Eeg Microstate Analysis in Patients With Disorders of Consciousness and Its Clinical Relevance

doi:10.21203/rs.3.rs-2121913/v1

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Eeg Microstate Analysis in Patients With Disorders of Consciousness and Its Clinical Relevance

https://doi.org/10.21203/rs.3.rs-2121913/v1

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published 03 Feb, 2023

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Disorders of Consciousness are divided into categories such as vegetative and minimally conscious states. Objective measures that allow correct identification of vegetative and minimally conscious state patients are required. EEG Microstate analysis is a promising approach that we believe has the potential to be effective in examining the resting state activities of the brain in different stages of consciousness by allowing the proper identification of vegetative and minimally conscious patients. As a result, we try to identify clinical evaluation scales and microstate characteristics with resting state EEGs from individuals with Disorders of Consciousness. Our prospective observational study included thirty individuals with a Disorder of Consciousness. As the control group, we included EEG data from 18 healthy individuals. We made clinical evaluations using patient behavior scales. We also analyzed the EEG data quantitatively and compared statistically using microstate analysis. In our study, microstate D coverage and occurrence differed substantially between vegetative and minimally conscious state patients. We performed microstate analysis on resting-state EEGs of patients with Disorders of Consciousness. There was a strong connection between microstate D characteristics and clinical scale scores. We also found significant relationships between microstate transition probabilities and clinical scale scores. We have shown that microstate D is the most potent parameter representing consciousness. Microstate analysis appears to be a strong option for future use in the diagnosis, follow-up, and treatment response of patients with Disorders of Consciousness.

Disorders of Consciousness

EEG Microstate analysis

minimally conscious state

vegetative state

microstate D

Neurological intensive care and regular medical treatment have greatly improved in recent years. As a result, many people are recovering from significant brain injuries (Wutzl et al., 2021). However, not everyone recovers entirely. Many patients stay in a prolonged coma, defined as the lack of alertness and awareness after the acute condition, with a considerable number of survivors also having impaired consciousness (Xie et al., 2017). Patients suffering from Disorders of Consciousness (DOC); it is divided into three states: coma, vegetative state (VS), and minimally conscious state (MCS) (Giacino et al., 2002). Patients with VS voluntarily open their eyes but do not respond to external stimuli. Patients with MCS show signs of awareness of themselves or their surroundings, albeit this awareness fluctuates (Giacino et al., 2002).

Various measures have been developed throughout the years to categorize DOC patients. The Coma Recovery Scale-Revised (CRS-R) is the most recent and preferable scale for evaluating consciousness in the post-acute period (Giacino et al., 2004). Simplified Evaluation of CONsciousness Disorders (SECONDs) is introduced as a novel quick assessment method that may be used in daily practice to identify the state of consciousness in patients with severe brain injury. Its primary advantages are that it is substantially quicker and simpler to deploy than CRS-R (Aubinet et al., 2021).

The first step in developing a clinical strategy and selecting a treatment is to make a correct diagnosis. The appropriate therapy can only be identified if the correct diagnosis is available. Although it has limitations, behavioral testing is the gold standard for diagnosis and prognosis (Giacino et al., 2009). However, bedside clinical evaluation has challenges; it necessitates knowledge and may be susceptible to the practitioner's subjectivity. As a result, objective metrics that allow correct diagnosis of VS and MCS patients are required (Luauté et al., 2010). The use of various neuroimaging and neurophysiological methods for this objective is a research area.

Quantitative electroencephalography (EEG) uses algorithms to ease the visual evaluation of EEG traces and add objective data to them. Resting-state EEG, when examined quantitatively, eliminates subjective inaccuracies in diagnosis and prognosis, allowing objective clinical evaluation and tracking of therapy response. Visual examination of raw EEG data is less objective than quantitative EEG, and quantitative EEG has been found to give better validation than visual grading (Nuwer, 1997). It has the advantages of being non-invasive, less costly, generally applicable, and bedside measuring over quantitative EEG, functional magnetic resonance imaging, or positron emission tomography, alternative assessment approaches.

Microstate analysis is a spatial-temporal quantitative EEG analysis approach that examines topographic maps of electrical potentials on an electrode array and their temporal evolution. Multi-channel EEG data is fundamentally considered and processed as a series of electrical field topography in this technique (Lehmann et al., 1987; Pascual-Marqui et al., 1995).

Microstate analysis is a promising tool that we believe can be effective in exploring the brain's resting state activity in different stages of awareness, allowing for the accurate diagnosis of VS and MCS patients. Furthermore, such analyses can help us comprehend the significant disparities in the resting state activities of patients with varying degrees of impaired consciousness and identify levels of awareness that behavioral tests cannot detect. As a result, we attempted to compare clinical evaluation scales and microstate characteristics in DOC patients' resting state EEGs using microstate analysis.

Participants

The Non-Interventional Clinical Research Ethics Committee of Istanbul Medipol University approved this study with decision number 711 dated 17.09.2020.

Thirty patients between 18 and 80 who met the criteria for DOC were enrolled in our prospective observational research (Giacino et al., 2018). The relatives of the patients were informed about the study in advance, and their formal agreement was acquired. Prior to the examination, the chronic conditions of the patients and the medications they took were learned. Unstable medical conditions, use of sedative/hypnotic drugs, severe heart, lung, liver, or kidney failure, history of primary psychiatric, a developmental, or neurological disease that may cause known functional disability, uncontrolled epileptic seizures, coma, and patients with a brain death diagnosis were all excluded from the study. As a control group, we used EEG data from 18 healthy people of similar ages and gender to the patient group.

The CRS-R and SECONDs scales were administered to the study participants multiple times during the day at various intervals, and the neurologist provided clinical assessments. To assure the patients' wakefulness prior to EEG recording, we administered verbal and tactile stimulation as recommended on the CRS-R scale (Giacino et al., 2004).

EEG Recordings

At the bedside, EEG was recorded digitally for 20 minutes at a sample rate of 256 Hz from 19 channels positioned according to the 10-20 system using the Neurosoft NEURON-SPECTRUM 5 equipment and Neurosoft neuron-spectrum.net software. For recording, Ag–AgCl ring electrodes were utilized. For a clean EEG recording, electrode impedances were maintained below 10 kΩ.

EEG Preprocessing

To preprocess the EEG data, we employed the EEGLAB (2021.0) toolbox running on the MATLAB (2021b) software. All EEG data from the patient group and 20-minute intervals with awake resting state records from the healthy control group were used for analysis. The data sample rate was set to 128 Hz. The raw data were organized using the mean reference and low and high band-pass filters (2-20Hz suited for microstate analysis) and a 50Hz notch filter. Artifacts were automatically eliminated using Artefact Subspace Reconstruction (Max acceptable 0.5-second window std dev 10) (Chang et al., 2018). Using independent component analysis (ICA), eye movement and muscular artifacts were automatically rectified on the data (Delorme & Makeig, 2004). Following that, any remaining artifacts were manually eliminated. Finally, periods of less than 2 seconds were removed from each EEG data set, and the first 120 seconds of clean sections were chosen for microstate analysis.

Microstate Analyses

We performed microstate analysis using additional compatible software (Version 1.2; http://www.thomaskoenig.ch/Download/EEGLAB_Microstates/), microstate analysis for the EEGLAB toolbox through MATLAB (2021b). We first identified the topographic maps in the momentary peak of Global field power (GFP, at the time points of the optimum signal-to-noise ratio, the maximum voltage values on all electrodes) by the standard procedure and calculated the individual microstate maps for each participant using the k-means clustering method. We have already adjusted the number of clusters to four since previously published four microstate classes described a high amount of EEG data variation in patient groups and healthy patients and terms of comparability to earlier studies. As a result, the clustering method identified each participant's four most prominent cluster maps. We used a permutation approach to minimize the common variance across patients to compute the group model maps individually for each group (VS, MCS, and healthy controls). According to the norm model maps published by Koenig et al., we classified the classes as microstates A-B-C-D (Koenig et al., 2002). We assigned individual microstate maps using the map template for healthy controls from the sorted group model maps. We extracted the following parameters from the microstate data: The total global variation rate explained by microstates, coverage (percentage of total time covered by a microstate class), duration (the average time when a microstate class remains temporarily stable in milliseconds), occurrence (frequency of occurrence of one microstate class), percentage of expected transitions (microstate transitions) calculated to reveal communication between networks. We also used the topographic variance analysis (TANOVA) method to evaluate the topographic differences between groups of microstate classes (A, B, C, D). This approach was implemented using the Ragu app (www.thomaskoenig.ch/Ragu_src.zip).

Statistical analysis

We used the JAMOVI 1.8.4 application to analyze the data statistically. We have defined statistical definitions for categorical variables as a percentage (%, frequency) and, depending on the normal distribution, a median (lowest-highest) or average ± standard deviation for continuous variables. We used visual and analytical approaches to check that the variables had normal dispersion (Kolmogorov - Smirnov / Shapiro - Wilk tests). Using Spearman correlation analysis, we determined microstate characteristics' connection, statistical implications, and clinical evaluation scales. In comparing the independent variables between the two groups, we utilized the Student t test for parameters with normal distribution and the Mann-Whitney U test for parameters with non-normal distribution. When we classified the patient group by clinical evaluation scales and compared different groups, we utilized ANOVA for normal distribution parameters and the Kruskal-Wallis test for non-normal distribution parameters. We performed cross-group comparisons in significant parameters using post-hoc Games-Howell testing. The statistical significance level was set at 0.05.

Demographic data

Two patients were excluded from the study because the EEG data contained heavy artifacts. 12 of the 28 DOC patients in the research had VS/UWS, 11 had MCS-, and 5 had MCS+.

Of the 18 healthy controls we included in the study, 12 were male, and six were female. Of 12 VS/UWS patients, ten were male, and two were female. Of 11 MCS patients, eight were male, and three were female. Four of the 5 MCS+ patients were male, and one was female. There was no gender difference between the groups (p=0.766). The average age; was 57.0±17.3 in VS/UWS patients, 44.6±19.3 in MCS- patients, 58.4±17.4 in MCS+ patients, and 51.7±15.3 in healthy controls. There was no age difference between the groups (p=0.421). According to the duration of the disease, 4 of the VS/UWS patients were acute, two were prolonged, and six were chronic. We had three acute, three prolonged, and five chronic MCS patients. Of the MCS+ patients, one was acute, two were prolonged, and two were chronic. According to disease etiologies, 10 VS/UWS patients were hypoxic brain injury (HBI), and 2 were cerebrovascular disease (CVD). MCS- patients had 5 HBI, 3 CVD, and three traumatic brain injuries (TBI). Of the MCS+ patients, 3 had HBI, 1 had CVD, and 1 had TBI. The mean CRS-R scores of the patients were 5.67±1.83 in the VS/UWS group, 10.4±2.50 in the MCS- group, and 16.6±2.51 in the MCS+ group. The mean SECONDs scores of the patients were 1±0 in the VS/UWS group, 3.73±1.10 in the MCS- group, and 6.8±0.45 in the MCS+ group (Table 1).

Microstate Topographies

When we analyzed the topographies of the microstate classes with the TANOVA method, we could not find a statistically significant difference between the groups (p=0.237). Microstate topographies of healthy controls were similar to those in the literature (14) (Figure 1).

Microstate Parameters

We comparatively examined the microstate parameters between groups (Table 2). The explained variance was significantly higher in the healthy controls than in the consciousness disorders group (p<0.001). There was no significant difference in the variance explained between the patient groups. The mean duration of microstates was higher in DOC patients (p=0.004). The frequency of occurrence of microstates was lower (p=0.008). There was no significant difference between the patient groups regarding the mean duration and frequency of microstates (Figure 2).

When we compared DOC patients with healthy controls, the mean duration of Microstate A was prolonged (p=0.014), and the occurrence was decreased (p=0.022). There was no significant difference in microstate A parameters between the groups.

The mean duration of microstate B was prolonged (p<0.001), and its coverage was increased (p=0.001). There was no significant difference in microstate B parameters between the groups.

The mean duration of microstate C was prolonged (p=0.002), and its coverage increased (p=0.047). There was no significant difference in microstate C parameters between the groups.

The frequency of occurrence of microstate D was decreased (p<0.001), and its coverage was also decreased (p<0.001). Between groups, healthy controls had more microstate D coverage than both MCS patients and VS/UWS patients (p<0.001), and MCS patients' MS D coverage had more than VS/UWS patients (p=0.013). There was no significant difference between MCS+ and MCS- patients.

Transition Probabilities

We also compared the temporal transitions of the intergroup microstate topographies (Table 3). In DOC patients, the expected probability of transition was greater from Microstate A to B (p=0.023) and less from A to D (p<0.001). A higher probability of transition from Microstate B to A (p=0.002), B to C (p<0.001), and C to B (p<0.001) was observed in DOC patients. The probabilities of transitioning from microstate D to A (p<0.001), D to B (p<0.001), and D to C (p<0.001) were higher in healthy controls (Figure 5).

When we compare the groups, The expected transition probabilities from A to D, B to C, C to B, D to A, and D to B were significantly different from healthy controls in the VS/UWS group and the MCS group. When the transition probabilities from A to B, B to A, and D to C were analyzed, we found a significant difference between VS/UWS and MCS patients but not between MCS and healthy controls. When the MCS+ and MCS- patient groups were compared, it was observed that the expected transition probabilities from microstate D to B were similar in the MCS+ group with healthy controls. There was a significant difference between MCS- patients (p=0.020) (Figure 6, 7).

Correlation with Clinical Scales

CRS-R and SECONDs scores were positively correlated with the occurrence and coverage of microstate D. Expected transition probabilities from microstate B to A and from A to B were negatively correlated with SECONDs scores. A positive correlation was found between the probability of transition from microstate C to D and the SECONDs score. There was a positive correlation between the transition probabilities from microstate D to B and D to C and both CRS-R and SECONDs scores (Figures 8, 9, 10).

Our study aimed to investigate the microstate parameters in patients with unconsciousness and to evaluate the relationship of these parameters with the level of consciousness and clinical assessment scales. For this purpose, we compared the microstate analyses of the resting state EEGs of VS, MCS- and MCS + patients and healthy controls. This study is one of the few studies examining the microstate parameters of patients with DOC. In the results of our study, we determined that patients with DOC differed from healthy controls with changes in microstate parameters. In particular, we found that the transition probabilities of microstate D parameters and some microstate maps (A > B, B > A, D > B, C > D, D > C) correlated with consciousness level and clinical assessment scales. We believe that our findings will contribute to the consciousness disorders and microstate analysis literature.

We analyzed the resting-state EEG data of VS, MCS, and healthy controls by separating them into four microstate maps. There was no significant topographic difference between the mean microstate maps of each group. Topographic maps of healthy controls were consistent with the literature (Khanna et al., 2015; Koenig et al., 2002; Michel & Koenig, 2018). This situation is essential in terms of comparability with the literature. While examining the microstate parameters, we used the mean topographic maps of our healthy control data, which formed our normals, as a template in all groups. According to this, In the DOC group, the total explained variance of the four microstate maps was significantly lower, as we expected. The literature has repeatedly reported that the total explained variance rate of the four microstate maps is above 70% in studies performed with healthy controls (Khanna et al., 2015; Koenig et al., 2002; Michel & Koenig, 2018). The decrease in the variance explained in the patient group shows that brain oscillations differ from healthy controls. However, we could not find a significant difference between the MCS and VS groups. Therefore, we think that the explained variance rate is a general parameter in showing consciousness disorders but not a sensitive enough indicator.

In DOC patients, the mean duration of microstates was significantly longer, and their frequency was significantly less. The literature has reported that the mean microstate durations increase and the frequencies decrease with age (Koenig et al., 2002). In a study conducted in 2012, it was determined that the average microstate durations were prolonged during deep sleep (NREM 3) (Brodbeck et al., 2012). In addition, in a recent study, it was reported that the mean microstate durations were prolonged in dementia with Lewy bodies, and this prolongation was negatively correlated with the functional connectivity of the basal ganglia network and the thalamic network; It was emphasized that this situation might be related to the slowdown in mental activities (Schumacher et al., 2019). However, studies conducted in recent years have shown that the average microstate parameters are prolonged in Alzheimer's patients (Tait et al., 2020). The increased mean duration was suggested as a finding reflecting cognitive decline, indicating that the conversion of microstate parameters to each other slowed down (Tait et al., 2020). There was no age difference between our patient and healthy groups. Mean duration and total frequency values did not successfully differentiate VS and MCS. Similar to the variance rate explained, we think these parameters are a general finding showing that the transition between microstates in patients with cognitive impairment slows down. Therefore brain oscillations slow down, but they are not sensitive enough to show the severity of the disease.

In DOC patients, the mean duration of Microstate A was prolonged, and its occurrence decreased. A study conducted in 2018 suggested that the incidence of Microstate A was a finding reflecting the prognosis of DOC patients (Stefan et al., 2018). Microstate A is defined as a network associated with the auditory network. The auditory network is associated with the bilateral superior and middle temporal gyrus, and the left middle frontal gyrus regions show functional connectivity (Britz et al., 2010). The changes we see in the parameters of microstate A may result from these regions being affected in DOC patients. In addition, Microstate A parameters did not distinguish VS-MCS sensitively, and it was not a sensitive parameter in showing the severity of the disease.

We found that the mean duration of microstate B was prolonged, and its coverage increased in DOC patients. Studies report that the duration or coverage of B is increased in patients with Parkinson's and dementia with Lewy bodies, and its duration is decreased in patients with schizophrenia (Chu et al., 2020; Nishida et al., 2013; Schumacher et al., 2019). Microstate B is a network location associated with the visual network. In this network, bilateral occipital areas and related subcortical structures show functional connectivity (Britz et al., 2010). Changes in MS B may indicate that these areas are affected in DOC patients. This parameter was also not found sensitive enough to show the severity of the disease.

In microstate C, similar to MS B, the mean duration was prolonged, and the coverage increased in DOC patients. Microstate C is a map associated with the saliency network. The anterior cingulate cortex, medial cingulate gyrus, left inferior frontal gyrus, left claustrum, right inferior frontal gyrus, and right amygdala show functional connectivity in this defined network (21). The changes we found in this parameter, in addition to reflecting the involvement of these areas, were still not sensitive enough to show the severity of the disease.

In microstate C, similar to MS B, the mean duration was prolonged, and the coverage increased in DOC patients. Microstate C is a map associated with the saliency network. The anterior cingulate cortex, medial cingulate gyrus, left inferior frontal gyrus, left claustrum, right inferior frontal gyrus, and right amygdala show functional connectivity in this defined network (Britz et al., 2010). The changes we found in this parameter, in addition to reflecting the involvement of these areas, were still not sensitive enough to show the severity of the disease.

When we examined the parameters related to the microstate D maps, we found that the coverage and occurrence decreased. These parameters also differed significantly between MCS and VS and correlated with clinical assessment scales. A study conducted in 2018 reported that the coverage of microstate D is the most sensitive parameter in differentiating VS and MCS patients (Stefan et al., 2018). The increasing coverage of microstate D was presented as a parameter indicating the severity of the disease. Surprisingly, in our study, the coverage in microstate D was associated with higher clinical scores, namely increased awareness. This difference may be due to methodological changes, we examined the 2–20 Hz frequency band, but the result found in the literature was only in the alpha frequency band. In a study conducted in 2012 examining the relationship between sleep stage microstates, it was reported that the incidence and frequency of Microstate D decreased as sleep deepened (Brodbeck et al., 2012). Publications show that microstate D's topography and parameters are also affected in Parkinson's disease and Alzheimer's disease (Chu et al., 2020; Ignacio Serrano et al., 2018; Pal et al., 2021; Tait et al., 2020). Microstate D is a map associated with the frontoparietal network, working memory network, and attention network (Britz et al., 2010). In particular, areas lateralized to the right, covering the right upper and middle frontal gyrus and the right upper and lower parietal lobules, show functional connectivity in this network. We think that the frequency and ratio of this map, which is associated with the frontoparietal network, are the most sensitive parameters in distinguishing DOC patients and showing the severity of the disease. Results of previous resting state network studies in DOC patients highlight the coexistence of a common disorder involving the associative cortices such as the midline of the frontoparietal network (i.e., anterior cingulate cortex-mesiofrontal and posterior cingulate cortex associated with internal awareness or self-related processes) and lateral network (i.e., prefrontal and posterior parietal area associated with environmental awareness) (Beuthien-baumann et al., 2003; Juengling et al., 2005; Laureys, Goldman, et al., 1999; Laureys, Lemaire, et al., 1999; Lull et al., 2010; Nakao et al., 2010; Silva et al., 2010). It has been shown that the connection in the midline frontoparietal cortex, also called the default mode network (DMN), reflects the consciousness level of DOC patients (Vanhaudenhuyse et al., 2010). A map reflecting DMN in microstate maps has not been reported by Britz et al.. However, Microstate D is mainly associated with the lateral frontoparietal network; this map we showed in our study to be sensitively affected in DOC patients may also reflect DMN.

Examining the transition probabilities between microstate maps, we found that DOC patients are more likely to prefer transitions over Microstate B and less likely to transition over Microstate D. In particular, we found that A > B, B > A, D > B, D > C, C > D transitions were associated with clinical assessment scales. It is thought that transitions between microstates are not random (Khanna et al., 2015). More work is needed to link these transition possibilities with functional networks. Our study is the first to examine the probabilities of microstate transition in DOC patients and to investigate its relationship with clinical assessment scales.

There are also some limitations of our study. First, we did not use a structural neuroimaging method in our study. The patient groups we examined were very heterogeneous. Different brain regions may be affected secondary to hypoxia, trauma, or cerebrovascular disease. In subsequent studies, structural brain imaging can also be used to provide better homogeneity of the groups. In our study, we examined four microstate topographies in the range of 2–20 Hz, which have been the most studied in the literature, as previously stated. Frequency ranges can be divided into the delta, theta, alpha, and beta frequencies; more detailed sub-analyses can be made, and more than four topographies can be examined; thus, more precise measurements can be made possible. Finally, we did not do a longitudinal follow-up in our study. In future studies, longitudinal follow-up can be carried out, comparing the prognosis of the patients with the microstate parameters, and the sensitivity of the microstate parameters can be investigated in determining the prognosis.

In this study, where we analyzed the resting state EEGs of patients with consciousness disorders, we successfully determined the relationship between the microstate parameters (especially Microstate D) and the severity of the disease. Among the microstates presented by Lehmann as atoms of thought, microstate D was identified as the best parameter reflecting consciousness. Our study is one of the few studies in this field and sheds light on the literature in this respect. Evaluation of parameter changes of Microstate D with repeated EEGs can be a clinical sign that can be used in the patient's follow-up. Microstate analysis seems to be a strong candidate for the future identification, follow-up, and treatment response of patients with Consciousness Disorders. Future studies with longitudinal follow-ups and confirmation of our data are needed.

Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Tables 1 to 3 are available in the Supplementary Files section

No competing interests reported.

TABLE1.png
Table 1. Characteristics of the groups included in the study
TABLE2.png
Table 2. Microstate parameters values of groups
TABLE3.png
Table 3. Expected transition probabilities between groups' microstate maps

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Eeg Microstate Analysis in Patients With Disorders of Consciousness and Its Clinical Relevance

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