Electroencephalogram Microstate Abnormalities in Early-Course Psychosis.
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SUBMITTER: Murphy M
PROVIDER: S-EPMC7309632 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
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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]
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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.
| S-EPMC10972890 | biostudies-literature
Project description:There has been considerable interest in the role of synchronous brain activity abnormalities in the pathophysiology of psychotic disorders and their relevance for treatment; one index of such activity are EEG resting-state microstates. These reflect electric field configurations of the brain that persist over 60-120 ms time periods. A set of quasi-stable microstates classes A, B, C, and D have been repeatedly identified across healthy participants. Changes in microstate parameters coverage, duration and occurrence have been found in medication-naïve as well as medicated patients with psychotic disorders compared to healthy controls. However, to date, only two studies have directly compared antipsychotic medication effects on EEG microstates either pre- vs. post-treatment or between medicated and unmedicated chronic schizophrenia patients. The aim of this study was therefore to directly compare EEG resting-state microstates between medicated and medication-naïve (untreated) first-episode (FEP) psychosis patients (mFEP vs. uFEP). We used 19-channel clinical EEG recordings to compare temporal parameters of four prototypical microstate classes (A-D) within an overall sample of 47 patients (mFEP n = 17; uFEP n = 30). The results demonstrated significant decreases of microstate class A and significant increases of microstate class B in mFEP compared to uFEP. No significant differences between groups were found for microstate classes C and D. Further studies are needed to replicate these results in longitudinal designs that assess antipsychotic medication effects on neural networks at the onset of the disorder and over time during illness progression. As treatment response and compliance in FEP patients are relatively low, such studies could contribute to better understand treatment outcomes and ultimately improve treatment strategies.
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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.
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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.
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Project description:Bipolar I disorder is a disabling illness affecting 1% of people worldwide. Family and twin studies suggest that psychotic bipolar disorder (BDP) represents a homogeneous subgroup with an etiology distinct from non-psychotic bipolar disorder (BDNP) and partially shared with schizophrenia. Studies of auditory electrophysiology [e.g., paired-stimulus and oddball measured with electroencephalography (EEG)] consistently report deviations in psychotic groups (schizophrenia, BDP), yet such studies comparing BDP and BDNP are sparse and, in some cases, conflicting. Auditory EEG responses are significantly reduced in unaffected relatives of psychosis patients, suggesting that they may relate to both psychosis liability and expression.While 64-sensor EEGs were recorded, age- and gender-matched samples of 70 BDP, 35 BDNP {20 with a family history of psychosis [BDNP(+)]}, and 70 psychiatrically healthy subjects were presented with typical auditory paired-stimuli and auditory oddball paradigms.Oddball P3b reductions were present and indistinguishable across all patient groups. P2s to paired stimuli were abnormal only in BDP and BDNP(+). Conversely, N1 reductions to stimuli in both paradigms and P3a reductions were present in both BDP and BDNP(-) groups but were absent in BDNP(+).Although nearly all auditory neural response components studied were abnormal in BDP, BDNP abnormalities at early- and mid-latencies were moderated by family psychosis history. The relationship between psychosis expression, heritable psychosis risk, and neurophysiology within bipolar disorder, therefore, may be complex. Consideration of such clinical disease heterogeneity may be important for future investigations of the pathophysiology of major psychiatric disturbance.
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Project description:The dynamics of the resting brain exhibit transitions between a small number of discrete networks, each remaining stable for tens to hundreds of milliseconds. These functional microstates are thought to be the building blocks of spontaneous consciousness. The electroencephalogram (EEG) is a useful tool for imaging microstates, and EEG microstate analysis can potentially give insight into altered brain dynamics underpinning cognitive impairment in disorders such as Alzheimer's disease (AD). Since EEG is non-invasive and relatively inexpensive, EEG microstates have the potential to be useful clinical tools for aiding early diagnosis of AD. In this study, EEG was collected from two independent cohorts of probable AD and cognitively healthy control participants, and a cohort of mild cognitive impairment (MCI) patients with four-year clinical follow-up. The microstate associated with the frontoparietal working-memory/attention network was altered in AD due to parietal inactivation. Using a novel measure of complexity, we found microstate transitioning was slower and less complex in AD. When combined with a spectral EEG measure, microstate complexity could classify AD with sensitivity and specificity > 80%, which was tested on an independent cohort, and could predict progression from MCI to AD in a small preliminary test cohort of 11 participants. EEG microstates therefore have potential to be a non-invasive functional biomarker of AD.
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Project description:The yield of epileptiform abnormalities in serial electroencephalography (EEG) studies has not been addressed in a population-based setting for subjects with incident epilepsy or a single unprovoked seizure, raising the possibility of methodologic limitations such as selection bias. Our aim was to address these limitations by assessing the yield and predictors of epileptiform abnormalities for the first and subsequent EEG recording in a study of incident epilepsy or single unprovoked seizure in Rochester, Minnesota.We used the resources of the Rochester Epidemiology Project to identify all 619 residents of Rochester, Minnesota, born in 1920 or later with a diagnosis of incident epilepsy (n = 478) or single unprovoked seizure (n = 141) between 1960 and 1994, who had at least one EEG study. Information on all EEG studies and their results was obtained by comprehensive review of medical records.Among subjects with epilepsy, the cumulative yield of epileptiform abnormalities was 53% after the first EEG study and 72% after the third. Among subjects with a single unprovoked seizure, the cumulative yield was 39% after the first EEG study and 68% after the third. Young age at diagnosis and idiopathic etiology were risk factors for finding epileptiform abnormalities across all EEG recordings.Although the cumulative yield of epileptiform abnormalities increases over successive EEG recordings, there is a decrease in the increment for each additional EEG study after the first EEG study. This is most evident in incident epilepsy and in younger subjects. Clinically it may be worthwhile to consider that the probability of finding an epileptiform abnormality after the third nonepileptiform EEG recording is low.
| S-EPMC4167205 | biostudies-literature
Project description:Resting state functional magnetic resonance imaging studies of psychosis have focused primarily on the amplitude of low-frequency fluctuations in the blood oxygen level dependent (BOLD) signal ranging from .01 to 0.1 Hz. Few studies, however, have investigated the amplitude of frequency fluctuations within discrete frequency bands and higher than 0.1 Hz in patients with psychosis at different illness stages. We investigated BOLD signal within three frequency ranges including slow-4 (.027-.073?Hz), slow-3 (.074-0.198?Hz) and slow-2 (0.199-0.25?Hz) in 89 patients with either first-episode or chronic psychosis and 119 healthy volunteers. We investigated the amplitude of frequency fluctuations within three frequency bands using 47 regions-of-interest placed within 14 known resting state networks derived using group independent component analysis. There were significant group x frequency interactions for the visual and motor cortex networks, with the largest significant group differences (patients < healthy volunteers) evident in slow-4 and slow-3, respectively. Also, healthy volunteers had an overall higher amplitude of frequency fluctuations compared to patients across the three frequency ranges in the visual cortex, dorsal attention and motor cortex networks with the opposite effect (patients > healthy volunteers) evident within the salience and frontal gyrus networks. Subsequent analyses indicated that these effects were evident in both first-episode and chronic patients. Our study provides new data regarding the importance of BOLD signal fluctuations within different frequency bands in the neurobiology of psychosis.
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Project description:The continuum view of the psychosis spectrum (PS) implies that, in population-based samples, PS symptoms should be associated with neural abnormalities similar to those found in help-seeking clinical risk individuals and in schizophrenia. To our knowledge, functional neuroimaging has not previously been applied in large population-based PS samples and can help us understand the neural architecture of psychosis more broadly and identify brain phenotypes beyond symptoms that are associated with the extended psychosis phenotype.To examine the categorical and dimensional relationships of PS symptoms to prefrontal hypoactivation during working memory and to amygdala hyperactivation during threat emotion processing.The Philadelphia Neurodevelopmental Cohort is a genotyped, prospectively accrued, population-based sample of almost 10,000 youths who received a structured psychiatric evaluation and a computerized neurocognitive battery. The study was conducted at an academic and children's hospital health care network, between November 1, 2009 to November 30, 2011. A subsample of 1445 youths underwent neuroimaging, including functional magnetic resonance imaging tasks examined herein. Participants were youth aged 11 to 22 years old identified through structured interview as having PS features (PS group) (n = 260) and typically developing (TD) comparison youth without significant psychopathology (TD group) (n = 220).Two functional magnetic resonance imaging paradigms were used: a fractal n-back working memory task probing executive system function and an emotion identification task probing amygdala responses to threatening faces.In the n-back task, working memory evoked lower activation in the PS group than the TD group throughout the executive control circuitry, including dorsolateral prefrontal cortex (cluster-corrected P <?.05). Within the PS group, dorsolateral prefrontal cortex activation correlated with cognitive deficits (r =?.32, P <?.001), but no correlation was found with positive symptom severity. During emotion identification, PS demonstrated elevated responses to threatening facial expressions in amygdala, as well as left fusiform cortex and right middle frontal gyrus (cluster-corrected P <?.05). The response in the amygdala correlated with positive symptom severity (r =?.16, P =?.01) but not with cognitive deficits.The pattern of functional abnormalities observed in the PS group is similar to that previously found in schizophrenia and help-seeking risk samples. Specific circuit dysfunction during cognitive and emotion-processing tasks is present early in the development of psychopathology and herein could not be attributed to chronic illness or medication confounds. Hypoactivation in executive circuitry and limbic hyperactivation to threat could reflect partly independent risk factors for PS symptoms, with the former relating to cognitive deficits that increase the risk for developing psychotic symptoms and the latter contributing directly to positive psychotic symptoms.
| S-EPMC4581844 | biostudies-literature
Project description:ImportanceStructural brain abnormalities are prominent in psychotic disorders, including schizophrenia. However, it is unclear when aberrations emerge in the disease process and if such deficits are present in association with less severe psychosis spectrum (PS) symptoms in youth.ObjectiveTo investigate the presence of structural brain abnormalities in youth with PS symptoms.Design, setting, and participantsThe Philadelphia Neurodevelopmental Cohort is a prospectively accrued, community-based sample of 9498 youth who received a structured psychiatric evaluation. A subsample of 1601 individuals underwent neuroimaging, including structural magnetic resonance imaging, at an academic and children's hospital health care network between November 1, 2009, and November 30, 2011.Main outcomes and measuresMeasures of brain volume derived from T1-weighted structural neuroimaging at 3 T. Analyses were conducted at global, regional, and voxelwise levels. Regional volumes were estimated with an advanced multiatlas regional segmentation procedure, and voxelwise volumetric analyses were conducted as well. Nonlinear developmental patterns were examined using penalized splines within a general additive model. Psychosis spectrum (PS) symptom severity was summarized using factor analysis and evaluated dimensionally.ResultsFollowing exclusions due to comorbidity and image quality assurance, the final sample included 791 participants aged youth 8 to 22 years. Fifty percent (n = 393) were female. After structured interviews, 391 participants were identified as having PS features (PS group) and 400 participants were identified as typically developing comparison individuals without significant psychopathology (TD group). Compared with the TD group, the PS group had diminished whole-brain gray matter volume (P = 1.8 × 10-10) and expanded white matter volume (P = 2.8 × 10-11). Voxelwise analyses revealed significantly lower gray matter volume in the medial temporal lobe (maximum z score = 5.2 and cluster size of 1225 for the right and maximum z score = 4.5 and cluster size of 310 for the left) as well as in frontal, temporal, and parietal cortex. Volumetric reduction in the medial temporal lobe was correlated with PS symptom severity.Conclusions and relevanceStructural brain abnormalities that have been commonly reported in adults with psychosis are present early in life in youth with PS symptoms and are not due to medication effects. Future longitudinal studies could use the presence of such abnormalities in conjunction with clinical presentation, cognitive profile, and genomics to predict risk and aid in stratification to guide early interventions.
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