Outcome Prediction in Unresponsive Wakefulness Syndrome and Minimally Conscious State by Non-linear Dynamic Analysis of the EEG

Effects of different frequencies music on cortical responses and functional connectivity in patients with minimal conscious state

The objective of this study was to investigate brain activation and functional network patterns during musical interventions in different frequency bands using functional near-infrared spectroscopy, and to provide a basis for more effective music therapy strategy selection for patients in minimally conscious state (MCS). Twenty six MCS patients and 20 healthy people were given music intervention with low frequency (31-180 Hz), medium frequency (180-4k Hz), and high frequency (4k-22k Hz) audio. In MCS patients, low frequency music intervention induced activation of left prefrontal cortex and left primary sensory cortex (S1), also a left-hemisphere lateralization effect of dorsolateral prefrontal cortex (DLPFC). And the functional connectivity of right DLPFC-right S1 was significantly improved by high frequency music intervention. The low frequency and high frequency music may contribute more than medium frequency music to the recovery of consciousness. This study also validated the effectiveness of fNIRS in studies of brain function in MCS patients.

A Potential Prognosis Indicator Based on P300 Brain-Computer Interface for Patients with Disorder of Consciousness

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.

tDCS-EEG for Predicting Outcome in Patients With Unresponsive Wakefulness Syndrome

Objectives

We aimed to assess the role of transcranial direct current stimulation (tDCS) combined with electroencephalogram (EEG) for predicting prognosis in UWS cases.

Methods

This was a historical control study that enrolled 85 patients with UWS. The subjects were assigned to the control (without tDCS) and tDCS groups. Conventional treatments were implemented in both the control and tDCS groups, along with 40 multi-target tDCS sessions only in the tDCS group. Coma Recovery Scale-Revised (CRS-R) was applied at admission. The non-linear EEG index was evaluated after treatment. The modified Glasgow Outcome Scale (mGOS) was applied 12 months after disease onset.

Results

The mGOS improvement rate in the tDCS group (37.1%) was higher than the control value (22.0%). Linear regression analysis revealed that the local and remote cortical networks under unaffected pain stimulation conditions and the remote cortical network under affected pain stimulation conditions were the main relevant factors for mGOS improvement. Furthermore, the difference in prefrontal-parietal cortical network was used to examine the sensitivity of prognostic assessment in UWS patients. The results showed that prognostic sensitivity could be increased from 54.5% (control group) to 84.6% (tDCS group).

Conclusions

This study proposes a tDCS-EEG protocol for predicting the prognosis of UWS. With multi-target tDCS combined with EEG, the sensitivity of prognostic assessment in patients with UWS was improved. The recovery might be related to improved prefrontal-parietal cortical networks of the unaffected hemisphere.

Entropy Metrics Correlating with Higher Residual Functioning in Patients with Chronic Disorders of Consciousness

To test the ability of different entropy measures to classify patients with different conditions of chronic disorder of consciousness, we applied the Lempel–Ziv complexity, the amplitude coalition entropy (ACE), and the synchrony coalition entropy (SCE) to the EEG signals recorded in 32 patients, clinically evaluated using the coma recovery scale revised (CRS-R). All the entropy measures indicated that differences found in the theta and alpha bands can distinguish patients in a minimal consciousness state (MCS) with respect to those in a vegetative state/unresponsive wakefulness state (VS/UWS). These differences were significant comparing the entropy measure performed on the anterior region of the left hemisphere and midline region. The values of theta-alpha entropy positively correlated with those of the CRS-R scores. Among the entropy measures, ACE most often highlighted significant differences. The higher values found in MCS were for the less impaired patients, according to their CRS-R, suggest that the preservation of signal entropy on the anterior region of the dominant hemisphere correlates with better preservation of consciousness, even in chronic conditions.

Frontal and parietal lobes play crucial roles in understanding the disorder of consciousness: A perspective from electroencephalogram studies

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.