Pre-trial and pre-response EEG microstates in schizophrenia: An endophenotypic marker

Soni, S. K.; Muthukrishnan, Suriya Prakash; Samanchi, Rupesh; Sood, Mamta; Kaur, Simran; Sharma, Ratna

doi:10.1016/j.bbr.2019.111964

Cited by 10 publications

(4 citation statements)

References 82 publications

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“…Attention deficit has been regarded as one of the prominent symptoms of schizophrenia. Numerous studies revealed that patients with schizophrenia had obvious deficits in information processing under conditions of high processing load and distraction ( 33 , 51 , 52 ). In fact, patients with schizophrenia demonstrated frontal lobe dysfunction ( 53 ), as well as reduced interaction and functional integration between the prefrontal cortex and other cortical and subcortical brain structures ( 9 ), which might result in the imbalance of neural activities of different sites.…”

Section: Discussionmentioning

confidence: 99%

Abnormalities of Electroencephalography Microstates in Drug-Naïve, First-Episode Schizophrenia

2022

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ObjectiveMicrostate analysis is a powerful tool to probe the brain functions, and changes in microstates under electroencephalography (EEG) have been repeatedly reported in patients with schizophrenia. This study aimed to investigate the dynamics of EEG microstates in drug-naïve, first-episode schizophrenia (FE-SCH) and to test the relationship between EEG microstates and clinical symptoms.MethodsResting-state EEG were recorded for 23 patients with FE-SCH and 23 healthy controls using a 64-channel cap. Three parameters, i.e., contribution, duration, and occurrence, of the four microstate classes were calculated. Group differences in EEG microstates and their clinical symptoms [assessed using the Positive and Negative Syndrome Scale (PANSS)] were analyzed.ResultsCompared with healthy controls, patients with FE-SCH showed increased duration, occurrence and contribution of microstate class C and decreased contribution and occurrence of microstate class D. In addition, the score of positive symptoms in PANSS was negatively correlated with the occurrence of microstate D.ConclusionOur findings showed abnormal patterns of EEG microstates in drug-naïve, first-episode schizophrenia, which might help distinguish individuals with schizophrenia in the early stage and develop early intervention strategies.

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Section: Discussionmentioning

confidence: 99%

Abnormalities of Electroencephalography Microstates in Drug-Naïve, First-Episode Schizophrenia

2022

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“…D'Croz- Baron et al (2019) investigated that six template microstate maps can best describe the dataset across the autism spectrum disorder and neurotypical controls. In research of schizophrenia (Soni et al, 2018(Soni et al, , 2019, four to six microstate maps were clustered, which related to the conditions of the experiments. Michel and Koenig (2018) discussed a metacriterion for the optimal number of clusters.…”

Section: Discussionmentioning

confidence: 99%

“…The greater part of the literature acknowledges four standard microstate maps on healthy subjects at rest. In addition, the characteristics of microstate sequences have been proven to offer a potential biomarker for some diseases, such as mood and anxiety disorders (Al Zoubi et al, 2019), autism spectrum disorder (D'Croz-Baron et al, 2019), and schizophrenia (Soni et al, 2018(Soni et al, , 2019da Cruz et al, 2020;Kim et al, 2021). Baradits et al (2020) created a specified feature set to represent microstate characteristics.…”

Section: Introductionmentioning

confidence: 99%

Dual-Threshold-Based Microstate Analysis on Characterizing Temporal Dynamics of Affective Process and Emotion Recognition From EEG Signals

Chen

et al. 2021

Front. Neurosci.

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Recently, emotion classification from electroencephalogram (EEG) data has attracted much attention. As EEG is an unsteady and rapidly changing voltage signal, the features extracted from EEG usually change dramatically, whereas emotion states change gradually. Most existing feature extraction approaches do not consider these differences between EEG and emotion. Microstate analysis could capture important spatio-temporal properties of EEG signals. At the same time, it could reduce the fast-changing EEG signals to a sequence of prototypical topographical maps. While microstate analysis has been widely used to study brain function, few studies have used this method to analyze how brain responds to emotional auditory stimuli. In this study, the authors proposed a novel feature extraction method based on EEG microstates for emotion recognition. Determining the optimal number of microstates automatically is a challenge for applying microstate analysis to emotion. This research proposed dual-threshold-based atomize and agglomerate hierarchical clustering (DTAAHC) to determine the optimal number of microstate classes automatically. By using the proposed method to model the temporal dynamics of auditory emotion process, we extracted microstate characteristics as novel temporospatial features to improve the performance of emotion recognition from EEG signals. We evaluated the proposed method on two datasets. For public music-evoked EEG Dataset for Emotion Analysis using Physiological signals, the microstate analysis identified 10 microstates which together explained around 86% of the data in global field power peaks. The accuracy of emotion recognition achieved 75.8% in valence and 77.1% in arousal using microstate sequence characteristics as features. Compared to previous studies, the proposed method outperformed the current feature sets. For the speech-evoked EEG dataset, the microstate analysis identified nine microstates which together explained around 85% of the data. The accuracy of emotion recognition achieved 74.2% in valence and 72.3% in arousal using microstate sequence characteristics as features. The experimental results indicated that microstate characteristics can effectively improve the performance of emotion recognition from EEG signals.

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“…[ 8 9 10 ] The pre-stimulus microstates and their underlying sources are capable of influencing stimulus processing and interpretation, leading to differences in accuracy and reaction times during a cognitive task. [ 11 ] As these microstates act as basic building blocks of mentation, their occurrence, sources, and temporal dynamics may reflect how accurately a mental task will be performed. [ 10 ] Deviations from normal temporal dynamics of specific microstates can underlie cognitive deficits observed in neuropsychiatric diseases like substance use disorders.…”

Section: Introductionmentioning

confidence: 99%

Default mode network aberrance in subjects of alcohol and opioid use disorders during working memory task: An exploratory EEG microstates study

Dastidar,

Leon,

Pegwal

et al. 2024

Indian Journal of Psychiatry

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Background: Aberrance in switching from default mode network (DMN) to fronto-parietal network (FPN) is proposed to underlie working memory deficits in subjects with substance use disorders, which can be studied using neuro-imaging techniques during cognitive tasks. The current study used EEG to investigate pre-stimulus microstates during the performance of Sternberg’s working memory task in subjects with substance use disorders. Methods: 128-channel EEG was acquired and processed in ten age and gender-matched subjects, each with alcohol use disorder, opioid use disorder, and controls while they performed Sternberg’s task. Behavioral parameters, pre-stimulus EEG microstate, and underlying sources were analyzed and compared between subjects with substance use disorders and controls. Results: Both alcohol and opioid use disorder subjects had significantly lower accuracy (P < 0.01), while reaction times were significantly higher only in subjects of alcohol use disorder compared to controls (P < 0.01) and opioid use disorder (P < 0.01), reflecting working memory deficits of varying degrees in subjects with substance use disorders. Pre-stimulus EEG microstate revealed four topographic Maps 1-4: subjects of alcohol and opioid use disorder showing significantly lower mean duration of Map 3 (visual processing) and Map 2 (saliency and DMN switching), respectively, compared to controls (P < 0.05). Conclusion: Reduced mean durations in Map 3 and 2 in subjects of alcohol and opioid use disorder can underlie their poorer performance in Sternberg’s task. Furthermore, cortical sources revealed higher activity in both groups of substance use disorders in the parahippocampal gyrus- a hub of DMN; superior and middle temporal gyri associated with impulsivity; and insula that maintains balance between executive reflective system and impulsive system. EEG microstates can be used to envisage neural underpinnings implicated for working memory deficits in subjects of alcohol and opioid use disorders, reflected by aberrant switching between neural networks and information processing mechanisms.

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Pre-trial and pre-response EEG microstates in schizophrenia: An endophenotypic marker

Cited by 10 publications

References 82 publications

Abnormalities of Electroencephalography Microstates in Drug-Naïve, First-Episode Schizophrenia

Abnormalities of Electroencephalography Microstates in Drug-Naïve, First-Episode Schizophrenia

Dual-Threshold-Based Microstate Analysis on Characterizing Temporal Dynamics of Affective Process and Emotion Recognition From EEG Signals

Default mode network aberrance in subjects of alcohol and opioid use disorders during working memory task: An exploratory EEG microstates study

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