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Thomas Koenig - One of the best experts on this subject based on the ideXlab platform.
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EEG Microstates and Psychosocial Stress During an Exchange Year
Brain Topography, 2020Co-Authors: Nursija Kadier, Maria Stein, Thomas KoenigAbstract:The well-known stress vulnerability model of psychosis assumes that psychotic episodes result from the coincidence of individual trait dispositions and triggering stressors. We thus hypothesized that a transient psychosocial stressor would not only increase the number of and stress caused by psychosis-like symptoms (like delusion-like symptoms or auditory hallucinations) in healthy subjects but also elicit changes in EEG Microstates that have been related to the presence of psychotic symptoms in patients with schizophrenia. Considering a radical change of one’s psychosocial environment as a significant stressor, we analyzed psychotic symptoms and EEG microstate data in teenage exchange-students at an early and a later phase of their stay. The subjects experienced a small and transient, but significant increase of stress by psychosis-like symptoms. These changes in mental state were associated with increases in microstate class A, which has previously been related to unspecific stress. microstate classes C and D, which have consistently been found to be altered in patients with psychosis, were found unaffected by the time of the recording and the subjective stress experiences. Therefore, we conclude that microstate class A appears to be a psychosis independent and rather general correlate of psychosocial stress, whereas changes in microstate classes C and D seem to be more specifically tied to the presence of psychotic symptoms.
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Investigating the temporal dynamics of electroencephalogram (EEG) Microstates using recurrent neural networks.
Human brain mapping, 2020Co-Authors: Apoorva Sikka, Hamidreza Jamalabadi, M Krylova, Sarah Alizadeh, Johan Van Der Meer, Lena Danyeli, Matthias Deliano, Petya Vicheva, Tim Hahn, Thomas KoenigAbstract:Electroencephalogram (EEG) Microstates that represent quasi-stable, global neuronal activity are considered as the building blocks of brain dynamics. Therefore, the analysis of microstate sequences is a promising approach to understand fast brain dynamics that underlie various mental processes. Recent studies suggest that EEG microstate sequences are non-Markovian and nonstationary, highlighting the importance of the sequential flow of information between different brain states. These findings inspired us to model these sequences using Recurrent Neural Networks (RNNs) consisting of long-short-term-memory (LSTM) units to capture the complex temporal dependencies. Using an LSTM-based auto encoder framework and different encoding schemes, we modeled the microstate sequences at multiple time scales (200-2,000 ms) aiming to capture stably recurring microstate patterns within and across subjects. We show that RNNs can learn underlying microstate patterns with high accuracy and that the microstate trajectories are subject invariant at shorter time scales (≤400 ms) and reproducible across sessions. Significant drop in the reconstruction accuracy was observed for longer sequence lengths of 2,000 ms. These findings indirectly corroborate earlier studies which indicated that EEG microstate sequences exhibit long-range dependencies with finite memory content. Furthermore, we find that the latent representations learned by the RNNs are sensitive to external stimulation such as stress while the conventional univariate microstate measures (e.g., occurrence, mean duration, etc.) fail to capture such changes in brain dynamics. While RNNs cannot be configured to identify the specific discriminating patterns, they have the potential for learning the underlying temporal dynamics and are sensitive to sequence aberrations characterized by changes in metal processes. Empowered with the macroscopic understanding of the temporal dynamics that extends beyond short-term interactions, RNNs offer a reliable alternative for exploring system level brain dynamics using EEG microstate sequences.
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resting state eeg in schizophrenia auditory verbal hallucinations are related to shortening of specific Microstates
Clinical Neurophysiology, 2011Co-Authors: Jochen Kindler, Werner Strik, Daniela Hubl, Thomas Dierks, Thomas KoenigAbstract:Objective: Abnormal perceptions and cognitions in schizophrenia might be related to abnormal resting states of the brain. Previous research found that a specific class (class D) of sub-second electroencephalography (EEG) Microstates was shortened in schizophrenia. This shortening correlated with positive symptoms. We questioned if this reflected positive psychotic traits or present psychopathology. Methods: Resting-state EEGs of frequently hallucinating patients, indicating on- and offset of hallucinations by button press, were analyzed. Microstate class D duration was related to spontaneous within-subject fluctuations of auditory hallucinations. Results: Microstate D was significantly shorter in periods with hallucinations. Conclusions: Microstates of class D resemble topographies associated with error monitoring. Its premature termination may facilitate the misattribution of self-generated inner speech to external sources during hallucinations. Significance: These results suggest that microstate D represents a biological state marker for hallucinatory experiences.
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Chronic schizophrenics with positive symptomatology have shortened EEG microstate durations
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2003Co-Authors: Pascal L. Faber, Lorena R. R. Gianotti, Thomas Koenig, J.v Golikova, V.y Novototsky-vlasov, John Gruzelier, Dietrich LehmannAbstract:Objective: In young, first-episode, never-treated schizophrenics compared with controls, (a) generally shorter durations of EEG Microstates were reported (Koukkou et al., Brain Topogr 6 (1994) 251; Kinoshita et al., Psychiatry Res Neuroimaging 83 (1998) 58), and (b) specifically, shorter duration of a particular class of Microstates (Koenig et al., Eur Arch Psychiatry Clin Neurosci 249 (1999) 205). We now examined whether older, chronic schizophrenic patients with positive symptomatology also show these characteristics. Methods: Multichannel resting EEG (62.2 s/subject) from two subject groups, 14 patients (36.1±10.2 years old) and 13 controls (35.1±8.2 years old), all males, was analyzed into Microstates using a global approach for microstate analysis that clustered the Microstates into 4 classes (Koenig et al., 1999). Results: (a) Hypothesis testing of general microstate shortening supported a trend (P=0.064). (b) Two-way repeated measure ANOVA (two subject groups×4 microstate classes) showed a significant group effect for microstate duration. Posthoc tests revealed that a microstate class with brain electric field orientation from left central to right central-posterior had significantly shorter Microstates in patients than controls (68.5 vs. 76.1 ms, P=0.034). Conclusions: The results were in line with the results from young, never-treated, productive patients, thus suggesting that in schizophrenic information processing, one class of mental operations might intermittently cause deviant mental constructs because of premature termination of processing.
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millisecond by millisecond year by year normative eeg Microstates and developmental stages
NeuroImage, 2002Co-Authors: Thomas Koenig, Dietrich Lehmann, Leslie S Prichep, Pedro Valdes Sosa, Elisabeth Braeker, Horst Kleinlogel, Robert Isenhart, Roy E JohnAbstract:Most studies of continuous EEG data have used frequency transformation, which allows the quantification of brain states that vary over seconds. For the analysis of shorter, transient EEG events, it is possible to identify and quantify brain electric Microstates as subsecond time epochs with stable field topography. These Microstates may correspond to basic building blocks of human information processing. Microstate analysis yields a compact and comprehensive repertoire of short lasting classes of brain topographic maps, which may be considered to reflect global functional states. Each microstate class is described by topography, mean duration, frequency of occurrence and percentage analysis time occupied. This paper presents normative microstate data for resting EEG obtained from a database of 496 subjects between the age of 6 and 80 years. The extracted microstate variables showed a lawful, complex evolution with age. The pattern of changes with age is compatible with the existence of developmental stages as claimed by developmental psychologists. The results are discussed in the framework of state dependent information processing and suggest the existence of biologically predetermined top-down processes that bias brain electric activity to functional states appropriate for age-specific learning and behavior.
Christoph M Michel - One of the best experts on this subject based on the ideXlab platform.
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EEG Microstates of dreams.
Scientific reports, 2020Co-Authors: Lucie Bréchet, Denis Brunet, Lampros Perogamvros, Giulio Tononi, Christoph M MichelAbstract:Why do people sometimes report that they remember dreams, while at other times they recall no experience? Despite the interest in dreams that may happen during the night, it has remained unclear which brain states determine whether these conscious experiences will occur and what prevents us from waking up during these episodes. Here we address this issue by comparing the EEG activity preceding awakenings with recalled vs. no recall of dreams using the EEG microstate approach. This approach characterizes transiently stable brain states of sub-second duration that involve neural networks with nearly synchronous dynamics. We found that two Microstates (3 and 4) dominated during NREM sleep compared to resting wake. Further, within NREM sleep, microstate 3 was more expressed during periods followed by dream recall, whereas microstate 4 was less expressed. Source localization showed that microstate 3 encompassed the medial frontal lobe, whereas microstate 4 involved the occipital cortex, as well as thalamic and brainstem structures. Since NREM sleep is characterized by low-frequency synchronization, indicative of neuronal bistability, we interpret the increased presence of the "frontal" microstate 3 as a sign of deeper local deactivation, and the reduced presence of the "occipital" microstate 4 as a sign of local activation. The latter may account for the occurrence of dreaming with rich perceptual content, while the former may account for why the dreaming brain may undergo executive disconnection and remain asleep. This study demonstrates that NREM sleep consists of alternating brain states whose temporal dynamics determine whether conscious experience arises.
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Microstates in resting-state EEG: Current status and future directions
Neuroscience and biobehavioral reviews, 2014Co-Authors: Arjun Khanna, Christoph M Michel, Alvaro Pascual-leone, Faranak FarzanAbstract:Electroencephalography (EEG) is a powerful method of studying the electrophysiology of the brain with high temporal resolution. Several analytical approaches to extract information from the EEG signal have been proposed. One method, termed microstate analysis, considers the multichannel EEG recording as a series of quasi-stable "Microstates" that are each characterized by a unique topography of electric potentials over the entire channel array. Because this technique simultaneously considers signals recorded from all areas of the cortex, it is capable of assessing the function of large-scale brain networks whose disruption is associated with several neuropsychiatric disorders. In this review, we first introduce the method of EEG microstate analysis. We then review studies that have discovered significant changes in the resting-state microstate series in a variety of neuropsychiatric disorders and behavioral states. We discuss the potential utility of this method in detecting neurophysiological impairments in disease and monitoring neurophysiological changes in response to an intervention. Finally, we discuss how the resting-state microstate series may reflect rapid switching among neural networks while the brain is at rest, which could represent activity of resting-state networks described by other neuroimaging modalities. We conclude by commenting on the current and future status of microstate analysis, and suggest that EEG Microstates represent a promising neurophysiological tool for understanding and assessing brain network dynamics on a millisecond timescale in health and disease.
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deviant dynamics of eeg resting state pattern in 22q11 2 deletion syndrome adolescents a vulnerability marker of schizophrenia
Schizophrenia Research, 2014Co-Authors: Miralena I Tomescu, Tonia A Rihs, Robert Becker, Juliane Britz, Anna Custo, Frederic Grouiller, Maude Schneider, Martin Debbane, Stephan Eliez, Christoph M MichelAbstract:Abstract Previous studies have repeatedly found altered temporal characteristics of EEG Microstates in schizophrenia. The aim of the present study was to investigate whether adolescents affected by the 22q11.2 deletion syndrome (22q11DS), known to have a 30 fold increased risk to develop schizophrenia, already show deviant EEG Microstates. If this is the case, temporal alterations of EEG Microstates in 22q11DS individuals could be considered as potential biomarkers for schizophrenia. We used high-density (204 channel) EEG to explore between-group microstate differences in 30 adolescents with 22q11DS and 28 age-matched controls. We found an increased presence of one microstate class (class C) in the 22q11DS adolescents with respect to controls that was associated with positive prodromal symptoms (hallucinations). A previous across-age study showed that the class C microstate was more present during adolescence and a combined EEG–fMRI study associated the class C microstate with the salience resting state network, a network known to be dysfunctional in schizophrenia. Therefore, the increased class C Microstates could be indexing the increased risk of 22q11DS individuals to develop schizophrenia if confirmed by our ongoing longitudinal study comparing both the adult 22q11DS individuals with and without schizophrenia, as well as schizophrenic individuals with and without 22q11DS.
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EEG Microstates of wakefulness and NREM sleep.
NeuroImage, 2012Co-Authors: Verena Brodbeck, Christoph M Michel, Alena Kuhn, Frederic Von Wegner, Astrid Morzelewski, Enzo Tagliazucchi, S.v. Borisov, Helmut LaufsAbstract:EEG-Microstates exploit spatio-temporal EEG features to characterize the spontaneous EEG as a sequence of a finite number of quasi-stable scalp potential field maps. So far, EEG-Microstates have been studied mainly in wakeful rest and are thought to correspond to functionally relevant brain-states. Four typical microstate maps have been identified and labeled arbitrarily with the letters A, B, C and D. We addressed the question whether EEG-microstate features are altered in different stages of NREM sleep compared to wakefulness. 32-channel EEG of 32 subjects in relaxed wakefulness and NREM sleep was analyzed using a clustering algorithm, identifying the most dominant amplitude topography maps typical of each vigilance state. Fitting back these maps into the sleep-scored EEG resulted in a temporal sequence of maps for each sleep stage. All 32 subjects reached sleep stage N2, 19 also N3, for at least 1 min and 45 s. As in wakeful rest we found four microstate maps to be optimal in all NREM sleep stages. The wake maps were highly similar to those described in the literature for wakefulness. The sleep stage specific map topographies of N1 and N3 sleep showed a variable but overall relatively high degree of spatial correlation to the wake maps (Mean: N1 92%; N3 87%). The N2 maps were the least similar to wake (mean: 83%). Mean duration, total time covered, global explained variance and transition probabilities per subject, map and sleep stage were very similar in wake and N1. In wake, N1 and N3, microstate map C was most dominant w.r.t. global explained variance and temporal presence (ratio total time), whereas in N2 microstate map B was most prominent. In N3, the mean duration of all microstate maps increased significantly, expressed also as an increase in transition probabilities of all maps to themselves in N3. This duration increase was partly--but not entirely--explained by the occurrence of slow waves in the EEG. The persistence of exactly four main microstate classes in all NREM sleep stages might speak in favor of an in principle maintained large scale spatial brain organization from wakeful rest to NREM sleep. In N1 and N3 sleep, despite spectral EEG differences, the microstate maps and characteristics were surprisingly close to wakefulness. This supports the notion that EEG Microstates might reflect a large scale resting state network architecture similar to preserved fMRI resting state connectivity. We speculate that the incisive functional alterations which can be observed during the transition to deep sleep might be driven by changes in the level and timing of activity within this architecture.
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EEG microstate sequences in healthy humans at rest reveal scale-free dynamics
Proceedings of the National Academy of Sciences of the United States of America, 2010Co-Authors: Dimitri Van De Ville, J. Britz, Christoph M MichelAbstract:Recent findings identified electroencephalography (EEG) Microstates as the electrophysiological correlates of fMRI resting-state networks. Microstates are defined as short periods (100 ms) during which the EEG scalp topography remains quasi-stable; that is, the global topography is fixed but strength might vary and polarity invert. Microstates represent the subsecond coherent activation within global functional brain networks. Surprisingly, these rapidly changing EEG Microstates correlate significantly with activity in fMRI resting-state networks after convolution with the hemodynamic response function that constitutes a strong temporal smoothing filter. We postulate here that microstate sequences should reveal scale-free, self-similar dynamics to explain this remarkable effect and thus that microstate time series show dependencies over long time ranges. To that aim, we deploy wavelet-based fractal analysis that allows determining scale-free behavior. We find strong statistical evidence that microstate sequences are scale free over six dyadic scales covering the 256-ms to 16-s range. The degree of long-range dependency is maintained when shuffling the local microstate labels but becomes indistinguishable from white noise when equalizing microstate durations, which indicates that temporal dynamics are their key characteristic. These results advance the understanding of temporal dynamics of brain-scale neuronal network models such as the global workspace model. Whereas Microstates can be considered the "atoms of thoughts," the shortest constituting elements of cognition, they carry a dynamic signature that is reminiscent at characteristic timescales up to multiple seconds. The scale-free dynamics of the Microstates might be the basis for the rapid reorganization and adaptation of the functional networks of the brain.
Dietrich Lehmann - One of the best experts on this subject based on the ideXlab platform.
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The resting microstate networks (RMN): cortical distributions, dynamics, and frequency specific information flow
arXiv: Neurons and Cognition, 2014Co-Authors: Roberto D. Pascual-marqui, Toshiaki Isotani, Pascal L. Faber, Dietrich Lehmann, K Kochi, Patricia Milz, Masafumi Yoshimura, Keiichiro Nishida, Toshihiko KinoshitaAbstract:A brain microstate is characterized by a unique, fixed spatial distribution of electrically active neurons with time varying amplitude. It is hypothesized that a microstate implements a functional/physiological state of the brain during which specific neural computations are performed. Based on this hypothesis, brain electrical activity is modeled as a time sequence of non-overlapping Microstates with variable, finite durations (Lehmann and Skrandies 1980, 1984; Lehmann et al 1987). In this study, EEG recordings from 109 participants during eyes closed resting condition are modeled with four Microstates. In a first part, a new confirmatory statistics method is introduced for the determination of the cortical distributions of electric neuronal activity that generate each microstate. All Microstates have common posterior cingulate generators, while three Microstates additionally include activity in the left occipital/parietal, right occipital/parietal, and anterior cingulate cortices. This appears to be a fragmented version of the metabolically (PET/fMRI) computed default mode network (DMN), supporting the notion that these four regions activate sequentially at high time resolution, and that slow metabolic imaging corresponds to a low-pass filtered version. In the second part of this study, the microstate amplitude time series are used as the basis for estimating the strength, directionality, and spectral characteristics (i.e., which oscillations are preferentially transmitted) of the connections that are mediated by the microstate transitions. The results show that the posterior cingulate is an important hub, sending alpha and beta oscillatory information to all other microstate generator regions. Interestingly, beyond alpha, beta oscillations are essential in the maintenance of the brain during resting state.
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Resting-state connectivity in the prodromal phase of schizophrenia: insights from EEG Microstates.
Schizophrenia research, 2014Co-Authors: Christina Andreou, Pascal L. Faber, Dietrich Lehmann, Gregor Leicht, Daniel Schoettle, Nenad Polomac, Ileana L Hanganu-opatz, Christoph MulertAbstract:Resting-state EEG Microstates are thought to reflect the momentary local states and interactions of distributed neural networks in the brain. Several changes in resting-state EEG Microstates have been described in acutely ill patients with schizophrenia, but it is not known whether these represent trait or state abnormalities. The present study aimed to investigate this issue by assessing EEG microstate characteristics in high-risk individuals (HR) and clinically stable first-episode patients with schizophrenia (SZ) with low symptom levels, compared to each other and healthy controls (HC). Participants were 18 HR, 18 SZ, and 22 HC subjects. 64-channel resting-state EEG recordings were used for microstate analyses. Microstates were clustered into four classes (A-D) according to their topography. Temporal parameters and topographies of Microstates were compared among groups. Microstate class A displayed higher coverage and occurrence in HR than SZ and HC, while microstate class B covered significantly more time in SZ compared to both HR and HC. Microstate class B displayed an aberrant spatial configuration in SZ, and to a lesser extent also in HR, compared to HC, with patients exhibiting significantly higher activity in the vicinity of the left posterior cingulate. Microstate abnormalities observed in HR were similar to those previously reported in acutely ill patients with schizophrenia. Moreover, there was evidence that HR and SZ might share specific disturbances in brain functional connectivity. These findings raise the possibility that certain abnormalities in resting-state EEG Microstates might be associated with an increased risk for psychosis. Copyright © 2013 Elsevier B.V. All rights reserved.
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Resting-state connectivity in the prodromal phase of schizophrenia: insights from EEG Microstates.
Schizophrenia Research, 2014Co-Authors: Christina Andreou, Pascal L. Faber, Dietrich Lehmann, Gregor Leicht, Daniel Schoettle, Nenad Polomac, Ileana L Hanganu-opatz, Christoph MulertAbstract:Abstract Introduction Resting-state EEG Microstates are thought to reflect the momentary local states and interactions of distributed neural networks in the brain. Several changes in resting-state EEG Microstates have been described in acutely ill patients with schizophrenia, but it is not known whether these represent trait or state abnormalities. The present study aimed to investigate this issue by assessing EEG microstate characteristics in high-risk individuals (HR) and clinically stable first-episode patients with schizophrenia (SZ) with low symptom levels, compared to each other and healthy controls (HC). Method Participants were 18 HR, 18 SZ, and 22 HC subjects. 64-channel resting-state EEG recordings were used for microstate analyses. Microstates were clustered into four classes (A–D) according to their topography. Temporal parameters and topographies of Microstates were compared among groups. Results Microstate class A displayed higher coverage and occurrence in HR than SZ and HC, while microstate class B covered significantly more time in SZ compared to both HR and HC. Microstate class B displayed an aberrant spatial configuration in SZ, and to a lesser extent also in HR, compared to HC, with patients exhibiting significantly higher activity in the vicinity of the left posterior cingulate. Discussion Microstate abnormalities observed in HR were similar to those previously reported in acutely ill patients with schizophrenia. Moreover, there was evidence that HR and SZ might share specific disturbances in brain functional connectivity. These findings raise the possibility that certain abnormalities in resting-state EEG Microstates might be associated with an increased risk for psychosis.
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EEG Microstates During Resting Represent Personality Differences
Brain Topography, 2012Co-Authors: Felix Schlegel, Dietrich Lehmann, Pascal L. Faber, Patricia Milz, Lorena R. R. GianottiAbstract:We investigated the spontaneous brain electric activity of 13 skeptics and 16 believers in paranormal phenomena; they were university students assessed with a self-report scale about paranormal beliefs. 33-channel EEG recordings during no-task resting were processed as sequences of momentary potential distribution maps. Based on the maps at peak times of Global Field Power, the sequences were parsed into segments of quasi-stable potential distribution, the ‘Microstates’. The Microstates were clustered into four classes of map topographies (A–D). Analysis of the microstate parameters time coverage, occurrence frequency and duration as well as the temporal sequence (syntax) of the microstate classes revealed significant differences: Believers had a higher coverage and occurrence of class B, tended to decreased coverage and occurrence of class C, and showed a predominant sequence of microstate concatenations from A to C to B to A that was reversed in skeptics (A to B to C to A). Microstates of different topographies, putative “atoms of thought”, are hypothesized to represent different types of information processing.The study demonstrates that personality differences can be detected in resting EEG microstate parameters and microstate syntax. Microstate analysis yielded no conclusive evidence for the hypothesized relation between paranormal belief and schizophrenia.
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Classes of Multichannel EEG Microstates in Light and Deep Hypnotic Conditions
Brain Topography, 2007Co-Authors: Hitoshi Katayama, Lorena R. R. Gianotti, Toshiaki Isotani, Pascal L. Faber, Kyohei Sasada, Toshihiko Kinoshita, Dietrich LehmannAbstract:The study assessed the brain electric mechanisms of light and deep hypnotic conditions in the framework of EEG temporal Microstates. Multichannel EEG of healthy volunteers during initial resting, light hypnosis, deep hypnosis, and eventual recovery was analyzed into temporal EEG Microstates of four classes. Microstates are defined by the spatial configuration of their potential distribution maps (‹potential landscapes’) on the head surface. Because different potential landscapes must have been generated by different active neural assemblies, it is reasonable to assume that they also incorporate different brain functions. The observed four microstate classes were very similar to the four standard microstate classes A, B, C, D [Koenig, T. et al. Neuroimage, 2002;16: 41–8] and were labeled correspondingly. We expected a progression of microstate characteristics from initial resting to light to deep hypnosis. But, all three microstate parameters (duration, occurrence/second and %time coverage) yielded values for initial resting and final recovery that were between those of the two hypnotic conditions of light and deep hypnosis. Microstates of the classes B and D showed decreased duration, occurrence/second and %time coverage in deep hypnosis compared to light hypnosis; this was contrary to Microstates of classes A and C which showed increased values of all three parameters. Reviewing the available information about Microstates in other conditions, the changes from resting to light hypnosis in certain respects are reminiscent of changes to meditation states, and changes to deep hypnosis of those in schizophrenic states.
Pascal L. Faber - One of the best experts on this subject based on the ideXlab platform.
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EEG Microstates during different phases of Transcendental Meditation practice
Cognitive processing, 2017Co-Authors: Pascal L. Faber, Patricia Milz, Frederick Travis, Niyazi ParimAbstract:Two phases of Transcendental Meditation (TM)—transcending and undirected mentation—were compared to each other and to task-free resting using multichannel EEG recorded from 20 TM practitioners. An EEG microstate analysis identified four classes of Microstates which were labeled A, B, C and D, based on their similarity to previously published classes. For each class of Microstates, mean duration, coverage and occurrence were computed. Resting and transcending differed from undirected mentation with decreased prominence of Class A and increased prominence of Class D Microstates. In addition, transcending showed decreased prominence of Class C Microstates compared to undirected mentation. Based on previous findings on the functional significance of the microstate classes, the results indicate an increased reference to reality and decreased visualization during resting and transcending compared to undirected mentation. Also, our results indicate decreased saliency of internally generated mentations during transcending compared to undirected mentation reflecting a more detached and less evaluative processing. It is proposed that the continuous cycling through these two phases of meditation during a TM session might facilitate and train the flexible modulation of the parameters of these Microstates of these particular classes which are known to be altered in psychiatric disorders. This might promote beneficial stabilizing effects for the practitioner of TM.
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The resting microstate networks (RMN): cortical distributions, dynamics, and frequency specific information flow
arXiv: Neurons and Cognition, 2014Co-Authors: Roberto D. Pascual-marqui, Toshiaki Isotani, Pascal L. Faber, Dietrich Lehmann, K Kochi, Patricia Milz, Masafumi Yoshimura, Keiichiro Nishida, Toshihiko KinoshitaAbstract:A brain microstate is characterized by a unique, fixed spatial distribution of electrically active neurons with time varying amplitude. It is hypothesized that a microstate implements a functional/physiological state of the brain during which specific neural computations are performed. Based on this hypothesis, brain electrical activity is modeled as a time sequence of non-overlapping Microstates with variable, finite durations (Lehmann and Skrandies 1980, 1984; Lehmann et al 1987). In this study, EEG recordings from 109 participants during eyes closed resting condition are modeled with four Microstates. In a first part, a new confirmatory statistics method is introduced for the determination of the cortical distributions of electric neuronal activity that generate each microstate. All Microstates have common posterior cingulate generators, while three Microstates additionally include activity in the left occipital/parietal, right occipital/parietal, and anterior cingulate cortices. This appears to be a fragmented version of the metabolically (PET/fMRI) computed default mode network (DMN), supporting the notion that these four regions activate sequentially at high time resolution, and that slow metabolic imaging corresponds to a low-pass filtered version. In the second part of this study, the microstate amplitude time series are used as the basis for estimating the strength, directionality, and spectral characteristics (i.e., which oscillations are preferentially transmitted) of the connections that are mediated by the microstate transitions. The results show that the posterior cingulate is an important hub, sending alpha and beta oscillatory information to all other microstate generator regions. Interestingly, beyond alpha, beta oscillations are essential in the maintenance of the brain during resting state.
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Resting-state connectivity in the prodromal phase of schizophrenia: insights from EEG Microstates.
Schizophrenia research, 2014Co-Authors: Christina Andreou, Pascal L. Faber, Dietrich Lehmann, Gregor Leicht, Daniel Schoettle, Nenad Polomac, Ileana L Hanganu-opatz, Christoph MulertAbstract:Resting-state EEG Microstates are thought to reflect the momentary local states and interactions of distributed neural networks in the brain. Several changes in resting-state EEG Microstates have been described in acutely ill patients with schizophrenia, but it is not known whether these represent trait or state abnormalities. The present study aimed to investigate this issue by assessing EEG microstate characteristics in high-risk individuals (HR) and clinically stable first-episode patients with schizophrenia (SZ) with low symptom levels, compared to each other and healthy controls (HC). Participants were 18 HR, 18 SZ, and 22 HC subjects. 64-channel resting-state EEG recordings were used for microstate analyses. Microstates were clustered into four classes (A-D) according to their topography. Temporal parameters and topographies of Microstates were compared among groups. Microstate class A displayed higher coverage and occurrence in HR than SZ and HC, while microstate class B covered significantly more time in SZ compared to both HR and HC. Microstate class B displayed an aberrant spatial configuration in SZ, and to a lesser extent also in HR, compared to HC, with patients exhibiting significantly higher activity in the vicinity of the left posterior cingulate. Microstate abnormalities observed in HR were similar to those previously reported in acutely ill patients with schizophrenia. Moreover, there was evidence that HR and SZ might share specific disturbances in brain functional connectivity. These findings raise the possibility that certain abnormalities in resting-state EEG Microstates might be associated with an increased risk for psychosis. Copyright © 2013 Elsevier B.V. All rights reserved.
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Resting-state connectivity in the prodromal phase of schizophrenia: insights from EEG Microstates.
Schizophrenia Research, 2014Co-Authors: Christina Andreou, Pascal L. Faber, Dietrich Lehmann, Gregor Leicht, Daniel Schoettle, Nenad Polomac, Ileana L Hanganu-opatz, Christoph MulertAbstract:Abstract Introduction Resting-state EEG Microstates are thought to reflect the momentary local states and interactions of distributed neural networks in the brain. Several changes in resting-state EEG Microstates have been described in acutely ill patients with schizophrenia, but it is not known whether these represent trait or state abnormalities. The present study aimed to investigate this issue by assessing EEG microstate characteristics in high-risk individuals (HR) and clinically stable first-episode patients with schizophrenia (SZ) with low symptom levels, compared to each other and healthy controls (HC). Method Participants were 18 HR, 18 SZ, and 22 HC subjects. 64-channel resting-state EEG recordings were used for microstate analyses. Microstates were clustered into four classes (A–D) according to their topography. Temporal parameters and topographies of Microstates were compared among groups. Results Microstate class A displayed higher coverage and occurrence in HR than SZ and HC, while microstate class B covered significantly more time in SZ compared to both HR and HC. Microstate class B displayed an aberrant spatial configuration in SZ, and to a lesser extent also in HR, compared to HC, with patients exhibiting significantly higher activity in the vicinity of the left posterior cingulate. Discussion Microstate abnormalities observed in HR were similar to those previously reported in acutely ill patients with schizophrenia. Moreover, there was evidence that HR and SZ might share specific disturbances in brain functional connectivity. These findings raise the possibility that certain abnormalities in resting-state EEG Microstates might be associated with an increased risk for psychosis.
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EEG Microstates During Resting Represent Personality Differences
Brain Topography, 2012Co-Authors: Felix Schlegel, Dietrich Lehmann, Pascal L. Faber, Patricia Milz, Lorena R. R. GianottiAbstract:We investigated the spontaneous brain electric activity of 13 skeptics and 16 believers in paranormal phenomena; they were university students assessed with a self-report scale about paranormal beliefs. 33-channel EEG recordings during no-task resting were processed as sequences of momentary potential distribution maps. Based on the maps at peak times of Global Field Power, the sequences were parsed into segments of quasi-stable potential distribution, the ‘Microstates’. The Microstates were clustered into four classes of map topographies (A–D). Analysis of the microstate parameters time coverage, occurrence frequency and duration as well as the temporal sequence (syntax) of the microstate classes revealed significant differences: Believers had a higher coverage and occurrence of class B, tended to decreased coverage and occurrence of class C, and showed a predominant sequence of microstate concatenations from A to C to B to A that was reversed in skeptics (A to B to C to A). Microstates of different topographies, putative “atoms of thought”, are hypothesized to represent different types of information processing.The study demonstrates that personality differences can be detected in resting EEG microstate parameters and microstate syntax. Microstate analysis yielded no conclusive evidence for the hypothesized relation between paranormal belief and schizophrenia.
Christina Andreou - One of the best experts on this subject based on the ideXlab platform.
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EEG Microstates as biomarker for psychosis in ultra-high-risk patients
Translational psychiatry, 2020Co-Authors: Renate De Bock, Amatya J Mackintosh, Franziska Maier, Stefan Borgwardt, Anita Riecher-rössler, Christina AndreouAbstract:Resting-state EEG Microstates are brief (50-100 ms) periods, in which the spatial configuration of scalp global field power remains quasi-stable before rapidly shifting to another configuration. Changes in microstate parameters have been described in patients with psychotic disorders. These changes have also been observed in individuals with a clinical or genetic high risk, suggesting potential usefulness of EEG Microstates as a biomarker for psychotic disorders. The present study aimed to investigate the potential of EEG Microstates as biomarkers for psychotic disorders and future transition to psychosis in patients at ultra-high-risk (UHR). We used 19-channel clinical EEG recordings and orthogonal contrasts to compare temporal parameters of four normative microstate classes (A-D) between patients with first-episode psychosis (FEP; n = 29), UHR patients with (UHR-T; n = 20) and without (UHR-NT; n = 34) later transition to psychosis, and healthy controls (HC; n = 25). Microstate A was increased in patients (FEP & UHR-T & UHR-NT) compared to HC, suggesting an unspecific state biomarker of general psychopathology. Microstate B displayed a decrease in FEP compared to both UHR patient groups, and thus may represent a state biomarker specific to psychotic illness progression. Microstate D was significantly decreased in UHR-T compared to UHR-NT, suggesting its potential as a selective biomarker of future transition in UHR patients.
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Resting-state connectivity in the prodromal phase of schizophrenia: insights from EEG Microstates.
Schizophrenia research, 2014Co-Authors: Christina Andreou, Pascal L. Faber, Dietrich Lehmann, Gregor Leicht, Daniel Schoettle, Nenad Polomac, Ileana L Hanganu-opatz, Christoph MulertAbstract:Resting-state EEG Microstates are thought to reflect the momentary local states and interactions of distributed neural networks in the brain. Several changes in resting-state EEG Microstates have been described in acutely ill patients with schizophrenia, but it is not known whether these represent trait or state abnormalities. The present study aimed to investigate this issue by assessing EEG microstate characteristics in high-risk individuals (HR) and clinically stable first-episode patients with schizophrenia (SZ) with low symptom levels, compared to each other and healthy controls (HC). Participants were 18 HR, 18 SZ, and 22 HC subjects. 64-channel resting-state EEG recordings were used for microstate analyses. Microstates were clustered into four classes (A-D) according to their topography. Temporal parameters and topographies of Microstates were compared among groups. Microstate class A displayed higher coverage and occurrence in HR than SZ and HC, while microstate class B covered significantly more time in SZ compared to both HR and HC. Microstate class B displayed an aberrant spatial configuration in SZ, and to a lesser extent also in HR, compared to HC, with patients exhibiting significantly higher activity in the vicinity of the left posterior cingulate. Microstate abnormalities observed in HR were similar to those previously reported in acutely ill patients with schizophrenia. Moreover, there was evidence that HR and SZ might share specific disturbances in brain functional connectivity. These findings raise the possibility that certain abnormalities in resting-state EEG Microstates might be associated with an increased risk for psychosis. Copyright © 2013 Elsevier B.V. All rights reserved.
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Resting-state connectivity in the prodromal phase of schizophrenia: insights from EEG Microstates.
Schizophrenia Research, 2014Co-Authors: Christina Andreou, Pascal L. Faber, Dietrich Lehmann, Gregor Leicht, Daniel Schoettle, Nenad Polomac, Ileana L Hanganu-opatz, Christoph MulertAbstract:Abstract Introduction Resting-state EEG Microstates are thought to reflect the momentary local states and interactions of distributed neural networks in the brain. Several changes in resting-state EEG Microstates have been described in acutely ill patients with schizophrenia, but it is not known whether these represent trait or state abnormalities. The present study aimed to investigate this issue by assessing EEG microstate characteristics in high-risk individuals (HR) and clinically stable first-episode patients with schizophrenia (SZ) with low symptom levels, compared to each other and healthy controls (HC). Method Participants were 18 HR, 18 SZ, and 22 HC subjects. 64-channel resting-state EEG recordings were used for microstate analyses. Microstates were clustered into four classes (A–D) according to their topography. Temporal parameters and topographies of Microstates were compared among groups. Results Microstate class A displayed higher coverage and occurrence in HR than SZ and HC, while microstate class B covered significantly more time in SZ compared to both HR and HC. Microstate class B displayed an aberrant spatial configuration in SZ, and to a lesser extent also in HR, compared to HC, with patients exhibiting significantly higher activity in the vicinity of the left posterior cingulate. Discussion Microstate abnormalities observed in HR were similar to those previously reported in acutely ill patients with schizophrenia. Moreover, there was evidence that HR and SZ might share specific disturbances in brain functional connectivity. These findings raise the possibility that certain abnormalities in resting-state EEG Microstates might be associated with an increased risk for psychosis.