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Thomas Koenig - One of the best experts on this subject based on the ideXlab platform.

  • EEG Microstates and Psychosocial Stress During an Exchange Year
    Brain Topography, 2020
    Co-Authors: Nursija Kadier, Maria Stein, Thomas Koenig
    Abstract:

    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.

  • Investigating the temporal dynamics of electroencephalogram (EEG) Microstates using recurrent neural networks.
    Human brain mapping, 2020
    Co-Authors: Apoorva Sikka, Hamidreza Jamalabadi, M Krylova, Sarah Alizadeh, Johan Van Der Meer, Lena Danyeli, Matthias Deliano, Petya Vicheva, Tim Hahn, Thomas Koenig
    Abstract:

    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.

  • towards using Microstate neurofeedback for the treatment of psychotic symptoms in schizophrenia a feasibility study in healthy participants
    Brain Topography, 2016
    Co-Authors: Laura Diaz Hernandez, Kathryn Rieger, Anja Baenninger, Daniel Brandeis, Thomas Koenig
    Abstract:

    Spontaneous EEG signal can be parsed into sub-second periods of stable functional states (Microstates) that assumingly correspond to brief large scale synchronization events. In schizophrenia, a specific class of Microstate (class "D") has been found to be shorter than in healthy controls and to be correlated with positive symptoms. To explore potential new treatment options in schizophrenia, we tested in healthy controls if neurofeedback training to self-regulate Microstate D presence is feasible and what learning patterns are observed. Twenty subjects underwent EEG-neurofeedback training to up-regulate Microstate D presence. The protocol included 20 training sessions, consisting of baseline trials (resting state), regulation trials with auditory feedback contingent on Microstate D presence, and a transfer trial. Response to neurofeedback was assessed with mixed effects modelling. All participants increased the percentage of time spent producing Microstate D in at least one of the three conditions (p < 0.05). Significant between-subjects across-sessions results showed an increase of 0.42 % of time spent producing Microstate D in baseline (reflecting a sustained change in the resting state), 1.93 % of increase during regulation and 1.83 % during transfer. Within-session analysis (performed in baseline and regulation trials only) showed a significant 1.65 % increase in baseline and 0.53 % increase in regulation. These values are in a range that is expected to have an impact upon psychotic experiences. Additionally, we found a negative correlation between alpha power and Microstate D contribution during neurofeedback training. Given that Microstate D has been related to attentional processes, this result provides further evidence that the training was to some degree specific for the attentional network. We conclude that Microstate-neurofeedback training proved feasible in healthy subjects. The implementation of the same protocol in schizophrenia patients may promote skills useful to reduce positive symptoms by means of EEG-neurofeedback.

  • 15 years of Microstate research in schizophrenia where are we a meta analysis
    Frontiers in Psychiatry, 2016
    Co-Authors: Kathryn Rieger, Laura Diaz Hernandez, Anja Baenninger, Thomas Koenig
    Abstract:

    Schizophrenia patients show abnormalities in a broad range of task demands. Therefore, an explanation common to all these abnormalities has to be sought independently of any particular task, ideally in the brain dynamics before a task takes place or during resting state. For the neurobiological investigation of such baseline states, EEG Microstate analysis is particularly well suited, because it identifies subsecond global states of stable connectivity patterns directly related to the recruitment of different types of information processing modes (e.g., integration of top-down and bottom-up information). Meanwhile, there is an accumulation of evidence that particular Microstate networks are selectively affected in schizophrenia. To obtain an overall estimate of the effect size of these Microstate abnormalities, we present a systematic meta-analysis over all studies available to date relating EEG Microstates to schizophrenia. Results showed medium size effects for two classes of Microstates, namely, a class labeled C that was found to be more frequent in schizophrenia and a class labeled D that was found to be shortened. These abnormalities may correspond to core symptoms of schizophrenia, e.g., insufficient reality testing and self-monitoring as during auditory verbal hallucinations. As interventional studies have shown that these Microstate features may be systematically affected using antipsychotic drugs or neurofeedback interventions, these findings may help introducing novel diagnostic and treatment options.

  • The functional significance of EEG Microstates--Associations with modalities of thinking
    NeuroImage, 2015
    Co-Authors: Patricia Milz, Pascal L. Faber, Dietrich Lehmann, Thomas Koenig, Kieko Kochi, Roberto D. Pascual-marqui
    Abstract:

    The momentary, global functional state of the brain is reflected by its electric field configuration. Cluster analytical approaches consistently extracted four head-surface brain electric field configurations that optimally explain the variance of their changes across time in spontaneous EEG recordings. These four configurations are referred to as EEG Microstate classes A, B, C, and D and have been associated with verbal/phonological, visual, subjective interoceptive-autonomic processing, and attention reorientation, respectively. The present study tested these associations via an intra-individual and inter-individual analysis approach. The intra-individual approach tested the effect of task-induced increased modality-specific processing on EEG Microstate parameters. The inter-individual approach tested the effect of personal modality-specific parameters on EEG Microstate parameters. We obtained multichannel EEG from 61 healthy, right-handed, male students during four eyes-closed conditions: object-visualization, spatial-visualization, verbalization (6 runs each), and resting (7 runs). After each run, we assessed participants' degrees of object-visual, spatial-visual, and verbal thinking using subjective reports. Before and after the recording, we assessed modality-specific cognitive abilities and styles using nine cognitive tests and two questionnaires. The EEG of all participants, conditions, and runs was clustered into four classes of EEG Microstates (A, B, C, and D). RMANOVAs, ANOVAs and post-hoc paired t-tests compared Microstate parameters between conditions. TANOVAs compared Microstate class topographies between conditions. Differences were localized using eLORETA. Pearson correlations assessed interrelationships between personal modality-specific parameters and EEG Microstate parameters during no-task resting. As hypothesized, verbal as opposed to visual conditions consistently affected the duration, occurrence, and coverage of Microstate classes A and B. Contrary to associations suggested by previous reports, parameters were increased for class A during visualization, and class B during verbalization. In line with previous reports, Microstate D parameters were increased during no-task resting compared to the three internal, goal-directed tasks. Topographic differences between conditions included particular sub-regions of components of the metabolic default mode network. Modality-specific personal parameters did not consistently correlate with Microstate parameters except verbal cognitive style which correlated negatively with Microstate class A duration and positively with class C occurrence. This is the first study that aimed to induce EEG Microstate class parameter changes based on their hypothesized functional significance. Beyond the associations of Microstate classes A and B with visual and verbal processing, respectively, our results suggest that a finely-tuned interplay between all four EEG Microstate classes is necessary for the continuous formation of visual and verbal thoughts. Our results point to the possibility that the EEG Microstate classes may represent the head-surface measured activity of intra-cortical sources primarily exhibiting inhibitory functions. However, additional studies are needed to verify and elaborate on this hypothesis.

Dietrich Lehmann - One of the best experts on this subject based on the ideXlab platform.

  • The functional significance of EEG Microstates--Associations with modalities of thinking
    NeuroImage, 2015
    Co-Authors: Patricia Milz, Pascal L. Faber, Dietrich Lehmann, Thomas Koenig, Kieko Kochi, Roberto D. Pascual-marqui
    Abstract:

    The momentary, global functional state of the brain is reflected by its electric field configuration. Cluster analytical approaches consistently extracted four head-surface brain electric field configurations that optimally explain the variance of their changes across time in spontaneous EEG recordings. These four configurations are referred to as EEG Microstate classes A, B, C, and D and have been associated with verbal/phonological, visual, subjective interoceptive-autonomic processing, and attention reorientation, respectively. The present study tested these associations via an intra-individual and inter-individual analysis approach. The intra-individual approach tested the effect of task-induced increased modality-specific processing on EEG Microstate parameters. The inter-individual approach tested the effect of personal modality-specific parameters on EEG Microstate parameters. We obtained multichannel EEG from 61 healthy, right-handed, male students during four eyes-closed conditions: object-visualization, spatial-visualization, verbalization (6 runs each), and resting (7 runs). After each run, we assessed participants' degrees of object-visual, spatial-visual, and verbal thinking using subjective reports. Before and after the recording, we assessed modality-specific cognitive abilities and styles using nine cognitive tests and two questionnaires. The EEG of all participants, conditions, and runs was clustered into four classes of EEG Microstates (A, B, C, and D). RMANOVAs, ANOVAs and post-hoc paired t-tests compared Microstate parameters between conditions. TANOVAs compared Microstate class topographies between conditions. Differences were localized using eLORETA. Pearson correlations assessed interrelationships between personal modality-specific parameters and EEG Microstate parameters during no-task resting. As hypothesized, verbal as opposed to visual conditions consistently affected the duration, occurrence, and coverage of Microstate classes A and B. Contrary to associations suggested by previous reports, parameters were increased for class A during visualization, and class B during verbalization. In line with previous reports, Microstate D parameters were increased during no-task resting compared to the three internal, goal-directed tasks. Topographic differences between conditions included particular sub-regions of components of the metabolic default mode network. Modality-specific personal parameters did not consistently correlate with Microstate parameters except verbal cognitive style which correlated negatively with Microstate class A duration and positively with class C occurrence. This is the first study that aimed to induce EEG Microstate class parameter changes based on their hypothesized functional significance. Beyond the associations of Microstate classes A and B with visual and verbal processing, respectively, our results suggest that a finely-tuned interplay between all four EEG Microstate classes is necessary for the continuous formation of visual and verbal thoughts. Our results point to the possibility that the EEG Microstate classes may represent the head-surface measured activity of intra-cortical sources primarily exhibiting inhibitory functions. However, additional studies are needed to verify and elaborate on this hypothesis.

  • The resting Microstate networks (RMN): cortical distributions, dynamics, and frequency specific information flow
    arXiv: Neurons and Cognition, 2014
    Co-Authors: Roberto D. Pascual-marqui, Toshiaki Isotani, Pascal L. Faber, Dietrich Lehmann, K Kochi, Patricia Milz, Masafumi Yoshimura, Keiichiro Nishida, Toshihiko Kinoshita
    Abstract:

    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.

  • Resting-state connectivity in the prodromal phase of schizophrenia: insights from EEG Microstates.
    Schizophrenia research, 2014
    Co-Authors: Christina Andreou, Pascal L. Faber, Dietrich Lehmann, Gregor Leicht, Daniel Schoettle, Nenad Polomac, Ileana L Hanganu-opatz, Christoph Mulert
    Abstract:

    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.

  • Resting-state connectivity in the prodromal phase of schizophrenia: insights from EEG Microstates.
    Schizophrenia Research, 2014
    Co-Authors: Christina Andreou, Pascal L. Faber, Dietrich Lehmann, Gregor Leicht, Daniel Schoettle, Nenad Polomac, Ileana L Hanganu-opatz, Christoph Mulert
    Abstract:

    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.

  • EEG Microstates During Resting Represent Personality Differences
    Brain Topography, 2012
    Co-Authors: Felix Schlegel, Dietrich Lehmann, Pascal L. Faber, Patricia Milz, Lorena R. R. Gianotti
    Abstract:

    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.

Pascal L. Faber - One of the best experts on this subject based on the ideXlab platform.

  • EEG Microstates during different phases of Transcendental Meditation practice
    Cognitive processing, 2017
    Co-Authors: Pascal L. Faber, Patricia Milz, Frederick Travis, Niyazi Parim
    Abstract:

    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.

  • The functional significance of EEG Microstates--Associations with modalities of thinking
    NeuroImage, 2015
    Co-Authors: Patricia Milz, Pascal L. Faber, Dietrich Lehmann, Thomas Koenig, Kieko Kochi, Roberto D. Pascual-marqui
    Abstract:

    The momentary, global functional state of the brain is reflected by its electric field configuration. Cluster analytical approaches consistently extracted four head-surface brain electric field configurations that optimally explain the variance of their changes across time in spontaneous EEG recordings. These four configurations are referred to as EEG Microstate classes A, B, C, and D and have been associated with verbal/phonological, visual, subjective interoceptive-autonomic processing, and attention reorientation, respectively. The present study tested these associations via an intra-individual and inter-individual analysis approach. The intra-individual approach tested the effect of task-induced increased modality-specific processing on EEG Microstate parameters. The inter-individual approach tested the effect of personal modality-specific parameters on EEG Microstate parameters. We obtained multichannel EEG from 61 healthy, right-handed, male students during four eyes-closed conditions: object-visualization, spatial-visualization, verbalization (6 runs each), and resting (7 runs). After each run, we assessed participants' degrees of object-visual, spatial-visual, and verbal thinking using subjective reports. Before and after the recording, we assessed modality-specific cognitive abilities and styles using nine cognitive tests and two questionnaires. The EEG of all participants, conditions, and runs was clustered into four classes of EEG Microstates (A, B, C, and D). RMANOVAs, ANOVAs and post-hoc paired t-tests compared Microstate parameters between conditions. TANOVAs compared Microstate class topographies between conditions. Differences were localized using eLORETA. Pearson correlations assessed interrelationships between personal modality-specific parameters and EEG Microstate parameters during no-task resting. As hypothesized, verbal as opposed to visual conditions consistently affected the duration, occurrence, and coverage of Microstate classes A and B. Contrary to associations suggested by previous reports, parameters were increased for class A during visualization, and class B during verbalization. In line with previous reports, Microstate D parameters were increased during no-task resting compared to the three internal, goal-directed tasks. Topographic differences between conditions included particular sub-regions of components of the metabolic default mode network. Modality-specific personal parameters did not consistently correlate with Microstate parameters except verbal cognitive style which correlated negatively with Microstate class A duration and positively with class C occurrence. This is the first study that aimed to induce EEG Microstate class parameter changes based on their hypothesized functional significance. Beyond the associations of Microstate classes A and B with visual and verbal processing, respectively, our results suggest that a finely-tuned interplay between all four EEG Microstate classes is necessary for the continuous formation of visual and verbal thoughts. Our results point to the possibility that the EEG Microstate classes may represent the head-surface measured activity of intra-cortical sources primarily exhibiting inhibitory functions. However, additional studies are needed to verify and elaborate on this hypothesis.

  • The resting Microstate networks (RMN): cortical distributions, dynamics, and frequency specific information flow
    arXiv: Neurons and Cognition, 2014
    Co-Authors: Roberto D. Pascual-marqui, Toshiaki Isotani, Pascal L. Faber, Dietrich Lehmann, K Kochi, Patricia Milz, Masafumi Yoshimura, Keiichiro Nishida, Toshihiko Kinoshita
    Abstract:

    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.

  • Resting-state connectivity in the prodromal phase of schizophrenia: insights from EEG Microstates.
    Schizophrenia research, 2014
    Co-Authors: Christina Andreou, Pascal L. Faber, Dietrich Lehmann, Gregor Leicht, Daniel Schoettle, Nenad Polomac, Ileana L Hanganu-opatz, Christoph Mulert
    Abstract:

    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.

  • Resting-state connectivity in the prodromal phase of schizophrenia: insights from EEG Microstates.
    Schizophrenia Research, 2014
    Co-Authors: Christina Andreou, Pascal L. Faber, Dietrich Lehmann, Gregor Leicht, Daniel Schoettle, Nenad Polomac, Ileana L Hanganu-opatz, Christoph Mulert
    Abstract:

    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.

Christoph M Michel - One of the best experts on this subject based on the ideXlab platform.

  • EEG Microstates of dreams.
    Scientific reports, 2020
    Co-Authors: Lucie Bréchet, Denis Brunet, Lampros Perogamvros, Giulio Tononi, Christoph M Michel
    Abstract:

    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.

  • Microstates in resting-state EEG: Current status and future directions
    Neuroscience and biobehavioral reviews, 2014
    Co-Authors: Arjun Khanna, Christoph M Michel, Alvaro Pascual-leone, Faranak Farzan
    Abstract:

    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.

  • deviant dynamics of eeg resting state pattern in 22q11 2 deletion syndrome adolescents a vulnerability marker of schizophrenia
    Schizophrenia Research, 2014
    Co-Authors: Miralena I Tomescu, Tonia A Rihs, Robert Becker, Juliane Britz, Anna Custo, Frederic Grouiller, Maude Schneider, Martin Debbane, Stephan Eliez, Christoph M Michel
    Abstract:

    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.

  • EEG Microstates of wakefulness and NREM sleep.
    NeuroImage, 2012
    Co-Authors: Verena Brodbeck, Christoph M Michel, Alena Kuhn, Frederic Von Wegner, Astrid Morzelewski, Enzo Tagliazucchi, S.v. Borisov, Helmut Laufs
    Abstract:

    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.

  • 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, 2010
    Co-Authors: Dimitri Van De Ville, J. Britz, Christoph M Michel
    Abstract:

    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.

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  • Gravitational Waves, Holography, and Black Hole Microstates
    2020
    Co-Authors: Vasil Dimitrov, Tom Lemmens, Daniel R. Mayerson, Bert Vercnocke
    Abstract:

    Gravitational wave observations of the near-horizon region of black holes lend insight into the quantum nature of gravity. In particular, gravitational wave echoes have been identified as a potential signature of quantum gravity-inspired structure near the horizon. In this paper, we connect such observables to the language of black hole Microstates in string theory and holography. To that end, we propose a toy model describing the AdS$_3$ near-horizon region of five-dimensional black holes, inspired by earlier work of Solodukhin. This model captures key features of recently constructed Microstate geometries, and allows us to make three observations. First, we relate the language of AdS/CFT, in particular the holographic retarded two-point correlator, to effective parameters modeling the structure that are used in flat space gravitational wave literature. Second, we find that for a typical Microstate, the `cap' of the microstructure is exponentially close to the horizon, making it an effective sub-Planckian correction to the black hole geometry, although the Microstate geometry itself is classical. Third, using a microcanonical ensemble average over geometries, we find support for the claim that the gravitational wave echo amplitude in a typical quantum Microstate of the black hole is exponentially suppressed by the black hole entropy.

  • Non-supersymmetric Microstates of the MSW system
    Journal of High Energy Physics, 2014
    Co-Authors: Souvik Banerjee, Borun D. Chowdhury, Bert Vercnocke, Amitabh Virmani
    Abstract:

    We present an analysis parallel to that of Giusto, Ross, and Saxena (arXiv:0708.3845)and construct a discrete family of non-supersymmetric Microstate geometries of the Maldacena-Strominger-Witten system. The supergravity configuration in which we look for the smooth Microstates is constructed using SO(4, 4) dualities applied to an appropriate seed solution. The SO(4, 4) approach offers certain technical advantages. Our Microstate solutions are smooth in five dimensions, as opposed to all previously known non-supersymmetric Microstates with AdS(3) cores, which are smooth only in six dimensions. The decoupled geometries for our Microstates are related to global AdS(3) x S-2 by spectral flows.

  • Camouflaged supersymmetry in solutions of extended supergravities
    Physical Review D, 2012
    Co-Authors: Iosif Bena, Hagen Triendl, Bert Vercnocke
    Abstract:

    We establish a relation between certain classes of flux compactifications and certain families of black hole Microstate solutions. This connection reveals a rather unexpected result: there exist supersymmetric solutions of N = 8 supergravity that live inside many N = 2 truncations, but are not supersymmetric inside any of them. If this phenomenon is generic, it indicates the possible existence of much larger families of supersymmetric black rings and black hole Microstates than previously thought.