Dataset Information

Electroencephalogram Microstate Abnormalities in Early-Course Psychosis.

ABSTRACT:

Background

Microstates are periods of characteristic electroencephalographic signal topography that are related to activity in brain networks. Previous work has identified abnormal microstate parameters in individuals with psychotic disorders. We combined microstate analysis with sample entropy analysis to study the dynamics of resting-state networks in patients with early-course psychosis.

Methods

We used microstate analysis to transform resting-state high-density electroencephalography data from 22 patients with early-course psychosis and 22 healthy control subjects into sequences of characteristic scalp topographies. Sample entropy was used to calculate the complexity of microstate sequences across a range of template lengths.

Results

Patients and control subjects produced similar sets of 4 microstates that agree with a widely reported canonical set (A, B, C, and D). Relative to control subjects, patients had decreased frequency of microstate A. In control subjects, sample entropy decreased as template length increased, suggesting that sequence of microstate transitions is self-similar across multiple transitions. In patients, sample entropy did not decrease, suggesting a lack of self-similarity in transition sequences. This finding was unrelated to data length or microstate topography. Entropy was elevated in unmedicated patients, and it decreased in patients who were administered medication. We identified patterns of transitions between microstates that were overrepresented in control data compared with representation in patient data.

Conclusions

Our findings suggest that patients with early-course psychosis have abnormally chaotic transitions between brain networks. This chaos may reflect an underlying abnormality in allocating neural resources and effecting appropriate transitions between distinct activity states in psychosis.

SUBMITTER: Murphy M

PROVIDER: S-EPMC7309632 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Publications

Electroencephalogram Microstate Abnormalities in Early-Course Psychosis.

Murphy Michael M Stickgold Robert R Öngür Dost D

Biological psychiatry. Cognitive neuroscience and neuroimaging 20190725 1

<h4>Background</h4>Microstates are periods of characteristic electroencephalographic signal topography that are related to activity in brain networks. Previous work has identified abnormal microstate parameters in individuals with psychotic disorders. We combined microstate analysis with sample entropy analysis to study the dynamics of resting-state networks in patients with early-course psychosis.<h4>Methods</h4>We used microstate analysis to transform resting-state high-density electroencephal ...[more]

PMID: 31543456

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Project description:IntroductionRecently, the microstate analysis method has been widely used to investigate the temporal and spatial dynamics of electroencephalogram (EEG) signals. However, most studies have focused on EEG at resting state, and few use microstate analysis to study emotional EEG. This paper aims to investigate the temporal and spatial patterns of EEG in emotional states, and the specific neurophysiological significance of microstates during the emotion cognitive process, and further explore the feasibility and effectiveness of applying the microstate analysis to emotion recognition.MethodsWe proposed a KLGEV-criterion-based microstate analysis method, which can automatically and adaptively identify the optimal number of microstates in emotional EEG. The extracted temporal and spatial microstate features then served as novel feature sets to improve the performance of EEG emotion recognition. We evaluated the proposed method on two publicly available emotional EEG datasets: the SJTU Emotion EEG Dataset (SEED) and the Database for Emotion Analysis using Physiological Signals (DEAP).ResultsFor the SEED dataset, 10 microstates were identified using the proposed method. These temporal and spatial features were fed into AutoGluon, an open-source automatic machine learning model, yielding an average three-class accuracy of 70.38% (±8.03%) in subject-dependent emotion recognition. For the DEAP dataset, the method identified 9 microstates. The average accuracy in the arousal dimension was 74.33% (±5.17%) and 75.49% (±5.70%) in the valence dimension, which were competitive performance compared to some previous machine-learning-based studies. Based on these results, we further discussed the neurophysiological relationship between specific microstates and emotions, which broaden our knowledge of the interpretability of EEG microstates. In particular, we found that arousal ratings were positively correlated with the activity of microstate C (anterior regions of default mode network) and negatively correlated with the activity of microstate D (dorsal attention network), while valence ratings were positively correlated with the activity of microstate B (visual network) and negatively correlated with the activity of microstate D (dorsal attention network).DiscussionIn summary, the findings in this paper indicate that the proposed KLGEV-criterion-based method can be employed to research emotional EEG signals effectively, and the microstate features are promising feature sets for EEG-based emotion recognition.

Project description:ImportanceMost patients with psychotic disorders experience severe cognitive impairment, but the onset and course of this impairment remain unclear. Moreover, the course of cognitive functions in other psychiatric conditions remains largely unexamined.ObjectiveTo chart the course of general and specific cognitive functions in individuals with psychotic disorders, psychotic experiences, and depression.Design, setting, and participantsThe Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective cohort study comprising all live births between April 1, 1991, and December 31, 1992, in Avon, England. The dates of analysis were September 2015 to July 2016. Participants who underwent cognitive testing at ages 18 months and 4, 8, 15, and 20 years and psychiatric assessment at age 18 years were included.Main outcomes and measuresIndividuals with psychotic disorder, psychosis with depression, psychotic experiences, and depression were compared with controls. Outcomes were full-scale, verbal, and nonverbal IQ at ages 18 months and 4, 8, 15, and 20 years, as well as measures of processing speed, working memory, language, visuospatial ability, and attention at ages 8 and 20 years.ResultsThe following numbers of individuals were available for analyses in this longitudinal birth cohort study: 511 (238 male [46.6%]) at age 18 months (mean [SD] age, 1.53 [0.03] years), 483 (229 male [47.4%]) at age 4 years (mean [SD] age, 4.07 [0.03] years), 3930 (1679 male [42.7%]) at age 8 years (mean [SD] age, 8.65 [0.29] years), 3783 (1686 male [44.6%]) at age 15 years (mean [SD] age, 15.45 [0.27] years), and 257 (90 male [35.0%]) at age 20 years (mean [SD] age, 20.06 [0.55] years). Individuals with psychotic disorder showed continually increasing deficits between infancy (18 months) and adulthood (20 years) in full-scale IQ (effect size of change [ESΔ] = -1.09, P = .02) and nonverbal IQ (ESΔ = -0.94, P = .008). The depression group showed a small, increasing deficit in nonverbal IQ (ESΔ = -0.29, P = .04) between infancy and adulthood. Between ages 8 and 20 years, the psychotic disorder group exhibited developmental lags (ie, slower growth) in measures of processing speed (ESΔ = -0.68, P = .001), working memory (ESΔ = -0.59, P = .004), and attention (ESΔ = -0.44, P = .001) and large, static deficits in measures of language (ES = -0.87, P = .005) and visuospatial ability (ES = -0.90, P = .001). There was only weak evidence for cognitive deficits in the psychosis with depression group and the psychotic experiences group.Conclusions and relevanceThe findings herein suggest that the origins of psychotic disorder involve dynamic developmental processes, affecting both verbal and nonverbal abilities throughout the first 2 decades of life and leading to increasing dysfunction. These developmental processes do not manifest in other psychiatric disorders, such as psychosis with depression and depression.

Project description:Psychosis disorders share overlapping symptoms and are characterized by a wide-spread breakdown in functional brain integration. Although neuroimaging studies have identified numerous connectivity abnormalities in affective and non-affective psychoses, whether they have specific or unique connectivity abnormalities, especially within the early stage is still poorly understood. The early phase of psychosis is a critical period with fewer chronic confounds and when treatment intervention may be most effective. In this work, we examined whole-brain functional network connectivity (FNC) from both static and dynamic perspectives in patients with affective psychosis (PAP) or with non-affective psychosis (PnAP) and healthy controls (HCs). A fully automated independent component analysis (ICA) pipeline called "Neuromark" was applied to high-quality functional magnetic resonance imaging (fMRI) data with 113 early-phase psychosis patients (32 PAP and 81 PnAP) and 52 HCs. Relative to the HCs, both psychosis groups showed common abnormalities in static FNC (sFNC) between the thalamus and sensorimotor domain, and between subcortical regions and the cerebellum. PAP had specifically decreased sFNC between the superior temporal gyrus and the paracentral lobule, and between the cerebellum and the middle temporal gyrus/inferior parietal lobule. On the other hand, PnAP showed increased sFNC between the fusiform gyrus and the superior medial frontal gyrus. Dynamic FNC (dFNC) was investigated using a combination of a sliding window approach, clustering analysis, and graph analysis. Three reoccurring brain states were identified, among which both psychosis groups had fewer occurrences in one antagonism state (state 2) and showed decreased network efficiency within an intermediate state (state 1). Compared with HCs and PnAP, PAP also showed a significantly increased number of state transitions, indicating more unstable brain connections in affective psychosis. We further found that the identified connectivity features were associated with the overall positive and negative syndrome scale, an assessment instrument for general psychopathology and positive symptoms. Our findings support the view that subcortical-cortical information processing is disrupted within five years of the initial onset of psychosis and provide new evidence that abnormalities in both static and dynamic connectivity consist of shared and unique features for the early affective and non-affective psychoses.

Dataset Information

Electroencephalogram Microstate Abnormalities in Early-Course Psychosis.

Background

Methods

Results

Conclusions

Publications

Electroencephalogram Microstate Abnormalities in Early-Course Psychosis.

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