Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness

 Probably important for your doctor to know about this. 

I'd suggest you call up the hospital president and ask if they have a research analyst whose only job is to monitor and implement stroke research. If they don't have one, YOU DON'T HAVE A FUNCTIONING STROKE HOSPITAL! RUN AWAY!

Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness

Fang Yu¹ Yanzhe Gao² Fenglian Li^1* Xueying Zhang^1* Fengyun Hu^3* Wenhui Jia³ Xiaohui Li¹

• ¹College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, China
• ²College of Life Sciences, Nankai University, Tianjin, China
• ³The Fifth Clinical Medical College of Shanxi Medical University, Department of Neurology, Shanxi Provincial People’s Hospital, Taiyuan, China

Introduction: Ischemic stroke patients commonly experience disorder of consciousness (DOC), leading to poorer discharge outcomes and higher mortality risks. Therefore, the identification of applicable electrophysiological biomarkers is crucial for the rapid diagnosis and evaluation of post-stroke disorder of consciousness (PS-DOC), while providing supportive evidence for cerebral neurology.

Methods: In our study, we conduct microstate analysis on resting-state electroencephalography (EEG) of 28 post-stroke patients with awake consciousness and 28 patients with PS-DOC, calculating the temporal features of microstates. Furthermore, we extract the Lempel-Ziv complexity of microstate sequences and the delta/alpha power ratio of EEG on spectral. Statistical analysis is performed to examine the distinctions in features between the two groups, followed by inputting the distinctive features into a support vector machine for the classification of PS-DOC.

Results: Both groups obtain four optimal topographies of EEG microstates, but notable distinctions are observed in microstate C. Within the PS-DOC group, there is a significant increase in the mean duration and coverage of microstates B and C, whereas microstate D displays a contrasting trend. Additionally, noteworthy variations are found in the delta/alpha ratio and Lempel-Ziv complexity between the two groups. The integration of the delta/alpha ratio with microstates’ temporal and Lempel-Ziv complexity features demonstrates the highest performance in the classifier (Accuracy = 91.07%).

Discussion: Our results suggest that EEG microstates can provide insights into the abnormal brain network dynamics in DOC patients post-stroke. Integrating the temporal and Lempel-Ziv complexity microstate features with spectral features offers a deeper understanding of the neuro mechanisms underlying brain damage in patients with DOC, holding promise as effective electrophysiological biomarkers for diagnosing PS-DOC.

1. Introduction

Stroke is widely acknowledged as the second leading cause of death and a prominent contributor to disability globally (Feigin et al., 2017, 2021), which results in severe behavioral impairments and widespread structural and functional network disruptions (Alkhachroum et al., 2022). Post-stroke, patients commonly experience symptoms such as disorder of consciousness (DOC) or coma, which contribute to increasing in-hospital mortality and unfavorable outcomes upon discharge for stroke patients (Li et al., 2016). Therefore, it is crucial to diagnose post-stroke disorder of consciousness (PS-DOC) promptly and accurately, while gaining a comprehensive understanding of the neural mechanisms underlying brain injury. Traditionally, clinical rating scales like the Glasgow Coma Scale (GCS) and Coma Recovery Scale Revision (CRS-R) have been used to assess patients with DOC. Although clinical behavioral assessment remains the gold standard (Hermann et al., 2020), these scoring systems exhibit high inter-rater and inter-examiner variability and lack objective evidence of central nervous system damage following brain injury (Claassen et al., 2016; Giacino et al., 2018; Song et al., 2018).

Currently, the utilization of electrophysiological methods, specifically electroencephalography (EEG), to measure neurological function in patients has been demonstrated as an effective method for rapidly assisting in the diagnosis of DOC (Bai et al., 2021b; Ballanti et al., 2022; Duszyk-Bogorodzka et al., 2022). Extensive research utilizing EEG-based spectral analysis, source imaging analysis, and graph theory analysis has improved neurophysiological assessments in the fields of stroke and DOC rehabilitation and diagnosis (Finnigan et al., 2016; Bai et al., 2021a; Zhuang et al., 2022; Bouchereau et al., 2023; Colombo et al., 2023). Regarding spectral patterns, previous studies have documented a notable reduction in alpha power after brain injury, including stroke (Edlow et al., 2021). Consequently, stroke leads to a marked elevation in the delta/alpha ratio (DAR), which quantifies the ratio of delta band power to alpha band power (Finnigan and van Putten, 2013). Likewise, distinguishing between patients with DOC and healthy controls often relies on the analysis of delta and alpha frequency bands, where increased delta rhythms and diminished alpha rhythms serve as prominent indicators of reduced consciousness levels (Rossi Sebastiano et al., 2015). Specifically, DOC patients demonstrate higher delta power than healthy controls (Sitt et al., 2014), while an augmentation in alpha power is observed during the recovery of consciousness in these individuals (Stefan et al., 2018). These studies seem to suggest that we can observe the relationship between the spectral feature DAR and reduced consciousness in patients with PS-DOC.

Although traditional spectral analysis of resting-state EEG integrates brain activity over several seconds in different frequency bands, this method fails to capture the spatial and temporal characteristics of resting-state brain networks occurring at shorter time scales (e.g., tens of milliseconds; Li et al., 2022). In contrast, multi-channel fusion of EEG microstate analysis can capture the spatiotemporal dynamics of activity in different brain regions at a sub-second time scale (Bréchet et al., 2019). Microstates represent specific topological patterns of electrical potentials and are typically classified into four distinct classes (Michel and Koenig, 2018), and the microstates persist for a transient period of approximately 60–120 ms in a quasi-stable state before rapidly transitioning to another microstate category (Lehmann et al., 1987). The swift transitions between microstates reflect rapid changes in brain dynamics, revealing the interconnectedness between cognitive function, information processing, and neural regulation in the brain (Khanna et al., 2014; Von Wegner et al., 2018; Liu et al., 2020). Furthermore, different microstate classes exhibit strong associations with specific resting-state networks (RSNs) in the brain, including the auditory network, visual network, salience network, and attention network, among others (Britz et al., 2010; Michel and Koenig, 2018). Increasing evidence suggests that abnormal alterations in temporal characteristics (such as mean duration, coverage, and occurrence) of microstates are observed in various neuropsychiatric disorders, including post-traumatic stress disorder (Terpou et al., 2022), schizophrenia (Rieger et al., 2016; Lin et al., 2022), Alzheimer’s disease (Tait et al., 2020), Parkinson’s disease (Pal et al., 2021), and depression (Zhao et al., 2022). However, microstate analysis research related to DOC primarily focuses on patients with diverse etiologies, including brain trauma, intracranial bleeding, hypoxic–ischemic, and other conditions (Guo et al., 2022; Toplutaş et al., 2023; Zhang et al., 2023). In contrast, there is limited research on microstate analysis in DOC patients with a single etiology, such as ischemic stroke, and our understanding of the temporal dynamics and spatiotemporal interaction effects in their brains remains insufficient.

Moreover, substantial evidence suggests that microstate time sequences display dynamic and nonlinear characteristics, including non-Markovian transition behaviors, where the transition to the next microstate class is independent of the current microstate class (Gschwind et al., 2015; Von Wegner et al., 2017). Increasing studies have introduced nonlinear measures applied to microstate sequences. In particular, Tait et al. pioneered the utilization of the Lempel-Ziv complexity (LZC) algorithm to investigate microstate transition patterns (Tait et al., 2020), revealing a reduction in microstate LZC among individuals with Alzheimer’s disease in comparison to their healthy counterparts. Subsequently, Zhang et al. explored the alterations in the LZC of microstate sequences in patients with brain diseases (Zhang et al., 2021), and Zhao et al. discovered an increase in the LZC of microstates in adolescents with depression (Zhao et al., 2022). Nonlinear analysis of EEG microstate sequences quantifies the persistent characteristics of brain electrical activity, revealing complex dynamic changes at very small time scales (Von Wegner et al., 2023). We suggest that the microstate LZC in PS-DOC patients may also exhibit some degree of abnormality, providing new insights into the neuro anomalies associated with DOC.

The aforementioned analysis indicates that the current understanding of EEG microstates in PS-DOC remains limited. To this end, the innovations and contributions of our study are summarized as follows.

Firstly, to the best of our knowledge, this paper is the first work to investigate the differences in EEG microstates between PS-DOC patients and post-stroke awake (PS-AW) conscious state patients. Comparison results show that there exist differences in microstate topographies between the two groups and especially exhibit significant alterations in temporal features among them.

Secondly, we analyze the Lempel-Ziv complexity of the microstate time sequences and find that, there exhibits higher repetitiveness and slower transition trends in the microstates of PS-DOC patients than that of PS-AW patients. Additionally, to supplement the spectral information in resting-state EEG, we calculate the DAR of spectral features in both groups. We find that DAR is significantly higher in PS-DOC patients.

Finally, we explore the potential of the aforementioned extracted features that are sensitive to intergroup variability in the classification of DOC. In particular, we fuse these features and feed the combined sets into an SVM classifier to identify the DOC among stroke patients. The outcome demonstrates that our work could accurately identify 92.86% of DOC patients.

In summary, our study contributes to a better understanding of resting-state EEG microstate features in patients with DOC post-stroke, helps us to identify potentially valid electrophysiological biomarkers, and provides important insights and neurological evidence into the causative mechanisms of decreased levels of consciousness post-stroke.

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