex was measured using event‐related spectral perturbation of TMS‐EEG. The temporal–spatial properties of the oscillatory reactivity were illustrated through life time, decay gradients and accumulative power. In DOC patients, an oscillatory reactivity was observed to be temporally and spatially suppressed. TMS‐EEG of DOC patients showed that the oscillations did not travel as far in healthy controls, in terms of both temporal and spatial dimensions. Moreover, cortical theta reactivity was found to be a reliable indicator in distinguishing DOC versus healthy controls when TMS of the parietal region and in distinguishing MCS versus VS/UWS when TMS of the frontal region. Additionally, a positive correlation was observed between the Coma Recovery Scale‐Revised scores of the DOC patients and the cortical theta reactivity. Conclusions The findings revealed a breakdown of oscillatory effective networks in DOC patients, which has implications for the use of TMS‐EEG in DOC evaluation and offers a neural oscillation viewpoint on the neurological basis of human consciousness.
Objective This study aimed to explore the electroencephalogram (EEG) indicators and clinical factors that may lead to poor prognosis in patients with prolonged disorder of consciousness (pDOC), and establish and verify a clinical predictive model based on these factors. Methods This study included 134 patients suffering from prolonged disorder of consciousness enrolled in our department of neurosurgery. We collected the data of sex, age, etiology, coma recovery scales (CRS-R) score, complications, blood routine, liver function, coagulation and other laboratory tests, resting EEG data and follow-up after discharge. These patients were divided into two groups: training set ( n = 107) and verification set ( n = 27). These patients were divided into a training set of 107 and a validation set of 27 for this study. Univariate and multivariate regression analysis were used to determine the factors affecting the poor prognosis of pDOC and to establish nomogram model. We use the receiver operating characteristic (ROC) and calibration curves to quantitatively test the effectiveness of the training set and the verification set. In order to further verify the clinical practical value of the model, we use decision curve analysis (DCA) to evaluate the model. Result The results from univariate and multivariate logistic regression analyses suggested that an increased frequency of occurrence microstate A, reduced CRS-R scores at the time of admission, the presence of episodes associated with paroxysmal sympathetic hyperactivity (PSH), and decreased fibrinogen levels all function as independent prognostic factors. These factors were used to construct the nomogram. The training and verification sets had areas under the curve of 0.854 and 0.920, respectively. Calibration curves and DCA demonstrated good model performance and significant clinical benefits in both sets. Conclusion This study is based on the use of clinically available and low-cost clinical indicators combined with EEG to construct a highly applicable and accurate model for predicting the adverse prognosis of patients with prolonged disorder of consciousness. It provides an objective and reliable tool for clinicians to evaluate the prognosis of prolonged disorder of consciousness, and helps clinicians to provide personalized clinical care and decision-making for patients with prolonged disorder of consciousness and their families.
Recently a positive treatment effect on disorders of consciousness (DOCs) with high-definition transcranial direct-current stimulation (HD-tDCS) has been reported; however, the neural modulation mechanisms of this treatment's efficacy need further investigation. To better understand the processing of HD-tDCS interventions, a long-lasting HD-tDCS protocol was applied to 15 unresponsive wakefulness syndrome (UWS) patients and 20 minimally conscious states (MCS) patients in this study. Ten minutes of resting-state electroencephalograms (EEGs) were recorded from the patients, and the coma recovery scale-revised scores (CRS-Rs) were assessed for each patient from four time-points (T0, T1, T2, and T3). Brain networks were constructed by calculating the EEG spectral connectivity using the debiased weighted phase lag index (dwPLI) and then quantified the network information transmission efficiency by graph theory. We found that there was an increasing trend in local and global information processing of beta and gamma bands in resting-state functional brain networks during the 14 days of HD-tDCS modulation for MCS patients. Furthermore, the increased functional connectivity not only occurred in the local brain area surrounding the stimulation position but was also present across more global brain areas. Our results suggest that long-lasting HD-tDCS on the precuneus may facilitate information processing among neural populations in MCS patients.
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, sustaining 14 days ) to rehabilitate 11 DOC patients. 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 at the delta band in DOC patients. EEG coherence might be useful for assessing the effect of HD-tDCS in DOC patients. Long-lasting HD-tDCS over the precuneus is promising for the treatment of DOC patients.
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.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.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.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.
Objective This study aimed to explore differences in sleep electroencephalogram (EEG) patterns in individuals with prolonged disorders of consciousness, utilizing polysomnography (PSG) to assist in distinguishing between the vegetative state (VS)/unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), thereby reducing misdiagnosis rates and enhancing the quality of medical treatment. Methods A total of 40 patients with prolonged disorders of consciousness (pDOC; 27 patients in the VS/UWS and 13 in the MCS) underwent polysomnography. We analyzed differential EEG indices between VS/UWS and MCS groups and performed correlation analyses between these indices and the Coma Recovery Scale-Revised (CRS-R) scores. The diagnostic accuracy of the differential indices was evaluated using receiver operating characteristic (ROC) curves. Results 1. The fractal dimension (Higuchi’s fractal dimension (HFD)) of patients in the MCS tended to be higher than that of patients in the VS/UWS across all phases, with a significant difference only in the waking phase ( p < 0.05). The HFD in the waking phase was positively correlated with the CRS-R score and exhibited the highest diagnostic accuracy at 88.3%. The Teager–Kaiser energy operator (TKEO) also showed higher levels in patients in the MCS compared to those in the VS/UWS, significantly so in the NREM2 phase ( p < 0.05), with a positive correlation with the CRS-R score and diagnostic accuracy of 75.2%. The δ -band power spectral density [PSD(δ)] in the patients in the MCS was lower than that in those in the VS/UWS, significantly so in the waking phase ( p < 0.05), and it was negatively correlated with the CRS-R score, with diagnostic accuracy of 71.5%. Conclusion Polysomnography for the VS/UWS and MCS revealed significant differences, aiding in distinguishing between the two patient categories and reducing misdiagnosis rates. Notably, the HFD and PSD( δ ) showed significantly better performance during wakefulness compared to sleep, while the TKEO was more prominent in the NREM2 stage. Notably, the HFD exhibited a robust correlation with the CRS-R scores, the highest diagnostic accuracy, and immense promise in the clinical diagnosis of prolonged disorders of consciousness.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)