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Pipelines of pre-processing, microstate segmentation, microstate characteristics and multivariate pattern analysis. Steps of each procedure are illustrated in boxes.
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Quasi-stable electrical fields in the EEG, called microstates carry information on the dynamics of large scale brain networks. Using machine learning techniques, we explored whether abnormalities in microstates can be used to classify patients with schizophrenia and healthy controls. We applied multivariate pattern analysis of microstate features t...
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... adopted the standard procedure for microstate segmentation from an earlier work ( Koenig et al., 2002), where the modified K-mean clustering algorithm was used (Pascual-Marqui et al., 1995). Here, we focus on the key points of the algorithm (Fig. 1), since a detailed description can be found elsewhere ( Michel et al., ...
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Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligenc...
Citations
... This may be related to resource allocation and switching in the brain during complex tasks [19,21]. When the network structure is compromised, connections between brain network functions are disrupted, impacting the brain's behavioral function [39]. ...
Electroencephalography (EEG) microstates are used to study cognitive processes and brain disease-related changes. However, dysfunctional patterns of microstate dynamics in Alzheimer's disease (AD) remain uncertain. To investigate microstate changes in AD using EEG and assess their association with cognitive function and pathological changes in cerebrospinal fluid (CSF). We enrolled 56 patients with AD and 38 age- and sex-matched healthy controls (HC). All participants underwent various neuropsychological assessments and resting-state EEG recordings. Patients with AD also underwent CSF examinations to assess biomarkers related to the disease. Stepwise regression was used to analyze the relationship between changes in microstate patterns and CSF biomarkers. Receiver operating characteristics analysis was used to assess the potential of these microstate patterns as diagnostic predictors for AD. Compared with HC, patients with AD exhibited longer durations of microstates C and D, along with a decreased occurrence of microstate B. These microstate pattern changes were associated with Stroop Color Word Test and Activities of Daily Living scale scores (all P < 0.05). Mean duration, occurrences of microstate B, and mean occurrence were correlated with CSF Aβ 1–42 levels, while duration of microstate C was correlated with CSF Aβ 1–40 levels in AD (all P < 0.05). EEG microstates are used to predict AD classification with moderate accuracy. Changes in EEG microstate patterns in patients with AD correlate with cognition and disease severity, relate to Aβ deposition, and may be useful predictors for disease classification.
... This effectively reveals the temporal dynamics of SCZ pathology. Leveraging changes in microstate parameters in SCZ patients, scholars have utilized microstates as crucial neural imaging biomarker in the automated identification of schizophrenia (Baradits et al., 2020;Luo et al., 2020;Wang et al., 2021). For instance, Baradits et al. (2020) This suggests the potential of microstates as valuable neural imaging biomarkers for brain disorders, enabling a more effective representation of patients' abnormal states (Kim et al., 2021). ...
... Leveraging changes in microstate parameters in SCZ patients, scholars have utilized microstates as crucial neural imaging biomarker in the automated identification of schizophrenia (Baradits et al., 2020;Luo et al., 2020;Wang et al., 2021). For instance, Baradits et al. (2020) This suggests the potential of microstates as valuable neural imaging biomarkers for brain disorders, enabling a more effective representation of patients' abnormal states (Kim et al., 2021). ...
... The comparison results are shown in Table 4. Compared with the previous EMD decomposition (Siuly et al., 2020), signal energy or frequency analysis methods (Devia et al., 2019;Akbari et al., 2021), the micro-state method adopted in this paper is significantly improved. Compared with the existing microstate analysis techniques (Baradits et al., 2020;Kim et al., 2021), the sensitivity of the microstate sequence to the template is used to greatly enhance the recognition of SCZ. The experimental results fully demonstrate the effectiveness of the proposed indicators, which provides an effective basis for the clinical diagnosis of schizophrenia and the realization of intelligent diagnosis. ...
Introduction
Microstate analysis enables the characterization of quasi-stable scalp potential fields on a sub-second timescale, preserving the temporal dynamics of EEG and spatial information of scalp potential distributions. Owing to its capacity to provide comprehensive pathological insights, it has been widely applied in the investigation of schizophrenia (SCZ). Nevertheless, previous research has primarily concentrated on differences in individual microstate temporal characteristics, neglecting potential distinctions in microstate semantic sequences and not fully considering the issue of the universality of microstate templates between SCZ patients and healthy individuals.
Methods
This study introduced a microstate semantic modeling analysis method aimed at schizophrenia recognition. Firstly, microstate templates corresponding to both SCZ patients and healthy individuals were extracted from resting-state EEG data. The introduction of a dual-template strategy makes a difference in the quality of microstate sequences. Quality features of microstate sequences were then extracted from four dimensions: Correlation, Explanation, Residual, and Dispersion. Subsequently, the concept of microstate semantic features was proposed, decomposing the microstate sequence into continuous sub-sequences. Specific semantic sub-sequences were identified by comparing the time parameters of sub-sequences.
Results
The SCZ recognition test was performed on the public dataset for both the quality features and semantic features of microstate sequences, yielding an impressive accuracy of 97.2%. Furthermore, cross-subject experimental validation was conducted, demonstrating that the method proposed in this paper achieves a recognition rate of 96.4% between different subjects.
Discussion
This research offers valuable insights for the clinical diagnosis of schizophrenia. In the future, further studies will seek to augment the sample size to enhance the effectiveness and reliability of this method.
... In parallel, the analysis of microstates in resting-state EEG has been performed with similar accuracy. The EEG indices might also be disturbed in people at a high risk of psychosis and those in early stages of schizophrenia [108]. Partially, they are related to the transition to psychosis in high-risk individuals and poor functional outcome. ...
The ongoing developments of psychiatric classification systems have largely improved
reliability of diagnosis, including that of schizophrenia. However, with an unknown
pathophysiology and lacking biomarkers its validity still remains low, requiring further
advancements. Research has helped to establish Multiple Sclerosis (MS) as the central nervous system (CNS) disorder with an established pathophysiology, defined biomarkers and therefore good validity and significantly improved treatment options. Before proposing next steps in research that aim to improve the diagnostic process of schizophrenia, it is imperative to recognize its clinical heterogeneity. Indeed, individuals with schizophrenia show high interindividual variability in terms of symptomatic manifestation, response to treatment, course of illness and functional outcomes. There is also a multiplicity of risk factors that contribute to the development of schizophrenia. Moreover, accumulating evidence indicates that several dimensions of psychopathology and risk factors cross current diagnostic categorizations. Schizophrenia shares a number of similarities with MS that is a demyelinating disease of the CNS. These similarities appear in the context of age of onset, geographical distribution, involvement of immune-inflammatory processes, neurocognitive impairment and various trajectories of illness course. This article provides critical appraisal of diagnostic process in schizophrenia, taking into consideration advancements that have been made in the diagnosis and management of MS. Based on the comparison between the two disorders, key directions for studies that aim to improve diagnostic process in schizophrenia are formulated. All of them converge on the necessity to deconstruct the psychosis spectrum, and adopt dimensional approaches with deep phenotyping to refine current diagnostic boundaries.
... Other works rely on the Super Vector Machine (SVM) algorithm. In [62], the SVM algorithm was employed for multivariate pattern analysis of microstate features. The analysis identified three patterns of correlated features, resulting in an accuracy of 82.7% for group separation. ...
Schizophrenia is a chronic and severe mental disorder that affects individuals in various ways, particularly in their ability to perceive, process, and respond to stimuli. This condition has a significant impact on a considerable number of individuals. Consequently, the study, analysis, and characterization of this pathology are of paramount importance. Electroencephalography (EEG) is frequently utilized in the diagnostic assessment of various brain disorders due to its non-intrusiveness, excellent resolution and ease of placement. However, the manual analysis of electroencephalogram (EEG) recordings can be a complex and time-consuming task for healthcare professionals. Therefore, the automated analysis of EEG recordings can help alleviate the burden on doctors and provide valuable insights to support clinical diagnosis. Many studies are working along these lines. In this research paper, the authors propose a machine learning (ML) method based on the eXtreme Gradient Boosting (XGB) algorithm for analyzing EEG signals. The study compares the performance of the proposed XGB-based approach with four other supervised ML systems. According to the results, the proposed XGB-based method demonstrates superior performance, with an AUC value of 0.94 and an accuracy value of 0.94, surpassing the other compared methods. The implemented system exhibits high accuracy and robustness in accurately classifying schizophrenia patients based on EEG recordings. This method holds the potential to be implemented as a valuable complementary tool for clinical use in hospitals, supporting clinicians in their clinical diagnosis of schizophrenia.
... MS time series may correspond to different functional connections (FCs) between RSNs [17]. The characteristics of EEG MSs can be used to quantify the operation of large-scale brain networks, and the transitions of different MS classes reflect the dynamics of brain activity states [18]. Resting-state EEG MSs are thought to reflect local instantaneous states and global interactions of distributed neural networks in the brain [12]. ...
Schizophrenia is a heterogeneous mental disorder with unknown etiology or pathological characteristics. Microstate analysis of the electroencephalogram (EEG) signal has shown significant potential value for clinical research. Importantly, significant changes in microstate-specific parameters have been extensively reported; however, these studies have ignored the information interactions within the microstate network in different stages of schizophrenia. Based on recent findings, since rich information about the functional organization of the brain can be revealed by functional connectivity dynamics, we use the first-order autoregressive model to construct the functional connectivity of intra- and intermicrostate networks to identify information interactions among microstate networks. We demonstrate that, beyond abnormal parameters, disrupted organization of the microstate networks plays a crucial role in different stages of the disease by 128-channel EEG data collected from individuals with first-episode schizophrenia, ultrahigh-risk, familial high-risk, and healthy controls. According to the characteristics of the microstates of patients at different stages, the parameters of microstate class A are reduced, those of class C are increased, and the transitions from intra- to intermicrostate functional connectivity are gradually disrupted. Furthermore, decreased integration of intermicrostate information might lead to cognitive deficits in individuals with schizophrenia and those in high-risk states. Taken together, these findings illustrate that the dynamic functional connectivity of intra- and intermicrostate networks captures more components of disease pathophysiology. Our work sheds new light on the characterization of dynamic functional brain networks based on EEG signals and provides a new interpretation of aberrant brain function in different stages of schizophrenia from the perspective of microstates.
... In contrast, in this project, the number of microstates are selected based on the evaluation of prototype topographies and measures of fitness. The microstate clusters obtained at the individual level are then again clustered to obtain the global microstate maps [19]. Three microstates are observed for both the resting state and NHPT trials EEG. ...
Electroencephalogram(EEG) microstates are brief periods of time during which the brain's electrical activity remains stable. The analysis of EEG microstates can help to identify the background neuronal activity at the millisecond level. The utilization of haptic and robotic technologies can help in evaluating human motor skills. A haptic device Geomagic Touch is used in this study to recreate Nine Hole Peg Test (NHPT) in an embedded reality setup. A preliminary study is conducted to explore changes in neural assemblies related to resting state and fine motor state EEG when fatigue sets in. Five healthy participants are recruited to perform a haptic NHPT under different physical conditions. Three distinct microstates are observed during the resting state and a separate set of 3 states are observed during the NHPT. Changes are assessed by utilising microstate parameters such as occurrence, coverage, duration, and global explained variance. It is found that the coverage of microstate C for resting states decreases for all the participants after the dumbbell exercise. During the fine-motor task, the coverage of microstate MS3 decreases for all participants except one. These results support the involvement of different neural assemblies, but also highlight the potential that physical fatigue can be observed and identified by assessing changes in microstate features, in this case, a parameter such as coverage.
... In a previous classification study using microstates to indicate characteristics, the SVM permitted an 82.7% accuracy in distinguishing schizophrenic patients from those who were healthy when 24 microstate indicators were incorporated (Baradits et al., 2020). Similarly, another study using 128 microstate indicators and secondary indicators for patients with HA and healthy individuals discovered that the SVM could achieve a classification accuracy of 81% . ...
The effects of transcranial magnetic stimulation in treating substance use disorders are gaining attention; however, most existing studies used subjective measures to examine the treatment effects. Objective electroencephalography (EEG)-based microstate analysis is important for measuring the efficacy of transcranial magnetic stimulation in patients with heroin addiction. We investigated dynamic brain activity changes in individuals with heroin addiction after transcranial magnetic stimulation using microstate indicators. Thirty-two patients received intermittent theta-burst stimulation (iTBS) over the left dorsolateral prefrontal cortex. Resting-state EEG data were collected pre-intervention and 10 days post-intervention. The feature values of the significantly different microstate classes were computed using a K-means clustering algorithm. Four EEG microstate classes (A-D) were noted. There were significant increases in the duration, occurrence, and contribution of microstate class A after the iTBS intervention. K-means classification accuracy reached 81.5%. The EEG microstate is an effective improvement indicator in patients with heroin addiction treated with iTBS. Microstates were examined using machine learning; this method effectively classified the pre- and post-intervention cohorts among patients with heroin addiction and healthy individuals. Using EEG microstate to measure heroin addiction and further exploring the effect of iTBS in patients with heroin addiction merit clinical investigation.
... Topographic electrophysiological state source imaging was used to estimate the source of microstates [34]. Today, microstates are widely used to explore the pathological mechanisms of brain diseases [35]. ...
Autism spectrum disorder (ASD) is a heterogeneous disorder that affects several behavioral domains of neurodevelopment. Transcranial direct current stimulation (tDCS) is a new method that modulates motor and cognitive function and may have potential applications in ASD treatment. To identify its potential effects on ASD, differences in electroencephalogram (EEG) microstates were compared between children with typical development (n = 26) and those with ASD (n = 26). Furthermore, children with ASD were divided into a tDCS (experimental) and sham stimulation (control) group, and EEG microstates and Autism Behavior Checklist (ABC) scores before and after tDCS were compared. Microstates A, B, and D differed significantly between children with TD and those with ASD. In the experimental group, the scores of microstates A and C and ABC before tDCS differed from those after tDCS. Conversely, in the control group, neither the EEG microstates nor the ABC scores before the treatment period (sham stimulation) differed from those after the treatment period. This study indicates that tDCS may become a viable treatment for ASD.
... Now-a-days, EEG has been largely exploited in the diagnosis of several nervous system diseases like Alzheimer's disease, epilepsy, and schizophrenia. EEG can be implemented by placing the electrodes on Schizophrenia patient's scalp at different location [8]. Based on the analysis, the existing technique is required to provide effective outcomes in terms of effective diagnosing and cognitive analysis with low resolution. ...
... EEG microstate representations provide a tool to analyze the temporal dynamics of whole-brain neuronal networks. Microstate analysis has been shown to be capable to diagnose schizophrenia [1] [2], epilepsy [3], Alzheimer's disease and early dementia [4]. However, the study of differences between the microstate characteristics of thought processes remain limited. ...
