1
|
Tomescu MI, Van der Donck S, Perisanu EM, Berceanu AI, Alaerts K, Boets B, Carcea I. Social functioning predicts individual changes in EEG microstates following intranasal oxytocin administration: A double-blind, cross-over randomized clinical trial. Psychophysiology 2024; 61:e14581. [PMID: 38594888 DOI: 10.1111/psyp.14581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 03/20/2024] [Accepted: 03/21/2024] [Indexed: 04/11/2024]
Abstract
Oxytocin (OXT) modulates social behaviors. However, the administration of exogenous OXT in humans produces inconsistent behavioral changes, affecting future consideration of OXT as a treatment for autism and other disorders with social symptoms. Inter-individual variability in social functioning traits might play a key role in how OXT changes brain activity and, therefore, behavior. Here, we investigated if inter-individual variability might dictate how single-dose intranasal OXT administration (IN-OXT) changes spontaneous neural activity during the eyes-open resting state. We used a double-blinded, randomized, placebo-controlled, cross-over design on 30 typically developing young adult men to investigate the dynamics of EEG microstates corresponding to activity in defined neural networks. We confirmed previous reports that, at the group level, IN-OXT increases the representation of the attention and salience microstates. Furthermore, we identified a decreased representation of microstates associated with the default mode network. Using multivariate partial least square statistical analysis, we found that social functioning traits associated with IN-OXT-induced changes in microstate dynamics in specific spectral bands. Correlation analysis further revealed that the higher the social functioning, the more IN-OXT increased the appearance of the visual network-associated microstate, and suppressed the appearance of a default mode network-related microstate. The lower the social functioning, the more IN-OXT increases the appearance of the salience microstate. The effects we report on the salience microstate support the hypothesis that OXT regulates behavior by enhancing social salience. Moreover, our findings indicate that social functioning traits modulate responses to IN-OXT and could partially explain the inconsistent reports on IN-OXT effects.
Collapse
Affiliation(s)
- Miralena I Tomescu
- Faculty of Educational Sciences, Department of Psychology, University "Stefan cel Mare" of Suceava, Bucharest, Romania
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania
- Faculty of Psychology and Educational Sciences, Department of Psychology, University of Bucharest, Bucharest, Romania
| | - Stephanie Van der Donck
- Center for Developmental Psychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Leuven Autism Research (LAuRes), KU Leuven, Leuven, Belgium
| | - Emanuela M Perisanu
- Institute of Cardiovascular Diseases, Timisoara, Romania
- Faculty of Medicine, University of Sibiu, Sibiu, Romania
| | - Alexandru I Berceanu
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania
| | - Kaat Alaerts
- Neuromodulation Laboratory, Research Group for Neurorehabilitation, KU Leuven, Leuven, Belgium
| | - Bart Boets
- Center for Developmental Psychiatry, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Leuven Autism Research (LAuRes), KU Leuven, Leuven, Belgium
| | - Ioana Carcea
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania
- Department of Pharmacology, Physiology, and Neuroscience, Rutgers Brain Health Institute, Rutgers, The State University of New Jersey, Newark, New Jersey, USA
| |
Collapse
|
2
|
Nazare K, Tomescu MI. Valence-specific EEG microstate modulations during self-generated affective states. Front Psychol 2024; 15:1300416. [PMID: 38855303 PMCID: PMC11160840 DOI: 10.3389/fpsyg.2024.1300416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 04/26/2024] [Indexed: 06/11/2024] Open
Abstract
Introduction This study aims to explore the temporal dynamics of brain networks involved in self-generated affective states, specifically focusing on modulating these states in both positive and negative valences. The overarching goal is to contribute to a deeper understanding of the neurodynamic patterns associated with affective regulation, potentially informing the development of biomarkers for therapeutic interventions in mood and anxiety disorders. Methods Utilizing EEG microstate analysis during self-generated affective states, we investigated the temporal dynamics of five distinct microstates across different conditions, including baseline resting state and self-generated states of positive valence (e.g., awe, contentment) and negative valence (e.g., anger, fear). Results The study revealed noteworthy modulations in microstate dynamics during affective states. Additionally, valence-specific mechanisms of spontaneous affective regulation were identified. Negative valence affective states were characterized by the heightened presence of attention-associated microstates and reduced occurrence of salience-related microstates during negative valence states. In contrast, positive valence affective states manifested a prevalence of microstates related to visual/autobiographical memory and a reduced presence of auditory/language-associated microstates compared to both baseline and negative valence states. Discussion This study contributes to the field by employing EEG microstate analysis to discern the temporal dynamics of brain networks involved in self-generated affective states. Insights from this research carry significant implications for understanding neurodynamic patterns in affective regulation. The identification of valence-specific modulations and mechanisms has potential applications in developing biomarkers for mood and anxiety disorders, offering novel avenues for therapeutic interventions.
Collapse
Affiliation(s)
- Karina Nazare
- CINETic Center, Department of Research and Development, National University of Theatre and Film “I.L. Caragiale”, Bucharest, Romania
- Faculty of Automatic Control and Computers, POLITEHNICA University of Bucharest, Bucharest, Romania
| | - Miralena I. Tomescu
- CINETic Center, Department of Research and Development, National University of Theatre and Film “I.L. Caragiale”, Bucharest, Romania
- Department of Psychology, Faculty of Educational Sciences, University “Stefan cel Mare” of Suceava, Suceava, Romania
| |
Collapse
|
3
|
Cao Q, Wang Y, Ji Y, He Z, Lei X. Resting-State EEG Reveals Abnormal Microstate Characteristics of Depression with Insomnia. Brain Topogr 2024; 37:388-396. [PMID: 36892651 DOI: 10.1007/s10548-023-00949-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 02/16/2023] [Indexed: 03/10/2023]
Abstract
Previous research revealed various aspects of resting-state EEG for depression and insomnia. However, the EEG characteristics of depressed subjects with insomnia are rarely studied, especially EEG microstates that capture the dynamic activities of the large-scale brain network. To fill these research gaps, the present study collected resting-state EEG data from 32 subclinical depression subjects with insomnia (SDI), 31 subclinical depression subjects without insomnia (SD), and 32 healthy controls (HCs). Four topographic maps were generated from clean EEG data after clustering and rearrangement. Temporal characteristics were obtained for statistical analysis, including cross-group variance analysis (ANOVA) and intra-group correlation analysis. In our study, the global clustering of all individuals in the EEG microstate analysis revealed the four previously discovered categories of microstates (A, B, C, and D). The occurrence of microstate B was lower in SDI than in SD and HC subjects. The correlation analysis showed that the total Pittsburgh Sleep Quality Index (PSQI) score negatively correlated with the occurrence of microstate C in SDI (r = - 0.415, p < 0.05). Conversely, there was a positive correlation between Self-rating Depression Scale (SDS) scores and the duration of microstate C in SD (r = 0.359, p < 0.05). These results indicate that microstates reflect altered large-scale brain network dynamics in subclinical populations. Abnormalities in the visual network corresponding to microstate B are an electrophysiological characteristic of subclinical individuals with symptoms of depressive insomnia. Further investigation is needed for microstate changes related to high arousal and emotional problems in people suffering from depression and insomnia.
Collapse
Affiliation(s)
- Qike Cao
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Yulin Wang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Yufang Ji
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Zhihui He
- The Ninth People's Hospital of Chongqing, Chongqing, 400700, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China.
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China.
| |
Collapse
|
4
|
Carbone GA, Michel CM, Farina B, Adenzato M, Ardito RB, Imperatori C, Artoni F. Altered EEG Patterns in Individuals with Disorganized Attachment: An EEG Microstates Study. Brain Topogr 2024; 37:420-431. [PMID: 38416284 DOI: 10.1007/s10548-024-01038-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 02/01/2024] [Indexed: 02/29/2024]
Abstract
Over the past years, different studies provided preliminary evidence that Disorganized Attachment (DA) may have dysregulatory and disintegrative effects on both autonomic arousal regulation and brain connectivity. However, despite the clinical relevance of this construct, few studies have investigated the specific alterations underlying DA using electroencephalography (EEG). Thus, the main aim of the current study was to investigate EEG microstate parameters of DA in a non-clinical sample (N = 50) before (pre) and after (post) the administration of the Adult Attachment Interview (AAI). Two EEG eyes-closed Resting State (RS) recordings were performed before and after the AAI, which was used for classifying the participants [i.e., Disorganized/Unresolved (D/U) or Organized/Resolved (O/R) individuals] and to trigger the attachment system. Microstates parameters (i.e., Mean Duration, Time Coverage and Occurrence) were extracted from each recording using Cartool software. EEG microstates clustering analysis revealed 6 different maps (labeled A, B, C, D, E, F) in both groups (i.e., D/U and O/R individuals) and in both conditions (i.e., pre-AAI and post-AAI). In the pre-AAI condition, compared to O/R individuals, D/U participants showed a shorter Mean Duration and Time Coverage of Map F; in the post-AAI condition, a significant reduction in the Mean Duration of Map E was also observed in D/U individuals. Finally, in the "within" statistical analysis (i.e., pre-AAI vs. post-AAI), only the D/U group exhibited a significant increase in Time Coverage of Map F after the AAI. Since these maps are associated with brain networks involved in emotional information processing and mentalization (i.e., Salience Network and Default Mode Network), our result might reflect the deficit in the ability to mentalize caregiver's interaction as well as the increased sensitivity to attachment-related stimuli typically observed in individuals with a D/U state of mind.
Collapse
Affiliation(s)
| | - Christoph M Michel
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Campus Biotech, Geneva, Switzerland
- Center for Biomedical Imaging (CIBM), Lausanne, Geneva, Switzerland
| | - Benedetto Farina
- Experimental and Applied Psychology Laboratory, Department of Human Sciences, European University of Rome, Rome, Italy
| | - Mauro Adenzato
- Department of Psychology, University of Turin, Turin, Italy
| | - Rita B Ardito
- Department of Psychology, University of Turin, Turin, Italy
| | - Claudio Imperatori
- Experimental and Applied Psychology Laboratory, Department of Human Sciences, European University of Rome, Rome, Italy.
| | - Fiorenzo Artoni
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Campus Biotech, Geneva, Switzerland
| |
Collapse
|
5
|
Deiber MP, Piguet C, Berchio C, Michel CM, Perroud N, Ros T. Resting-State EEG Microstates and Power Spectrum in Borderline Personality Disorder: A High-Density EEG Study. Brain Topogr 2024; 37:397-409. [PMID: 37776472 PMCID: PMC11026215 DOI: 10.1007/s10548-023-01005-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 08/30/2023] [Indexed: 10/02/2023]
Abstract
Borderline personality disorder (BPD) is a debilitating psychiatric condition characterized by emotional dysregulation, unstable sense of self, and impulsive, potentially self-harming behavior. In order to provide new neurophysiological insights on BPD, we complemented resting-state EEG frequency spectrum analysis with EEG microstates (MS) analysis to capture the spatiotemporal dynamics of large-scale neural networks. High-density EEG was recorded at rest in 16 BPD patients and 16 age-matched neurotypical controls. The relative power spectrum and broadband MS spatiotemporal parameters were compared between groups and their inter-correlations were examined. Compared to controls, BPD patients showed similar global spectral power, but exploratory univariate analyses on single channels indicated reduced relative alpha power and enhanced relative delta power at parietal electrodes. In terms of EEG MS, BPD patients displayed similar MS topographies as controls, indicating comparable neural generators. However, the MS temporal dynamics were significantly altered in BPD patients, who demonstrated opposite prevalence of MS C (lower than controls) and MS E (higher than controls). Interestingly, MS C prevalence correlated positively with global alpha power and negatively with global delta power, while MS E did not correlate with any measures of spectral power. Taken together, these observations suggest that BPD patients exhibit a state of cortical hyperactivation, represented by decreased posterior alpha power, together with an elevated presence of MS E, consistent with symptoms of elevated arousal and/or vigilance. This is the first study to investigate resting-state MS patterns in BPD, with findings of elevated MS E and the suggestion of reduced posterior alpha power indicating a disorder-specific neurophysiological signature previously unreported in a psychiatric population.
Collapse
Affiliation(s)
- Marie-Pierre Deiber
- Department of Psychiatry, University Hospitals of Geneva, Chemin du Petit-Bel-Air 2, 1226 Thônex, Geneva, Switzerland.
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
| | - Camille Piguet
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Department of Pediatrics, University Hospitals of Geneva, Geneva, Switzerland
| | - Cristina Berchio
- Department of Pediatrics, University of Geneva, Geneva, Switzerland
| | - Christoph M Michel
- Functional Brain Mapping Laboratory, Department of Fundamental Neuroscience, University of Geneva, Geneva, Switzerland
- Center for Biomedical Imaging, CIBM, Lausanne, Switzerland
| | - Nader Perroud
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Psychiatric Specialties, Department of Psychiatry, University Hospitals of Geneva, Geneva, Switzerland
| | - Tomas Ros
- Department of Psychiatry, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Center for Biomedical Imaging, CIBM, Lausanne, Switzerland
- Department of Neuroscience, University of Geneva, Geneva, Switzerland
| |
Collapse
|
6
|
Zarka D, Cevallos C, Ruiz P, Petieau M, Cebolla AM, Bengoetxea A, Cheron G. Electroencephalography microstates highlight specific mindfulness traits. Eur J Neurosci 2024; 59:1753-1769. [PMID: 38221503 DOI: 10.1111/ejn.16247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/29/2023] [Accepted: 12/18/2023] [Indexed: 01/16/2024]
Abstract
The present study aimed to investigate the spontaneous dynamics of large-scale brain networks underlying mindfulness as a dispositional trait, through resting-state electroencephalography (EEG) microstates analysis. Eighteen participants had attended a standardized mindfulness-based stress reduction training (MBSR), and 18 matched waitlist individuals (CTRL) were recorded at rest while they were passively exposed to auditory stimuli. Participants' mindfulness traits were assessed with the Five Facet Mindfulness Questionnaire (FFMQ). To further explore the relationship between microstate dynamics at rest and mindfulness traits, participants were also asked to rate their experience according to five phenomenal dimensions. After training, MBSR participants showed a highly significant increase in FFMQ score, as well as higher observing and non-reactivity FFMQ sub-scores than CTRL participants. Microstate analysis revealed four classes of microstates (A-D) in global clustering across all subjects. The MBSR group showed lower duration, occurrence and coverage of microstate C than the control group. Moreover, these microstate C parameters were negatively correlated to non-reactivity sub-scores of FFMQ across participants, whereas the microstate A occurrence was negatively correlated to FFMQ total score. Further analysis of participants' self-reports suggested that MBSR participants showed a better sensory-affective integration of auditory interferences. In line with previous studies, our results suggest that temporal dynamics of microstate C underlie specifically the non-reactivity trait of mindfulness. These findings encourage further research into microstates in the evaluation and monitoring of the impact of mindfulness-based interventions on the mental health and well-being of individuals.
Collapse
Affiliation(s)
- D Zarka
- Laboratory of Neurophysiology and Movement Biomechanics, Research Unit in Sciences of Osteopathy, Faculty of Human Motor Sciences, Université Libre de Bruxelles, Brussels, Belgium
- Research Unit in Sciences of Osteopathy, Faculty of Human Motor Sciences, Université Libre de Bruxelles, Brussels, Belgium
| | - C Cevallos
- Laboratory of Neurophysiology and Movement Biomechanics, Research Unit in Sciences of Osteopathy, Faculty of Human Motor Sciences, Université Libre de Bruxelles, Brussels, Belgium
- Departamento de Ingeniería Mecánica, Facultad de Ingeniería Mecánica, Escuela Politécnica Nacional, Quito, Ecuador
| | - P Ruiz
- Departamento de Ingeniería Mecánica, Facultad de Ingeniería Mecánica, Escuela Politécnica Nacional, Quito, Ecuador
| | - M Petieau
- Laboratory of Neurophysiology and Movement Biomechanics, Research Unit in Sciences of Osteopathy, Faculty of Human Motor Sciences, Université Libre de Bruxelles, Brussels, Belgium
| | - A M Cebolla
- Laboratory of Neurophysiology and Movement Biomechanics, Research Unit in Sciences of Osteopathy, Faculty of Human Motor Sciences, Université Libre de Bruxelles, Brussels, Belgium
| | - A Bengoetxea
- Research Unit in Sciences of Osteopathy, Faculty of Human Motor Sciences, Université Libre de Bruxelles, Brussels, Belgium
- Athenea Neuroclinics, San Sebastian, Spain
| | - G Cheron
- Laboratory of Neurophysiology and Movement Biomechanics, Research Unit in Sciences of Osteopathy, Faculty of Human Motor Sciences, Université Libre de Bruxelles, Brussels, Belgium
- Laboratory of Electrophysiology, Université de Mons, Mons, Belgium
| |
Collapse
|
7
|
Michel CM, Brechet L, Schiller B, Koenig T. Current State of EEG/ERP Microstate Research. Brain Topogr 2024; 37:169-180. [PMID: 38349451 PMCID: PMC10884048 DOI: 10.1007/s10548-024-01037-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 01/31/2024] [Indexed: 02/23/2024]
Abstract
The analysis of EEG microstates for investigating rapid whole-brain network dynamics during rest and tasks has become a standard practice in the EEG research community, leading to a substantial increase in publications across various affective, cognitive, social and clinical neuroscience domains. Recognizing the growing significance of this analytical method, the authors aim to provide the microstate research community with a comprehensive discussion on methodological standards, unresolved questions, and the functional relevance of EEG microstates. In August 2022, a conference was hosted in Bern, Switzerland, which brought together many researchers from 19 countries. During the conference, researchers gave scientific presentations and engaged in roundtable discussions aiming at establishing steps toward standardizing EEG microstate analysis methods. Encouraged by the conference's success, a special issue was launched in Brain Topography to compile the current state-of-the-art in EEG microstate research, encompassing methodological advancements, experimental findings, and clinical applications. The call for submissions for the special issue garnered 48 contributions from researchers worldwide, spanning reviews, meta-analyses, tutorials, and experimental studies. Following a rigorous peer-review process, 33 papers were accepted whose findings we will comprehensively discuss in this Editorial.
Collapse
Affiliation(s)
- Christoph M Michel
- Functional Brain Mapping Lab, Department of Basic Neurosciences, Medical Faculty, University of Geneva, Geneva, Switzerland.
- Center for Biomedical Imaging (CIBM), Lausanne, Geneva, Switzerland.
| | - Lucie Brechet
- Department of Readaptation and Geriatrics, Medical Faculty, University of Geneva, Geneva, Switzerland
| | - Bastian Schiller
- Laboratory for Biological Psychology, Clinical Psychology, and Psychotherapy, Albert-Ludwigs-University of Freiburg, Freiburg, Germany
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| |
Collapse
|
8
|
Tarailis P, Koenig T, Michel CM, Griškova-Bulanova I. The Functional Aspects of Resting EEG Microstates: A Systematic Review. Brain Topogr 2024; 37:181-217. [PMID: 37162601 DOI: 10.1007/s10548-023-00958-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 04/11/2023] [Indexed: 05/11/2023]
Abstract
A growing body of clinical and cognitive neuroscience studies have adapted a broadband EEG microstate approach to evaluate the electrical activity of large-scale cortical networks. However, the functional aspects of these microstates have not yet been systematically reviewed. Here, we present an overview of the existing literature and systematize the results to provide hints on the functional role of electrical brain microstates. Studies that evaluated and manipulated the temporal properties of resting-state microstates and utilized questionnaires, task-initiated thoughts, specific tasks before or between EEG session(s), pharmacological interventions, neuromodulation approaches, or localized sources of the extracted microstates were selected. Fifty studies that met the inclusion criteria were included. A new microstate labeling system has been proposed for a comprehensible comparison between the studies, where four classical microstates are referred to as A-D, and the others are labeled by the frequency of their appearance. Microstate A was associated with both auditory and visual processing and links to subjects' arousal/arousability. Microstate B showed associations with visual processing related to self, self-visualization, and autobiographical memory. Microstate C was related to processing personally significant information, self-reflection, and self-referential internal mentation rather than autonomic information processing. In contrast, microstate E was related to processing interoceptive and emotional information and to the salience network. Microstate D was associated with executive functioning. Microstate F is suggested to be a part of the Default Mode Network and plays a role in personally significant information processing, mental simulations, and theory of mind. Microstate G is potentially linked to the somatosensory network.
Collapse
Affiliation(s)
- Povilas Tarailis
- Life Sciences Centre, Institute of Biosciences, Vilnius University, Vilnius, Lithuania
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Christoph M Michel
- Functional Brain Mapping Laboratory, Department of Fundamental Neuroscience, University of Geneva, Geneva, Switzerland
- Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| | | |
Collapse
|
9
|
Koenig T, Diezig S, Kalburgi SN, Antonova E, Artoni F, Brechet L, Britz J, Croce P, Custo A, Damborská A, Deolindo C, Heinrichs M, Kleinert T, Liang Z, Murphy MM, Nash K, Nehaniv C, Schiller B, Smailovic U, Tarailis P, Tomescu M, Toplutaş E, Vellante F, Zanesco A, Zappasodi F, Zou Q, Michel CM. EEG-Meta-Microstates: Towards a More Objective Use of Resting-State EEG Microstate Findings Across Studies. Brain Topogr 2024; 37:218-231. [PMID: 37515678 PMCID: PMC10884358 DOI: 10.1007/s10548-023-00993-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 07/16/2023] [Indexed: 07/31/2023]
Abstract
Over the last decade, EEG resting-state microstate analysis has evolved from a niche existence to a widely used and well-accepted methodology. The rapidly increasing body of empirical findings started to yield overarching patterns of associations of biological and psychological states and traits with specific microstate classes. However, currently, this cross-referencing among apparently similar microstate classes of different studies is typically done by "eyeballing" of printed template maps by the individual authors, lacking a systematic procedure. To improve the reliability and validity of future findings, we present a tool to systematically collect the actual data of template maps from as many published studies as possible and present them in their entirety as a matrix of spatial similarity. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps from ongoing or published studies. The tool also allows importing novel template maps and systematically extracting the findings associated with specific microstate maps in the literature. The analysis of 40 included sets of template maps indicated that: (i) there is a high degree of similarity of template maps across studies, (ii) similar template maps were associated with converging empirical findings, and (iii) representative meta-microstates can be extracted from the individual studies. We hope that this tool will be useful in coming to a more comprehensive, objective, and overarching representation of microstate findings.
Collapse
Affiliation(s)
- Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland.
- Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Huddinge, Sweden.
- Children's Hospital Los Angeles, The Saban Research Institute, Los Angeles, CA, 90027, USA.
| | - Sarah Diezig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | | | - Elena Antonova
- Division of Psychology, Department of Life Sciences, College of Health, Medicine and Life Sciences & Centre for Cognitive Neuroscience, Brunel University London, Kingston Lane, Uxbridge, UB8 3PH, UK
| | - Fiorenzo Artoni
- Human Neuron Lab, Faculty of Medicine, Department of Clinical Neurosciences, University of Geneva, Geneva, Switzerland
| | - Lucie Brechet
- Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, Geneva, 1202, Switzerland
| | - Juliane Britz
- Department of Psychology, University of Fribourg, Fribourg, Switzerland
| | - Pierpaolo Croce
- Department of Neurosciences, Imaging and Clinical Sciences, Behavioral Imaging and Neural Dynamics Center, Institute for Advanced Biomedical Technologies, "Gabriele d'Annunzio" University, Chieti, 66100, Italy
| | - Anna Custo
- Department of Nuclear Medicine, Geneva University Hospital (HUG), Geneva, Switzerland
| | - Alena Damborská
- Department of Psychiatry, Faculty of Medicine, University Hospital Brno, Masaryk University, Brno, Czechia
| | - Camila Deolindo
- Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
| | - Markus Heinrichs
- Department of Psychology, Laboratory for Biological Psychology, Clinical Psychology and Psychotherapy, Albert-Ludwigs-University of Freiburg, Breisgau, Germany
| | - Tobias Kleinert
- Department of Psychology, Laboratory for Biological Psychology, Clinical Psychology and Psychotherapy, Albert-Ludwigs-University of Freiburg, Breisgau, Germany
- Department of Ergonomics, Leibniz Research Centre for Working Environment and Human Factors, Ardeystr. 67, Dortmund, 44139, Germany
| | - Zhen Liang
- School of Biomedical Engineering, Medical School, Shenzhen University, Shenzhen, China
| | - Michael M Murphy
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- McLean Hospital, Belmont, MA, USA
| | - Kyle Nash
- Department of Psychology, University of Alberta, Edmonton, AB, T6G 2E9, Canada
| | - Chrystopher Nehaniv
- Departments of Systems Design Engineering and Electrical & Computer Engineering, University of Waterloo, 200 University Avenue W, Waterloo, ON, N2L 3G1, Canada
| | - Bastian Schiller
- Department of Psychology, Laboratory for Biological Psychology, Clinical Psychology and Psychotherapy, Albert-Ludwigs-University of Freiburg, Breisgau, Germany
| | - Una Smailovic
- Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Huddinge, Sweden
- Department of Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden
| | - Povilas Tarailis
- Life Sciences Centre, Institute of Biosciences, Vilnius University, Vilnius, Lithuania
| | - Miralena Tomescu
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania
- Faculty of Educational Sciences, Department of Psychology, University "Stefan cel Mare" of Suceava, Suceava, Romania
- Faculty of Psychology and Educational Sciences, Department of Cognitive Sciences, University of Bucharest, Bucharest, Romania
| | - Eren Toplutaş
- Department of Neurology, Istanbul Eyupsultan Public Hospital, Istanbul, Turkey
- Program of Neuroscience Ph.D, Graduate School of Health Sciences, Istanbul Medipol University, Istanbul, Turkey
| | - Federica Vellante
- Department of Neurosciences, Imaging and Clinical Sciences, Behavioral Imaging and Neural Dynamics Center, Institute for Advanced Biomedical Technologies, "Gabriele d'Annunzio" University, Chieti, 66100, Italy
| | - Anthony Zanesco
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Filippo Zappasodi
- Department of Neurosciences, Imaging and Clinical Sciences, Behavioral Imaging and Neural Dynamics Center, Institute for Advanced Biomedical Technologies, "Gabriele d'Annunzio" University, Chieti, 66100, Italy
| | - Qihong Zou
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Christoph M Michel
- Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, Geneva, 1202, Switzerland
| |
Collapse
|
10
|
Kleinert T, Koenig T, Nash K, Wascher E. On the Reliability of the EEG Microstate Approach. Brain Topogr 2024; 37:271-286. [PMID: 37410275 PMCID: PMC10884204 DOI: 10.1007/s10548-023-00982-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 06/21/2023] [Indexed: 07/07/2023]
Abstract
EEG microstates represent functional brain networks observable in resting EEG recordings that remain stable for 40-120ms before rapidly switching into another network. It is assumed that microstate characteristics (i.e., durations, occurrences, percentage coverage, and transitions) may serve as neural markers of mental and neurological disorders and psychosocial traits. However, robust data on their retest-reliability are needed to provide the basis for this assumption. Furthermore, researchers currently use different methodological approaches that need to be compared regarding their consistency and suitability to produce reliable results. Based on an extensive dataset largely representative of western societies (2 days with two resting EEG measures each; day one: n = 583; day two: n = 542) we found good to excellent short-term retest-reliability of microstate durations, occurrences, and coverages (average ICCs = 0.874-0.920). There was good overall long-term retest-reliability of these microstate characteristics (average ICCs = 0.671-0.852), even when the interval between measures was longer than half a year, supporting the longstanding notion that microstate durations, occurrences, and coverages represent stable neural traits. Findings were robust across different EEG systems (64 vs. 30 electrodes), recording lengths (3 vs. 2 min), and cognitive states (before vs. after experiment). However, we found poor retest-reliability of transitions. There was good to excellent consistency of microstate characteristics across clustering procedures (except for transitions), and both procedures produced reliable results. Grand-mean fitting yielded more reliable results compared to individual fitting. Overall, these findings provide robust evidence for the reliability of the microstate approach.
Collapse
Affiliation(s)
- Tobias Kleinert
- Department of Ergonomics, Leibniz Research Centre for Working Environment and Human Factors, Ardeystr. 67, 44139, Dortmund, Germany.
- Department of Biological Psychology, Clinical Psychology, and Psychotherapy, University of Freiburg, Stefan-Meier Str. 8, 79104, Freiburg, Germany.
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, 3000, Bern, Switzerland
| | - Kyle Nash
- Department of Psychology, University of Alberta, Edmonton, AB, T6G 2E9, Canada
| | - Edmund Wascher
- Department of Ergonomics, Leibniz Research Centre for Working Environment and Human Factors, Ardeystr. 67, 44139, Dortmund, Germany
| |
Collapse
|
11
|
Deodato M, Seeber M, Mammeri K, Michel CM, Vuilleumier P. Combined effects of neuroticism and negative emotional context on spontaneous EEG dynamics. Soc Cogn Affect Neurosci 2024; 19:nsae012. [PMID: 38334689 PMCID: PMC10873851 DOI: 10.1093/scan/nsae012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2022] [Revised: 11/03/2023] [Accepted: 02/08/2024] [Indexed: 02/10/2024] Open
Abstract
Neuroticism is a personality trait with great clinical relevance, defined as a tendency to experience negative affect, sustained self-generated negative thoughts and impaired emotion regulation. Here, we investigated spontaneous brain dynamics in the aftermath of negative emotional events and their links with neuroticism in order to shed light on the prolonged activity of large-scale brain networks associated with the control of affect. We recorded electroencephalography (EEG) from 36 participants who were asked to rest after watching neutral or fearful video clips. Four topographic maps (i.e. microstates classes A, B, C and D) explained the majority of the variance in spontaneous EEG. Participants showed greater presence of microstate D and lesser presence of microstate C following exposure to fearful stimuli, pointing to changes in attention- and introspection-related networks previously associated with these microstates. These emotional effects were more pronounced for participants with low neuroticism. Moreover, neuroticism scores were positively correlated with microstate C and negatively correlated with microstate D, regardless of previous emotional stimulation. Our results reveal distinctive effects of emotional context on resting-state EEG, consistent with a prolonged impact of negative affect on the brain, and suggest a possible link with neuroticism.
Collapse
Affiliation(s)
- Michele Deodato
- Laboratory for Behavioral Neurology and Imaging of Cognition, Department of Fundamental Neurosciences, University Medical School of Geneva, Geneva 1202, Switzerland
- Psychology Program, Division of Science, New York University Abu Dhabi, Abu Dhabi, UAE
| | - Martin Seeber
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, Campus Biotech, University of Geneva, Geneva 1201, Switzerland
| | - Kevin Mammeri
- Laboratory for Behavioral Neurology and Imaging of Cognition, Department of Fundamental Neurosciences, University Medical School of Geneva, Geneva 1202, Switzerland
- Swiss Center for Affective Sciences, University of Geneva, Campus Biotech, Geneva 1202, Switzerland
| | - Christoph M Michel
- Functional Brain Mapping Laboratory, Department of Fundamental Neurosciences, Campus Biotech, University of Geneva, Geneva 1201, Switzerland
- Center for Biomedical Imaging (CIBM), Lausanne and Geneva, Lausanne 1015, Switzerland
| | - Patrik Vuilleumier
- Laboratory for Behavioral Neurology and Imaging of Cognition, Department of Fundamental Neurosciences, University Medical School of Geneva, Geneva 1202, Switzerland
- Swiss Center for Affective Sciences, University of Geneva, Campus Biotech, Geneva 1202, Switzerland
| |
Collapse
|
12
|
Bai Y, Yu M, Li Y. Dynamic Neural Patterns of Human Emotions in Virtual Reality: Insights from EEG Microstate Analysis. Brain Sci 2024; 14:113. [PMID: 38391688 PMCID: PMC10886836 DOI: 10.3390/brainsci14020113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/10/2024] [Accepted: 01/22/2024] [Indexed: 02/24/2024] Open
Abstract
Emotions play a crucial role in human life and affect mental health. Understanding the neural patterns associated with emotions is essential. Previous studies carried out some exploration of the neural features of emotions, but most have designed experiments in two-dimensional (2D) environments, which differs from real-life scenarios. To create a more real environment, this study investigated emotion-related brain activity using electroencephalography (EEG) microstate analysis in a virtual reality (VR) environment. We recruited 42 healthy volunteers to participate in our study. We explored the dynamic features of different emotions, and four characteristic microstates were analyzed. In the alpha band, microstate A exhibited a higher occurrence in both negative and positive emotions than in neutral emotions. Microstate C exhibited a prolonged duration of negative emotions compared to positive emotions, and a higher occurrence was observed in both microstates C and D during positive emotions. Notably, a unique transition pair was observed between microstates B and C during positive emotions, whereas a unique transition pair was observed between microstates A and D during negative emotions. This study emphasizes the potential of integrating virtual reality (VR) and EEG to facilitate experimental design. Furthermore, this study enhances our comprehension of neural activities during various emotional states.
Collapse
Affiliation(s)
- Yicai Bai
- School of Life Sciences, Shanghai University, Shanghai 200444, China
| | - Minchang Yu
- School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
| | - Yingjie Li
- School of Life Sciences, Shanghai University, Shanghai 200444, China
- College of International Education, Shanghai University, Shanghai 200444, China
| |
Collapse
|
13
|
Peng RJ, Fan Y, Li J, Zhu F, Tian Q, Zhang XB. Abnormalities of electroencephalography microstates in patients with depression and their association with cognitive function. World J Psychiatry 2024; 14:128-140. [PMID: 38327889 PMCID: PMC10845229 DOI: 10.5498/wjp.v14.i1.128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 11/09/2023] [Accepted: 12/22/2023] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND A growing number of recent studies have explored underlying activity in the brain by measuring electroencephalography (EEG) in people with depression. However, the consistency of findings on EEG microstates in patients with depression is poor, and few studies have reported the relationship between EEG microstates, cognitive scales, and depression severity scales. AIM To investigate the EEG microstate characteristics of patients with depression and their association with cognitive functions. METHODS A total of 24 patients diagnosed with depression and 32 healthy controls were included in this study using the Structured Clinical Interview for Disease for The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. We collected information relating to demographic and clinical characteristics, as well as data from the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS; Chinese version) and EEG. RESULTS Compared with the controls, the duration, occurrence, and contribution of microstate C were significantly higher [depression (DEP): Duration 84.58 ± 24.35, occurrence 3.72 ± 0.56, contribution 30.39 ± 8.59; CON: Duration 72.77 ± 10.23, occurrence 3.41 ± 0.36, contribution 24.46 ± 4.66; Duration F = 6.02, P = 0.049; Occurrence F = 6.19, P = 0.049; Contribution F = 10.82, P = 0.011] while the duration, occurrence, and contribution of microstate D were significantly lower (DEP: Duration 70.00 ± 15.92, occurrence 3.18 ± 0.71, contribution 22.48 ± 8.12; CON: Duration 85.46 ± 10.23, occurrence 3.54 ± 0.41, contribution 28.25 ± 5.85; Duration F = 19.18, P < 0.001; Occurrence F = 5.79, P = 0.050; Contribution F = 9.41, P = 0.013) in patients with depression. A positive correlation was observed between the visuospatial/constructional scores of the RBANS scale and the transition probability of microstate class C to B (r = 0.405, P = 0.049). CONCLUSION EEG microstate, especially C and D, is a possible biomarker in depression. Patients with depression had a more frequent transition from microstate C to B, which may relate to more negative rumination and visual processing.
Collapse
Affiliation(s)
- Rui-Jie Peng
- Suzhou Medical College, Soochow University, Suzhou 215123, Jiangsu Province, China
| | - Yu Fan
- Department of Psychiatry, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou 215137, Jiangsu Province, China
| | - Jin Li
- Department of Psychiatry, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou 215137, Jiangsu Province, China
| | - Feng Zhu
- Department of Psychiatry, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou 215137, Jiangsu Province, China
| | - Qing Tian
- Department of Psychiatry, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou 215137, Jiangsu Province, China
| | - Xiao-Bin Zhang
- Department of Psychiatry, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou 215137, Jiangsu Province, China
| |
Collapse
|
14
|
Yang Z, Xia L, Fu Y, Zheng Y, Zhao M, Feng Z, Shi C. Altered EEG Microstates Dynamics in Individuals with Subthreshold Depression When Generating Negative Future Events. Brain Topogr 2024; 37:52-62. [PMID: 37812293 DOI: 10.1007/s10548-023-01011-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 09/25/2023] [Indexed: 10/10/2023]
Abstract
Negative bias in prospection may play a crucial role in driving and maintaining depression. Recent research suggests abnormal activation and functional connectivity in regions of the default mode network (DMN) during future event generation in depressed individuals. However, the neural dynamics during prospection in these individuals remain unknown. To capture network dynamics at high temporal resolution, we employed electroencephalogram (EEG) microstate analysis. We examined microstate properties during both positive and negative prospection in 35 individuals with subthreshold depression (SD) and 35 controls. We identified similar sets of four canonical microstates (A-D) across groups and conditions. Source analysis indicated that each microstate map partially overlapped with a subsystem of the DMN (A: verbal; B: visual-spatial; C: self-referential; and D: modulation). Notably, alterations in EEG microstates were primarily observed in negative prospection of individuals with SD. Specifically, when generating negative future events, the coverage, occurrence, and duration of microstate A increased, while the coverage and duration of microstates B and D decreased in the SD group compared to controls. Furthermore, we observed altered transitions, particularly involving microstate C, during negative prospection in the SD group. These altered dynamics suggest dysconnectivity between subsystems of the DMN during negative prospection in individuals with SD. In conclusion, we provide novel insights into the neural mechanisms of negative bias in depression. These alterations could serve as specific markers for depression and potential targets for future interventions.
Collapse
Affiliation(s)
- Zhuoya Yang
- Department of Basic Psychology, School of Medical Psychology, Army Medical University, Chongqing, 400038, China
- School of Medical Psychology, Army Medical University, 30 Gaotanyan Street, Chongqing, 400038, China
| | - Lei Xia
- Experimental Research Center for Medical and Psychological Science, School of Medical Psychology, Army Medical University, Chongqing, 400038, China
- School of Medical Psychology, Army Medical University, 30 Gaotanyan Street, Chongqing, 400038, China
| | - Yixiao Fu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Yingcan Zheng
- Department of Developmental Psychology for Armyman, School of Medical Psychology, Army Medical University, Chongqing, 400038, China
- School of Medical Psychology, Army Medical University, 30 Gaotanyan Street, Chongqing, 400038, China
| | - Mengxue Zhao
- Department of Military Psychology, School of Medical Psychology, Army Medical University, Chongqing, 400038, China
- School of Medical Psychology, Army Medical University, 30 Gaotanyan Street, Chongqing, 400038, China
| | - Zhengzhi Feng
- School of Medical Psychology, Army Medical University, 30 Gaotanyan Street, Chongqing, 400038, China.
| | - Chunmeng Shi
- Institute of Rocket Force Medicine, State Key Laboratory of Trauma, Burns and Combined Injury, Army Medical University, 30 Gaotanyan Street, Chongqing, 400038, China.
| |
Collapse
|
15
|
Osumi M, Sumitani M, Iwatsuki K, Hoshiyama M, Imai R, Morioka S, Hirata H. Resting-state Electroencephalography Microstates Correlate with Pain Intensity in Patients with Complex Regional Pain Syndrome. Clin EEG Neurosci 2024; 55:121-129. [PMID: 37844609 DOI: 10.1177/15500594231204174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2023]
Abstract
Objective: Severe pain and other symptoms in complex regional pain syndrome (CRPS), such as allodynia and hyperalgesia, are associated with abnormal resting-state brain network activity. No studies to date have examined resting-state brain networks in CRPS patients using electroencephalography (EEG), which can clarify the temporal dynamics of brain networks. Methods: We conducted microstate analysis using resting-state EEG signals to prospectively reveal direct correlations with pain intensity in CRPS patients (n = 17). Five microstate topographies were fitted back to individual CRPS patients' EEG data, and temporal microstate measures were subsequently calculated. Results: Our results revealed five distinct microstates, termed microstates A to E, from resting EEG data in patients with CRPS. Microstates C, D and E were significantly correlated with pain intensity before pain treatment. Particularly, microstates D and E were significantly improved together with pain alleviation after pain treatment. As microstates D and E in the present study have previously been related to attentional networks and the default mode network, improvement in these networks might be related to pain relief in CRPS patients. Conclusions: The functional alterations of these brain networks affected the pain intensity of CRPS patients. Therefore, EEG microstate analyses may be used to identify surrogate markers for pain intensity.
Collapse
Affiliation(s)
- Michihiro Osumi
- Graduate School of Health Science, Kio University. 4-2-2 Umaminaka, Kitakatsuragigun, Nara, Japan
- Neurorehabilitation Research Center, Kio University. 4-2-2 Umaminaka, Kitakatsuragigun, Nara, Japan
| | - Masahiko Sumitani
- Department of Pain and Palliative Medicine, The University of Tokyo Hospital. 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan
| | - Katsuyuki Iwatsuki
- Department of Hand Surgery, Graduate School of Medicine, Nagoya University, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, Japan
| | - Minoru Hoshiyama
- Department of Health Sciences, Faculty of Medicine, Nagoya University, 1-1-20 Daiko-minami, Higashi-ku, Nagoya, Aichi, Japan
| | - Ryota Imai
- School of Rehabilitation, Osaka Kawasaki Rehabilitation University, Kaizuka, Osaka, Japan
| | - Shu Morioka
- Graduate School of Health Science, Kio University. 4-2-2 Umaminaka, Kitakatsuragigun, Nara, Japan
- Neurorehabilitation Research Center, Kio University. 4-2-2 Umaminaka, Kitakatsuragigun, Nara, Japan
| | - Hitoshi Hirata
- Department of Hand Surgery, Graduate School of Medicine, Nagoya University, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, Japan
| |
Collapse
|
16
|
Wen Y, Li H, Huang Y, Qiao D, Ren T, Lei L, Li G, Yang C, Xu Y, Han M, Liu Z. Dynamic network characteristics of adolescents with major depressive disorder: Attention network mediates the association between anhedonia and attentional deficit. Hum Brain Mapp 2023; 44:5749-5769. [PMID: 37683097 PMCID: PMC10619388 DOI: 10.1002/hbm.26474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 08/10/2023] [Accepted: 08/17/2023] [Indexed: 09/10/2023] Open
Abstract
Attention deficit is a critical symptom that impairs social functioning in adolescents with major depressive disorder (MDD). In this study, we aimed to explore the dynamic neural network activity associated with attention deficits and its relationship with clinical outcomes in adolescents with MDD. We included 188 adolescents with MDD and 94 healthy controls. By combining psychophysics, resting-state electroencephalography (EEG), and functional magnetic resonance imaging (fMRI) techniques, we aimed to identify dynamic network features through the investigation of EEG microstate characteristics and related temporal network features in adolescents with MDD. At baseline, microstate analysis revealed that the occurrence of Microstate C in the patient group was lower than that in healthy controls, whereas the duration and coverage of Microstate D increased in the MDD group. Mediation analysis revealed that the probability of transition from Microstate C to D mediated anhedonia and attention deficits in the MDD group. fMRI results showed that the temporal variability of the dorsal attention network (DAN) was significantly weaker in patients with MDD than in healthy controls. Importantly, the temporal variability of DAN mediated the relationship between anhedonia and attention deficits in the patient group. After acute-stage treatment, the response prediction group (RP) showed improvement in Microstates C and D compared to the nonresponse prediction group (NRP). For resting-state fMRI data, the temporal variability of DAN was significantly higher in the RP group than in the NRP group. Overall, this study enriches our understanding of the neural mechanisms underlying attention deficits in patients with MDD and provides novel clinical biomarkers.
Collapse
Affiliation(s)
- Yujiao Wen
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Hong Li
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Yangxi Huang
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Dan Qiao
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Tian Ren
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Lei Lei
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Gaizhi Li
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Chunxia Yang
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Yifan Xu
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Min Han
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| | - Zhifen Liu
- Department of PsychiatryThe First Hospital of Shanxi Medical UniversityTaiyuanChina
| |
Collapse
|
17
|
Hu J, Zhou D, Zhao L, Ma L, Peng X, He X, Chen R, Chen W, Jiang Z, Kuang L, Wang W. Associations between abnormal electroencephalogram microstates and childhood emotional abuse in adolescent depression. ZHONG NAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF CENTRAL SOUTH UNIVERSITY. MEDICAL SCIENCES 2023; 48:1678-1685. [PMID: 38432858 PMCID: PMC10929957 DOI: 10.11817/j.issn.1672-7347.2023.230220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Indexed: 03/05/2024]
Abstract
OBJECTIVES Childhood traumatic experiences greatly influenced the brain network activities of patients with depression, and there is an urgent need to explore the temporal dynamics for these changes. This study aims to investigate the abnormalities of resting-state electroencephalogram (EEG) microstates in eye-open state of depressed adolescents and to explore the correlations between their EEG microstates and the childhood traumatic experience. METHODS Using resting-state EEG microstate analysis, we explored the temporal dynamics of brain activity in patients with adolescent depression. This study selected 66 adolescents with depression as a patient group, and 27 healthy adolescents as a healthy control group. A modified k-means clustering algorithm was used to classify the 64-channel resting-state EEG data into different microstates. Independent sample t-tests were used to compare the microstate parameters between the 2 groups and further assciations between these parameters and childhood traumatic experience in patients were explored via using Spearman correlation. RESULTS In this study, significant differences were observed in the occurrence and transition probabilities of EEG microstates between the healthy control and the patient group. Notably, there was a statistically significant difference (P<0.05) in the occurrence of microstate A across 2 groups, exhibiting a negative correlation with the emotional abuse component within the childhood trauma scores (Spearman's rho=-0.31, P=0.013). Furthermore, patient-specific, non-random transitions from microstate B to A (Spearman's rho=-0.30, P=0.015) and C to A (Spearman's rho=-0.31, P=0.013) were inversely associated with the scores of emotional abuse factors from childhood trauma in the patient group, showing statistically significant differences when comparing to the healthy controls (P<0.05). Upon stratification into quartiles based on the emotional abuse factor scores, the occurrence of microstate A, as well as the transition rates from microstates B to A and C to A, retained statistical significance following adjustment for multiple comparisons (all P<0.05). CONCLUSIONS The abnormal temporal dynamics in brain networks of adolescents with depression are linked to childhood emotional abuse. Those who have suffered severe emotional abuse may show greater impairments in the brain's visual and central executive networks. EEG microstate analysis could be a potential tool for detecting adolescent depression with severe childhood trauma.
Collapse
Affiliation(s)
- Jinhui Hu
- Mental Health Center, University-Town Hospital, Chongqing Medical University, Chongqing 401331.
| | - Dongdong Zhou
- Mental Health Center, University-Town Hospital, Chongqing Medical University, Chongqing 401331
| | - Lin Zhao
- Department of Psychiatry, First Affiliated Hospital, Chongqing Medical University, Chongqing 400042, China
| | - Lingli Ma
- Department of Psychiatry, First Affiliated Hospital, Chongqing Medical University, Chongqing 400042, China
| | - Xinyu Peng
- Department of Psychiatry, First Affiliated Hospital, Chongqing Medical University, Chongqing 400042, China
| | - Xiaoqing He
- Mental Health Center, University-Town Hospital, Chongqing Medical University, Chongqing 401331
| | - Ran Chen
- Mental Health Center, University-Town Hospital, Chongqing Medical University, Chongqing 401331
| | - Wanjun Chen
- Mental Health Center, University-Town Hospital, Chongqing Medical University, Chongqing 401331
| | - Zhenghao Jiang
- Mental Health Center, University-Town Hospital, Chongqing Medical University, Chongqing 401331
| | - Li Kuang
- Mental Health Center, University-Town Hospital, Chongqing Medical University, Chongqing 401331
- Department of Psychiatry, First Affiliated Hospital, Chongqing Medical University, Chongqing 400042, China
| | - Wo Wang
- Mental Health Center, University-Town Hospital, Chongqing Medical University, Chongqing 401331.
| |
Collapse
|
18
|
Horr NK, Mousavi B, Han K, Li A, Tang R. Human behavior in free search online shopping scenarios can be predicted from EEG activation using Hjorth parameters. Front Neurosci 2023; 17:1191213. [PMID: 38027474 PMCID: PMC10667477 DOI: 10.3389/fnins.2023.1191213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023] Open
Abstract
The present work investigates whether and how decisions in real-world online shopping scenarios can be predicted based on brain activation. Potential customers were asked to search through product pages on e-commerce platforms and decide, which products to buy, while their EEG signal was recorded. Machine learning algorithms were then trained to distinguish between EEG activation when viewing products that are later bought or put into the shopping card as opposed to products that are later discarded. We find that Hjorth parameters extracted from the raw EEG can be used to predict purchase choices to a high level of accuracy. Above-chance predictions based on Hjorth parameters are achieved via different standard machine learning methods with random forest models showing the best performance of above 80% prediction accuracy in both 2-class (bought or put into card vs. not bought) and 3-class (bought vs. put into card vs. not bought) classification. While conventional EEG signal analysis commonly employs frequency domain features such as alpha or theta power and phase, Hjorth parameters use time domain signals, which can be calculated rapidly with little computational cost. Given the presented evidence that Hjorth parameters are suitable for the prediction of complex behaviors, their potential and remaining challenges for implementation in real-time applications are discussed.
Collapse
|
19
|
Yang R, Zhao Y, Tan Z, Lai J, Chen J, Zhang X, Sun J, Chen L, Lu K, Cao L, Liu X. Differentiation between bipolar disorder and major depressive disorder in adolescents: from clinical to biological biomarkers. Front Hum Neurosci 2023; 17:1192544. [PMID: 37780961 PMCID: PMC10540438 DOI: 10.3389/fnhum.2023.1192544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 08/24/2023] [Indexed: 10/03/2023] Open
Abstract
Background Mood disorders are very common among adolescents and include mainly bipolar disorder (BD) and major depressive disorder (MDD), with overlapping depressive symptoms that pose a significant challenge to realizing a rapid and accurate differential diagnosis in clinical practice. Misdiagnosis of BD as MDD can lead to inappropriate treatment and detrimental outcomes, including a poorer ultimate clinical and functional prognosis and even an increased risk of suicide. Therefore, it is of great significance for clinical management to identify clinical symptoms or features and biological markers that can accurately distinguish BD from MDD. With the aid of bibliometric analysis, we explore, visualize, and conclude the important directions of differential diagnostic studies of BD and MDD in adolescents. Materials and methods A literature search was performed for studies on differential diagnostic studies of BD and MDD among adolescents in the Web of Science Core Collection database. All studies considered for this article were published between 2004 and 2023. Bibliometric analysis and visualization were performed using the VOSviewer and CiteSpace software. Results In total, 148 publications were retrieved. The number of publications on differential diagnostic studies of BD and MDD among adolescents has been generally increasing since 2012, with the United States being an emerging hub with a growing influence in the field. Boris Birmaher is the top author in terms of the number of publications, and the Journal of Affective Disorders is the most published journal in the field. Co-occurrence analysis of keywords showed that clinical characteristics, genetic factors, and neuroimaging are current research hotspots. Ultimately, we comprehensively sorted out the current state of research in this area and proposed possible research directions in future. Conclusion This is the first-ever study of bibliometric and visual analyses of differential diagnostic studies of BD and MDD in adolescents to reveal the current research status and important directions in the field. Our research and analysis results might provide some practical sources for academic scholars and clinical practice.
Collapse
Affiliation(s)
- Ruilan Yang
- CAS Key Laboratory of Brain Connectome and Manipulation, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, Guangdong, China
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Yanmeng Zhao
- Southern Medical University, Guangzhou, Guangdong, China
| | - Zewen Tan
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Juan Lai
- CAS Key Laboratory of Brain Connectome and Manipulation, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, Guangdong, China
| | - Jianshan Chen
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Xiaofei Zhang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Jiaqi Sun
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Lei Chen
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Kangrong Lu
- School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, China
| | - Liping Cao
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Xuemei Liu
- CAS Key Laboratory of Brain Connectome and Manipulation, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, Guangdong, China
- University of Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
20
|
Hong Y, Moore IL, Smith DE, Long NM. Spatiotemporal Dynamics of Memory Encoding and Memory Retrieval States. J Cogn Neurosci 2023; 35:1463-1477. [PMID: 37348133 PMCID: PMC10513765 DOI: 10.1162/jocn_a_02022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
Memory encoding and memory retrieval are neurally distinct brain states that can be differentiated on the basis of cortical network activity. However, it is unclear whether sustained engagement of one network or fluctuations between multiple networks give rise to these memory states. The spatiotemporal dynamics of memory states may have important implications for memory behavior and cognition; however, measuring temporally resolved signals of cortical networks poses a challenge. Here, we recorded scalp electroencephalography from participants performing a mnemonic state task in which they were biased toward memory encoding or retrieval. We performed a microstate analysis to measure the temporal dynamics of cortical networks throughout this mnemonic state task. We find that Microstate E, a putative analog of the default mode network, shows temporally sustained dissociations between memory encoding and retrieval, with greater engagement during retrieve compared with encode trials. We further show that decreased engagement of Microstate E is a general property of encoding, rather than a reflection of retrieval suppression. Thus, memory success, as well as cognition more broadly, may be influenced by the ability to engage or disengage Microstate E in a goal-dependent manner.
Collapse
Affiliation(s)
- Yuju Hong
- University of Virginia, Charlottesville
| | | | | | | |
Collapse
|
21
|
Ghaderi AH, Brown EC, Clark DL, Ramasubbu R, Kiss ZHT, Protzner AB. Functional brain network features specify DBS outcome for patients with treatment resistant depression. Mol Psychiatry 2023; 28:3888-3899. [PMID: 37474591 DOI: 10.1038/s41380-023-02181-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 06/29/2023] [Accepted: 07/05/2023] [Indexed: 07/22/2023]
Abstract
Deep brain stimulation (DBS) has shown therapeutic benefits for treatment resistant depression (TRD). Stimulation of the subcallosal cingulate gyrus (SCG) aims to alter dysregulation between subcortical and cortex. However, the 50% response rates for SCG-DBS indicates that selection of appropriate patients is challenging. Since stimulation influences large-scale network function, we hypothesized that network features can be used as biomarkers to inform outcome. In this pilot project, we used resting-state EEG recorded longitudinally from 10 TRD patients with SCG-DBS (11 at baseline). EEGs were recorded before DBS-surgery, 1-3 months, and 6 months post surgery. We used graph theoretical analysis to calculate clustering coefficient, global efficiency, eigenvector centrality, energy, and entropy of source-localized EEG networks to determine their topological/dynamical features. Patients were classified as responders based on achieving a 50% or greater reduction in Hamilton Depression (HAM-D) scores from baseline to 12 months post surgery. In the delta band, false discovery rate analysis revealed that global brain network features (segregation, integration, synchronization, and complexity) were significantly lower and centrality of subgenual anterior cingulate cortex (ACC) was higher in responders than in non-responders. Accordingly, longitudinal analysis showed SCG-DBS increased global network features and decreased centrality of subgenual ACC. Similarly, a clustering method separated two groups by network features and significant correlations were identified longitudinally between network changes and depression symptoms. Despite recent speculation that certain subtypes of TRD are more likely to respond to DBS, in the SCG it seems that underlying brain network features are associated with ability to respond to DBS. SCG-DBS increased segregation, integration, and synchronizability of brain networks, suggesting that information processing became faster and more efficient, in those patients in whom it was lower at baseline. Centrality results suggest these changes may occur via altered connectivity in specific brain regions especially ACC. We highlight potential mechanisms of therapeutic effect for SCG-DBS.
Collapse
Affiliation(s)
- Amir Hossein Ghaderi
- Department of Psychology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neuroscience, University of Calgary, Calgary, AB, Canada
| | - Elliot C Brown
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neuroscience, University of Calgary, Calgary, AB, Canada
- Mathison Centre for Mental Health, University of Calgary, Calgary, AB, Canada
- Arden University Berlin, 10963, Berlin, Germany
- Charité-Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin, Berlin, Germany
- Berlin Institute of Health, 10117, Berlin, Germany
| | - Darren Laree Clark
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neuroscience, University of Calgary, Calgary, AB, Canada
- Mathison Centre for Mental Health, University of Calgary, Calgary, AB, Canada
| | - Rajamannar Ramasubbu
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neuroscience, University of Calgary, Calgary, AB, Canada
- Mathison Centre for Mental Health, University of Calgary, Calgary, AB, Canada
| | - Zelma H T Kiss
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
- Department of Clinical Neuroscience, University of Calgary, Calgary, AB, Canada.
- Mathison Centre for Mental Health, University of Calgary, Calgary, AB, Canada.
| | - Andrea B Protzner
- Department of Psychology, University of Calgary, Calgary, AB, Canada.
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
- Mathison Centre for Mental Health, University of Calgary, Calgary, AB, Canada.
| |
Collapse
|
22
|
Zhou DD, Peng XY, Zhao L, Ma LL, Hu JH, Jiang ZH, He XQ, Wang W, Chen R, Kuang L. Neurophysiological biomarkers for depression classification: Utilizing microstate k-mers and a bag-of-words model. J Psychiatr Res 2023; 165:197-204. [PMID: 37517240 DOI: 10.1016/j.jpsychires.2023.07.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/30/2023] [Accepted: 07/16/2023] [Indexed: 08/01/2023]
Abstract
Microstates are analogous to characters in a language, and short fragments consisting of several microstates (k-mers) are analogous to words. We aimed to investigate whether microstate k-mers could be used as neurophysiological biomarkers to differentiate between depressed patients and normal controls. We utilized a bag-of-words model to process microstate sequences, using k-mers with a k range of 1-10 as terms, and the term frequency (TF) with or without inverse-document-frequency (IDF) as features. We performed nested cross-validation on Dataset 1 (27 patients and 26 controls) and Dataset 2 (34 patients and 30 controls) separately and then trained on one dataset and tested on the other. The best area under the curve (AUC) of 81.5% was achieved for the model with L1 regularization using the TF of 4-mers as features in Dataset 1, and the best AUC of 88.9% was achieved for the model with L1 regularization using the TF of 9-mers as features in Dataset 2. When Dataset 1 was used as the training set, the best AUC of predicting Dataset 2 was 74.1% for the model with L2 regularization using the TF-IDF of 9-mers as features, while the best AUC of predicting Dataset 1 was 70.2% for the model with L1 regularization using the TF of 8-mers as features. Our study provided novel insights into the potential of microstate k-mers as neurophysiological biomarkers for individual-level classification of depression. These may facilitate further exploration of microstate sequences using natural language processing techniques.
Collapse
Affiliation(s)
- Dong-Dong Zhou
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Xin-Yu Peng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lin Zhao
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ling-Li Ma
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jin-Hui Hu
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Zheng-Hao Jiang
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Xiao-Qing He
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Wo Wang
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China
| | - Ran Chen
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China.
| | - Li Kuang
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China; Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| |
Collapse
|
23
|
Lee SH, Hwang HH, Kim S, Hwang J, Park J, Park S. Clinical Implication of Maumgyeol Basic Service-the 2 Channel Electroencephalography and a Photoplethysmogram-based Mental Health Evaluation Software. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2023; 21:583-593. [PMID: 37424425 PMCID: PMC10335898 DOI: 10.9758/cpn.23.1062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/24/2023] [Accepted: 03/29/2023] [Indexed: 07/11/2023]
Abstract
Objective Maumgyeol Basic service is a mental health evaluation and grade scoring software using the 2 channels EEG and photoplethysmogram (PPG). This service is supposed to assess potential at-risk groups with mental illness more easily, rapidly, and reliably. This study aimed to evaluate the clinical implication of the Maumgyeol Basic service. Methods One hundred one healthy controls and 103 patients with a psychiatric disorder were recruited. Psychological evaluation (Mental Health Screening for Depressive Disorders [MHS-D], Mental Health Screening for Anxiety Disorders [MHS-A], cognitive stress response scale [CSRS], 12-item General Health Questionnaire [GHQ-12], Clinical Global Impression [CGI]) and digit symbol substitution test (DSST) were applied to all participants. Maumgyeol brain health score and Maumgyeol mind health score were calculated from 2 channel frontal EEG and PPG, respectively. Results Participants were divided into three groups: Maumgyeol Risky, Maumgyeol Good, and Maumgyeol Usual. The Maumgyeol mind health scores, but not brain health scores, were significantly lower in the patients group compared to healthy controls. Maumgyeol Risky group showed significantly lower psychological and cognitive ability evaluation scores than Maumgyeol Usual and Good groups. Maumgyel brain health score showed significant correlations with CSRS and DSST. Maumgyeol mind health score showed significant correlations with CGI and DSST. About 20.6% of individuals were classified as the No Insight group, who had mental health problems but were unaware of their illnesses. Conclusion This study suggests that the Maumgyeol Basic service can provide important clinical information about mental health and be used as a meaningful digital mental healthcare monitoring solution to prevent symptom aggravation.
Collapse
Affiliation(s)
- Seung-Hwan Lee
- Bwave Inc., Goyang, Korea
- Department of Psychiatry, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea
- Clinical Emotion and Cognition Research Laboratory, Department of Psychiatry, Inje University, Goyang, Korea
| | - Hyeon-Ho Hwang
- Clinical Emotion and Cognition Research Laboratory, Department of Psychiatry, Inje University, Goyang, Korea
- Department of Human-Computer Interaction, Hanyang University, Ansan, Korea
| | - Sungkean Kim
- Department of Human-Computer Interaction, Hanyang University, Ansan, Korea
| | | | | | | |
Collapse
|
24
|
The EEG microstate representation of discrete emotions. Int J Psychophysiol 2023; 186:33-41. [PMID: 36773887 DOI: 10.1016/j.ijpsycho.2023.02.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 02/03/2023] [Accepted: 02/07/2023] [Indexed: 02/11/2023]
Abstract
Understanding how human emotions are represented in our brain is a central question in the field of affective neuroscience. While previous studies have mainly adopted a modular and static perspective on the neural representation of emotions, emerging research suggests that emotions may rely on a distributed and dynamic representation. The present study aimed to explore the EEG microstate representations for nine discrete emotions (Anger, Disgust, Fear, Sadness, Neutral, Amusement, Inspiration, Joy and Tenderness). Seventy-eight participants were recruited to watch emotion eliciting videos with their EEGs recorded. Multivariate analysis revealed that different emotions had distinct EEG microstate features. By using the EEG microstate features in the Neutral condition as the reference, the coverage of C, duration of C and occurrence of B were found to be the top-contributing microstate features for the discrete positive and negative emotions. The emotions of Disgust, Fear and Joy were found to be most effectively represented by EEG microstate. The present study provided the first piece of evidence of EEG microstate representation for discrete emotions, highlighting a whole-brain, dynamical representation of human emotions.
Collapse
|
25
|
Thirioux B, Langbour N, Bokam P, Renaudin L, Wassouf I, Harika-Germaneau G, Jaafari N. Microstates imbalance is associated with a functional dysregulation of the resting-state networks in obsessive-compulsive disorder: a high-density electrical neuroimaging study using the TESS method. Cereb Cortex 2023; 33:2593-2611. [PMID: 35739579 DOI: 10.1093/cercor/bhac229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/13/2022] [Accepted: 05/14/2022] [Indexed: 11/14/2022] Open
Abstract
The dysfunctional patterns of microstates dynamics in obsessive-compulsive disorder (OCD) remain uncertain. Using high-density electrical neuroimaging (EEG) at rest, we explored microstates deterioration in OCD and whether abnormal microstates patterns are associated with a dysregulation of the resting-state networks interplay. We used EEG microstates analyses, TESS method for sources reconstruction, and General Linear Models to test for the effect of disease severity on neural responses. OCD patients exhibited an increased contribution and decreased duration of microstates C and D, respectively. Activity was decreased in the Salience Network (SN), associated with microstate C, but increased in the Default Mode Network (DMN) and Executive Control Network (ECN), respectively, associated with microstates E and D. The hyperactivity of the right angular gyrus in the ECN correlated with the symptoms severity. The imbalance between microstates C and D invalidates the hypothesis that this electrophysiological pattern is specific to psychosis. Demonstrating that the SN-ECN dysregulation manifests as abnormalities in microstates C and D, we confirm that the SN deterioration in OCD is accompanied by a failure of the DMN to deactivate and aberrant compensatory activation mechanisms in the ECN. These abnormalities explain typical OCD clinical features but also detachment from reality, shared with psychosis.
Collapse
Affiliation(s)
- Bérangère Thirioux
- Unité de Recherche Clinique Pierre Deniker, Centre Hospitalier Henri Laborit, 86021 Poitiers, France
- CNRS 7295, Centre de Recherches sur la Cognition et l'Apprentissage, Université de Poitiers, 86021 Poitiers, France
| | - Nicolas Langbour
- Unité de Recherche Clinique Pierre Deniker, Centre Hospitalier Henri Laborit, 86021 Poitiers, France
- CNRS 7295, Centre de Recherches sur la Cognition et l'Apprentissage, Université de Poitiers, 86021 Poitiers, France
| | - Prasanth Bokam
- Unité de Recherche Clinique Pierre Deniker, Centre Hospitalier Henri Laborit, 86021 Poitiers, France
| | - Léa Renaudin
- Unité de Recherche Clinique Pierre Deniker, Centre Hospitalier Henri Laborit, 86021 Poitiers, France
| | - Issa Wassouf
- Unité de Recherche Clinique Pierre Deniker, Centre Hospitalier Henri Laborit, 86021 Poitiers, France
- CNRS 7295, Centre de Recherches sur la Cognition et l'Apprentissage, Université de Poitiers, 86021 Poitiers, France
| | - Ghina Harika-Germaneau
- Unité de Recherche Clinique Pierre Deniker, Centre Hospitalier Henri Laborit, 86021 Poitiers, France
- CNRS 7295, Centre de Recherches sur la Cognition et l'Apprentissage, Université de Poitiers, 86021 Poitiers, France
- Faculté de Médecine et de Pharmacie, Université de Poitiers, 86021 Poitiers, France
| | - Nematollah Jaafari
- Unité de Recherche Clinique Pierre Deniker, Centre Hospitalier Henri Laborit, 86021 Poitiers, France
- CNRS 7295, Centre de Recherches sur la Cognition et l'Apprentissage, Université de Poitiers, 86021 Poitiers, France
- Faculté de Médecine et de Pharmacie, Université de Poitiers, 86021 Poitiers, France
| |
Collapse
|
26
|
Rochas V, Gschwind M, Nedeltchev K, Seeck M. Spike-microstates correlate with interictal epileptogenic discharges: a marker for hidden epileptic activity. Brain Commun 2023; 5:fcad124. [PMID: 37151228 PMCID: PMC10154908 DOI: 10.1093/braincomms/fcad124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 02/20/2023] [Accepted: 04/14/2023] [Indexed: 05/09/2023] Open
Abstract
Objectively estimating disease severity and treatment success is a main problem in outpatient managing of epilepsy. Self-reported seizures diaries are well-known to underestimate the actual seizure count, and repeated EEGs might not show interictal epileptiform discharges (IEDs), although patients suffer from seizures. In this prospective study, we investigate the potential of microstate analysis to monitor epilepsy patients independently of their IED count. From our databank of candidates for epilepsy surgery, we included 18 patients who underwent controlled resting EEG sessions (with eyes closed, 30 min), at around the same time of the day, during at least four days (range: 4-8 days; mean: 5). Nine patients with temporal foci, six with extratemporal foci, and three with generalized epilepsy were included. Each patient's IEDs were marked and the topographic voltage maps of the IED peaks were averaged, and an individual average spike topography (AST) was created. The AST was then backfitted to each timepoint of the whole EEG resulting in the Spike-Microstate (SMS). The presence of the SMS in the residual EEG outside of the short IEDs epochs was determined for each recording session in each patient and correlated with the occurrence of the IEDs across all recording session, as well as with the drug charge of each day. Overall, SMS was much more represented in the routine EEG than the IEDs: they were identified 262 times more often than IEDs. The SMS time coverage correlated significantly with the IED occurrence rate (rho = 0.56; P < 0.001). If only patients with focal epilepsy were considered, this correlation was even higher rho = 0.69 (P < 0.001). Drug charge per day did not correlate with SMS. In this proof-of-concept study, the time coverage of SMS correlated strongly with the occurrence rate of the IEDs, they can be retrieved in the scalp EEG at a much higher occurrence rate. We conclude that SMS, once obtained for a given patient, are a more abundant marker of hidden epileptic activity than IEDs, in particular in focal epilepsy, and can be used also in absence of IEDs. Future larger studies are needed to verify its potential as monitoring tool and to determine cut-off values when drug protection becomes imperfect.
Collapse
Affiliation(s)
- Vincent Rochas
- Correspondence to: Vincent Rochas Fundamental Neuroscience Department University of Geneva, Chemin des Mines 9 1202 Genève, Switzerland E-mail:
| | | | | | - Margitta Seeck
- EEG and Epilepsy Unit, Department of Neurology University Hospital Geneva and University of Geneva, 1201 Geneva, Switzerland
| |
Collapse
|
27
|
The pro-inflammatory factors contribute to the EEG microstate abnormalities in patients with major depressive disorder. Brain Behav Immun Health 2022; 26:100523. [PMID: 36267834 PMCID: PMC9576533 DOI: 10.1016/j.bbih.2022.100523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 09/19/2022] [Accepted: 09/25/2022] [Indexed: 11/22/2022] Open
Abstract
Pro-inflammatory factors may be associated with abnormalities in functional brain networks, which may be a mechanism in the pathogenesis of major depressive disorder (MDD). Electroencephalogram (EEG) microstates reflect the functioning of brain networks. However, the relationship between pro-inflammatory factors and the microstate abnormalities in patients with MDD is poorly understood. 24 MDD patients and 24 age-and sex-matched healthy controls (HC) were recruited. Montgomery-Asberg Depression Rating Scale(MADRS) were assessed. Serum (interleukin- 2(IL- 2), tumor necrosis factor-α (TNF-α) and hs-C-reactive protein (CRP)and EEG data were collected. K-means clustering was performed to characterize different microstates. For each microstate, duration, occurrence and coverage were estimated. Four microstates (e.g. A, B, C, D) were characterized, MDD group showed lower duration, occurrence and coverage of microstate B and microstate D, while higher duration of microstate A and microstate C and levels of IL-2, TNF-α, hs-CRP than HC group. The duration, occurrence and coverage of microstate D were negatively correlated with levels of pro-inflammatory factors (IL-2, TNF- α and hs- CRP) (all P < 0.05). Serum pro-inflammatory induced the abnormalities of microstate D. Together, these findings add to the understanding of the pathophysiology of MDD and point to pro-inflammatory factors contribute to EEG microstate abnormalities in patients with MDD.
Collapse
|
28
|
Zhao Z, Niu Y, Zhao X, Zhu Y, Shao Z, Wu X, Wang C, Gao X, Wang C, Xu Y, Zhao J, Gao Z, Ding J, Yu Y. EEG microstate in first-episode drug-naive adolescents with depression. J Neural Eng 2022; 19. [PMID: 35952647 DOI: 10.1088/1741-2552/ac88f6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 08/11/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND A growing number of studies have revealed significant abnormalities in EEG microstate in patients with depression, but these findings may be affected by medication. Therefore, how the EEG microstates abnormally change in patients with depression in the early stage and without the influence of medication has not been investigated so far. METHODS Resting-state EEG data and Hamilton Depression Rating Scale (HDRS) were collected from 34 first-episode drug-naïve adolescent with depression and 34 matched healthy controls. EEG microstate analysis was applied and nonlinear characteristics of EEG microstate sequences were studied by sample entropy and Lempel-Ziv complexity (LZC). The microstate temporal parameters and complexity were tried to train a SVM for classification of patients with depression. RESULTS Four typical EEG microstate topographies were obtained in both groups, but microstate C topography was significantly abnormal in depression patients. The duration of microstate B, C, D and the occurrence and coverage of microstate B significantly increased, the occurrence and coverage of microstate A, C reduced significantly in depression group. Sample entropy and LZC in the depression group were abnormally increased and were negatively correlated with HDRS. When the combination of EEG microstate temporal parameters and complexity of microstate sequence was used to classify patients with depression from healthy controls, a classification accuracy of 90.9% was obtained. CONCLUSION Abnormal EEG microstate has appeared in early depression, reflecting an underlying abnormality in configuring neural resources and transitions between distinct brain network states. EEG microstate can be used as a neurophysiological biomarker for early auxiliary diagnosis of depression.
Collapse
Affiliation(s)
- Zongya Zhao
- Xinxiang Medical University, College of Medical Engineering, Xinxiang, Henan, 453003, CHINA
| | - Yanxiang Niu
- Xinxiang Medical University, college of medical engineering, Xinxiang, Henan, 453600, CHINA
| | - Xiaofeng Zhao
- First Affiliated Hospital of Zhengzhou University, Department of Psychiatry, Zhengzhou, 450000, CHINA
| | - Yu Zhu
- Xinxiang Medical University, college of medical engineering, Xinxiang, Henan, 453600, CHINA
| | - Zhenpeng Shao
- Xinxiang Medical University, college of medical engineering, Xinxiang, Henan, 453600, CHINA
| | - Xingyang Wu
- Xinxiang Medical University, college of medical engineering, Xinxiang, 453003, CHINA
| | - Chong Wang
- Xinxiang Medical University, college of medical engineering, Xinxiang, Henan, 453600, CHINA
| | - Xudong Gao
- Xinxiang Medical University, college of medical engineering, Xinxiang, Henan, 453600, CHINA
| | - Chang Wang
- Xinxiang Medical University, college of medical engineering, Xinxiang, Henan, 453600, CHINA
| | - Yongtao Xu
- Xinxiang Medical University, college of medical engineering, Xinxiang, 453003, CHINA
| | - Junqiang Zhao
- Xinxiang Medical University, college of medical engineering, Xinxiang, 453003, CHINA
| | - Zhixian Gao
- Xinxiang Medical University, college of medical engineering, Xinxiang, 453003, CHINA
| | - Junqing Ding
- First Affiliated Hospital of Zhengzhou University Zhengzhou, Department of Neurology, Zhengzhou, 450000, CHINA
| | - Yi Yu
- Xinxiang Medical University, college of Biomedical Engineering, Xinxiang, 453003, CHINA
| |
Collapse
|
29
|
EEG microstate temporal Dynamics Predict depressive symptoms in College Students. Brain Topogr 2022; 35:481-494. [PMID: 35790705 DOI: 10.1007/s10548-022-00905-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 05/19/2022] [Indexed: 11/02/2022]
Abstract
Previous studies on resting-state electroencephalographic responses in patients with depressive disorders have identified electroencephalogram (EEG) parameters as potential biomarkers for the early detection and diagnosis of depressive disorders. However, these studies did not investigate the relationship between resting-state EEG microstates and the early detection of depressive symptoms in preclinical individuals. To explore the possible association between resting-state EEG microstate temporal dynamics and depressive symptoms among college students, EEG microstate analysis was performed on eyes-closed resting-state EEG data for approximately 5 min from 34 undergraduates with high intensity of depressive symptoms and 34 age- and sex-matched controls with low intensity of depressive symptoms. Five microstate classes (A-E) were identified to best explain the datasets of both groups. Compared to controls, the mean duration, occurrence, and coverage of microstate class B increased significantly, whereas the occurrence and coverage of microstate classes D and E decreased significantly in individuals with high intensity of depressive symptoms. Additionally, the presence of microstate class B was positively correlated with participants' Beck Depression Inventory-II (BDI-II) scores, and the presence of microstate classes D and E were negatively correlated with their BDI-II scores. Further, individuals with high intensity of depressive symptoms had higher transition probabilities of A→B, B→A, B→C, B→D, and C→B, with lower transition probabilities of A→D, A→E, D→A, D→E, E→A, E→C, and E→D than controls. These results highlight resting-state EEG microstate temporal dynamics as potential biomarkers for the early detection and timely treatment of depression in college students.
Collapse
|
30
|
Lei L, Liu Z, Zhang Y, Guo M, Liu P, Hu X, Yang C, Zhang A, Sun N, Wang Y, Zhang K. EEG microstates as markers of major depressive disorder and predictors of response to SSRIs therapy. Prog Neuropsychopharmacol Biol Psychiatry 2022; 116:110514. [PMID: 35085607 DOI: 10.1016/j.pnpbp.2022.110514] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 01/05/2022] [Accepted: 01/18/2022] [Indexed: 10/19/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is associated with abnormal neural activities and brain connectivity. EEG microstate is a voltage topology map that reflects transient activations of the brain network. A limited number of studies on EEG microstate in MDD have focused on differences between patients and healthy controls. However, EEG microstate changes in MDD patients before and after drug treatment have not been evaluated. We assessed EEG microstate characteristics and evaluated changes in brain network dynamics in MDD patients before and after drug treatment. Moreover, we evaluated the neuro-electrophysiological mechanisms of antidepressant therapies. METHODS 64-channel resting EEG was obtained from 101 patients with first-episode untreated depression (0 week) and 45 healthy controls (HC) from January to December 2020. MDD patients were treated with selective serotonin reuptake inhibitors (SSRI). EEG data for 51 MDD patients who had completed an 8-week follow-up was collected. After pre-processing, EEG data from different groups were subjected to microstate analysis, and the atomize and agglomerate hierarchical clustering (AAHC) was into 4 microstates. Next, EEG signals from each patient were fitted using templates of 4 microstates. Finally, microstate indices were collected and analyzed. RESULTS Global clustering generated 4 microstates (A, B, C, D) in all subjects, which explained 65-84% of the global variance. Compared to HC, the duration of microstate D reduced while those of microstates A and B increased in MDD patients. After the 8-week treatment period, the duration and coverage of microstate D increased, the frequency of microstate A and transition probability of microstate D to A reduced, while transition probability of microstate B to D and D to B increased in MDD patients. There were no differences in microstate features between HC and MDD at 8 weeks. In patients with first-episode untreated depression, lower average durations of microstate D, relatively higher frequencies of microstate C and lower transition probabilities of microstate D to B correlated with better effects after 8 weeks. The higher occurrence and proportion of microstate C at 8 weeks was positively correlated with the HAMD score and reduction rate. The same observation was reached for the transition probability of microstate A to C. However, the transition probability of microstate D to B showed a negative correlation with the HAMD score at 8 weeks. CONCLUSION Microstate D is a potential electrophysiological trait of MDD and can predict treatment outcomes of SSRIs. Therefore, EEG microstate analysis may not only be an objective method for evaluating treatment outcomes of depression, but is also a potential new approach for exploring the neuro-electrophysiological mechanisms of antidepressant therapy. Public title: Multidimensional diagnosis, individualized treatment and management techniques based on clinic-pathological characteristics of depressive disorder; Registration number: ChiCTR1900026600; Date of registration: 2019-10-15; URL: http://www.chictr.org.cn/index.aspx.
Collapse
Affiliation(s)
- Lei Lei
- Department of Psychiatry, First Hospital of Shanxi Medical University, taiyuan, Shanxi 030000, China; First clinical medical college, Shanxi Medical University, Taiyuan, Shanxi 030000, China
| | - Zhifen Liu
- Department of Psychiatry, First Hospital of Shanxi Medical University, taiyuan, Shanxi 030000, China; First clinical medical college, Shanxi Medical University, Taiyuan, Shanxi 030000, China
| | - Yu Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, taiyuan, Shanxi 030000, China; First clinical medical college, Shanxi Medical University, Taiyuan, Shanxi 030000, China
| | - Meng Guo
- Department of Psychiatry, First Hospital of Shanxi Medical University, taiyuan, Shanxi 030000, China; First clinical medical college, Shanxi Medical University, Taiyuan, Shanxi 030000, China
| | - Penghong Liu
- Department of Psychiatry, First Hospital of Shanxi Medical University, taiyuan, Shanxi 030000, China; First clinical medical college, Shanxi Medical University, Taiyuan, Shanxi 030000, China
| | - Xiaodong Hu
- Department of Psychiatry, First Hospital of Shanxi Medical University, taiyuan, Shanxi 030000, China; First clinical medical college, Shanxi Medical University, Taiyuan, Shanxi 030000, China
| | - Chunxia Yang
- Department of Psychiatry, First Hospital of Shanxi Medical University, taiyuan, Shanxi 030000, China; First clinical medical college, Shanxi Medical University, Taiyuan, Shanxi 030000, China
| | - Aixia Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, taiyuan, Shanxi 030000, China; First clinical medical college, Shanxi Medical University, Taiyuan, Shanxi 030000, China
| | - Ning Sun
- Department of Psychiatry, First Hospital of Shanxi Medical University, taiyuan, Shanxi 030000, China; First clinical medical college, Shanxi Medical University, Taiyuan, Shanxi 030000, China
| | - Yanfang Wang
- Department of Psychiatry, First Hospital of Shanxi Medical University, taiyuan, Shanxi 030000, China; First clinical medical college, Shanxi Medical University, Taiyuan, Shanxi 030000, China.
| | - Kerang Zhang
- Department of Psychiatry, First Hospital of Shanxi Medical University, taiyuan, Shanxi 030000, China; First clinical medical college, Shanxi Medical University, Taiyuan, Shanxi 030000, China.
| |
Collapse
|
31
|
Bréchet L, Michel CM. EEG Microstates in Altered States of Consciousness. Front Psychol 2022; 13:856697. [PMID: 35572333 PMCID: PMC9094618 DOI: 10.3389/fpsyg.2022.856697] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 03/11/2022] [Indexed: 11/16/2022] Open
Abstract
Conscious experiences unify distinct phenomenological experiences that seem to be continuously evolving. Yet, empirical evidence shows that conscious mental activity is discontinuous and can be parsed into a series of states of thoughts that manifest as discrete spatiotemporal patterns of global neuronal activity lasting for fractions of seconds. EEG measures the brain’s electrical activity with high temporal resolution on the scale of milliseconds and, therefore, might be used to investigate the fast spatiotemporal structure of conscious mental states. Such analyses revealed that the global scalp electric fields during spontaneous mental activity are parceled into blocks of stable topographies that last around 60–120 ms, the so-called EEG microstates. These brain states may be representing the basic building blocks of consciousness, the “atoms of thought.” Altered states of consciousness, such as sleep, anesthesia, meditation, or psychiatric diseases, influence the spatiotemporal dynamics of microstates. In this brief perspective, we suggest that it is possible to examine the underlying characteristics of self-consciousness using this EEG microstates approach. Specifically, we will summarize recent results on EEG microstate alterations in mind-wandering, meditation, sleep and anesthesia, and discuss the functional significance of microstates in altered states of consciousness.
Collapse
Affiliation(s)
- Lucie Bréchet
- Functional Brain Mapping Laboratory, Department of Fundamental Neuroscience, University of Geneva, Geneva, Switzerland.,Department of Neurology, Harvard Medical School, Boston, MA, United States
| | - Christoph M Michel
- Functional Brain Mapping Laboratory, Department of Fundamental Neuroscience, University of Geneva, Geneva, Switzerland.,Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
| |
Collapse
|
32
|
Kabbara A, Robert G, Khalil M, Verin M, Benquet P, Hassan M. An electroencephalography connectome predictive model of major depressive disorder severity. Sci Rep 2022; 12:6816. [PMID: 35473962 PMCID: PMC9042869 DOI: 10.1038/s41598-022-10949-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 04/05/2022] [Indexed: 11/21/2022] Open
Abstract
Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N = 328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r = 0.6, p = 4 × 10-18) using intrinsic functional connectivity in the EEG alpha band (8-13 Hz). In particular, the brain regions which contributed the most to the predictive network belong to the default mode network. We further tested the predictive potential of the established model by conducting two external validations on (N1 = 53, N2 = 154). Results showed statistically significant correlations between the predicted and the measured depression scale scores (r1 = 0.52, r2 = 0.44, p < 0.001). These findings lay the foundation for developing a generalizable and scientifically interpretable EEG network-based markers that can ultimately support clinicians in a biologically-based characterization of MDD.
Collapse
Affiliation(s)
- Aya Kabbara
- Lebanese Association for Scientific Research, Tripoli, Lebanon
- MINDig, F-35000, Rennes, France
| | - Gabriel Robert
- Academic Department of Psychiatry, Centre Hospitalier Guillaume Régnier, Rennes, France
- Empenn, U1228, IRISA, UMR 6074, Rennes, France
- Comportement et Noyaux Gris Centraux, EA 4712, CHU Rennes, Université de Rennes 1, 35000, Rennes, France
| | - Mohamad Khalil
- Azm Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon
- CRSI Research Center, Faculty of Engineering, Lebanese University, Beirut, Lebanon
| | - Marc Verin
- Comportement et Noyaux Gris Centraux, EA 4712, CHU Rennes, Université de Rennes 1, 35000, Rennes, France
- Univ Rennes, Inserm, LTSI-U1099, F-35000, Rennes, France
| | - Pascal Benquet
- Univ Rennes, Inserm, LTSI-U1099, F-35000, Rennes, France
| | - Mahmoud Hassan
- MINDig, F-35000, Rennes, France.
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland.
| |
Collapse
|
33
|
Niu Z, Jia L, Liu Y, Wang Q, Li Y, Yang L, Li X, Wang X. Scale-free dynamics of microstate sequence in negative schizophrenia and depressive disorder. Comput Biol Med 2022; 143:105287. [PMID: 35172224 DOI: 10.1016/j.compbiomed.2022.105287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/10/2022] [Accepted: 01/24/2022] [Indexed: 12/01/2022]
Abstract
OBJECTIVE Negative schizophrenia (NSZ) and depressive disorder (DE) have many clinical similarities (e.g., lack of energy, social withdrawal). The purpose of this study was to explore microstate (MS) and scale-free dynamics of microstate sequence (SFML) in NSZ patients, DE patients and healthy controls (HC). METHODS The subjects included 30 NSZ patients, 32 DE patients and 34 age-matched healthy controls. A resting-state electroencephalogram (rsEEG) was recorded under two conditions: (1) resting state with eyes opened (EO) and (2) resting state with eyes closed (EC). First, rsEEG signals were filtered into 1-45 Hz. Then, MS analysis was performed using the Microstate EEGLAB toolbox. Finally, the SFML feature of the sequence, which was transformed from the MS label sequence, was extracted by the Hurst exponent (HE). RESULTS The rsEEG data of all subjects were clustered into six topographies. We could conclude that DE and NSZ patients show similar abnormalities in EO state. However, in the EC state, MS A, and B values were unique to NSZ patients, while DE patients had different values for MS C D and F. We also found a large correlation between these features and clinical information. In SFML, the Hurst exponent of the EO state might be more useful in assessing the characteristics of NSZ, while that of EC state can be used to understand these disorders with different random walk classifications. SIGNIFICANCE The methods are associated with the ability to dynamically change of brain and information processing system. The MS and SFML of the EO state can be used to reflect the similar abnormalities of NSZ and DE patients. We recommend the EC state as the appropriate state to study the difference between the disorders. By combing the two states and these method, we can learn and study more similarities and differences between NSZ and DE.
Collapse
Affiliation(s)
- Zikang Niu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern, Beijing Normal University, Beijing, China
| | - Lina Jia
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yi Liu
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Qian Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yang Li
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Lijuan Yang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern, Beijing Normal University, Beijing, China.
| | - Xue Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| |
Collapse
|
34
|
Yan DD, Zhao LL, Song XW, Zang XH, Yang LC. Automated detection of clinical depression based on convolution neural network model. BIOMED ENG-BIOMED TE 2022; 67:131-142. [PMID: 35142145 DOI: 10.1515/bmt-2021-0232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 01/24/2022] [Indexed: 11/15/2022]
Abstract
As a common mental disorder, depression is placing an increasing burden on families and society. However, the current methods of depression detection have some limitations, and it is essential to find an objective and efficient method. With the development of automation and artificial intelligence, computer-aided diagnosis has attracted more and more attention. Therefore, exploring the use of deep learning (DL) to detect depression has valuable potential. In this paper, convolutional neural network (CNN) is applied to build a diagnostic model for depression based on electroencephalogram (EEG). EEG recordings are analyzed by three different CNN structures, namely EEGNet, DeepConvNet and ShallowConvNet, to dichotomize depression patients and healthy controls. EEG data were collected in the resting state from three electrodes (Fp1, Fz, Fp2) among 80 subjects (40 depressive patients and 40 normal subjects). After the preprocessing step, the DL structures are employed to classify the data, and their recognition performance is evaluated by comparing the classification results. The classification performance shows that depression was effectively detected using EEGNet with 93.74% accuracy, 94.85% sensitivity and 92.61% specificity. In the process of optimizing the parameters of EEGNet structure, the highest accuracy can reach 94.27%. Compared with traditional diagnostic methods, EEGNet is highly worthy for the future depression detection and valuable in terms of accuracy and speed.
Collapse
Affiliation(s)
- Dan-Dan Yan
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Lu-Lu Zhao
- School of Control Science and Engineering, Shandong University, Jinan, China.,School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Xin-Wang Song
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Xiao-Han Zang
- School of Control Science and Engineering, Shandong University, Jinan, China
| | - Li-Cai Yang
- School of Control Science and Engineering, Shandong University, Jinan, China
| |
Collapse
|
35
|
A survey of brain network analysis by electroencephalographic signals. Cogn Neurodyn 2022; 16:17-41. [PMID: 35126769 PMCID: PMC8807775 DOI: 10.1007/s11571-021-09689-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 04/25/2021] [Accepted: 05/31/2021] [Indexed: 02/03/2023] Open
Abstract
Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.
Collapse
|
36
|
Sun Y, Ren G, Ren J, Wang Q. Intrinsic Brain Activity in Temporal Lobe Epilepsy With and Without Depression: Insights From EEG Microstates. Front Neurol 2022; 12:753113. [PMID: 35058871 PMCID: PMC8764160 DOI: 10.3389/fneur.2021.753113] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 12/03/2021] [Indexed: 11/18/2022] Open
Abstract
Background: Depression is the most common psychiatric comorbidity of temporal lobe epilepsy (TLE). In the recent years, studies have focused on the common pathogenesis of TLE and depression. However, few of the studies focused on the dynamic characteristics of TLE with depression. We tested the hypotheses that there exist abnormalities in microstates in patients with TLE with depression. Methods: Participants were classified into patients with TLE with depression (PDS) (n = 19) and patients with TLE without depression (nPDS) (n = 19) based upon the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V). Microstate analysis was applied based on 256-channel electroencephalography (EEG) to detect the dynamic changes in whole brain. The coverage (proportion of time spent in each state), frequency of occurrence, and duration (average time of each state) were calculated. Results: Patients with PDS showed a shorter mean microstate duration with higher mean occurrence per second compared to patients with nPDS. There was no difference between the two groups in the coverage of microstate A–D. Conclusion: This is the first study to present the temporal fluctuations of EEG topography in comorbid depression in TLE using EEG microstate analysis. The temporal characteristics of the four canonical EEG microstates were significantly altered in patients with TLE suffer from comorbid depression.
Collapse
Affiliation(s)
- Yueqian Sun
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Guoping Ren
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Center for Clinical Medicine of Neurological Diseases, Beijing, China
| | - Jiechuan Ren
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Center for Clinical Medicine of Neurological Diseases, Beijing, China
| | - Qun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Center for Clinical Medicine of Neurological Diseases, Beijing, China.,Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, China
| |
Collapse
|
37
|
Tomescu MI, Papasteri CC, Sofonea A, Boldasu R, Kebets V, Pistol CAD, Poalelungi C, Benescu V, Podina IR, Nedelcea CI, Berceanu AI, Carcea I. Spontaneous thought and microstate activity modulation by social imitation. Neuroimage 2022; 249:118878. [PMID: 34999201 DOI: 10.1016/j.neuroimage.2022.118878] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 12/05/2021] [Accepted: 01/05/2022] [Indexed: 02/08/2023] Open
Abstract
The human mind wanders spontaneously and frequently, revisiting the past and imagining the future of self and of others. External and internal factors can influence wandering spontaneous thoughts, whose content predicts subsequent emotional states. We propose that social imitation, an action that increases well-being and closeness by poorly understood mechanisms, impacts behavioural states in part by modulating post-imitation mind-wandering. In 43 young subjects, we find that imitating the arm movements of an actor alters the dynamics and the content of subsequent resting-state spontaneous thoughts. Imitation-sensitive features of spontaneous thoughts correlate with behavioural states and personality traits. EEG microstate analysis reveals that global patterns of correlated neuronal activity predict imitation-induced changes in spontaneous thoughts. Exploratory analyses indicate a possible modulatory effect of social imitation via the endogenous release of oxytocin. Thus, social imitation can induce selective modulations of ongoing activity in specific neural networks to change spontaneous thought patterns as a function of personality traits, and to ultimately orchestrate behavioural states.
Collapse
Affiliation(s)
- Miralena I Tomescu
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania; Department of Psychology and Educational Sciences, University of Bucharest, Bucharest, Romania; Department of Educational Sciences, University "Stefan cel Mare" of Suceava, Suceava, Romania
| | - Claudiu C Papasteri
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania; Department of Psychology and Educational Sciences, University of Bucharest, Bucharest, Romania
| | - Alexandra Sofonea
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania
| | - Romina Boldasu
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania
| | - Valeria Kebets
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - Constantin A D Pistol
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania
| | - Catalina Poalelungi
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania; Department of Biology, University of Bucharest, Bucharest, Romania
| | - Vlad Benescu
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania
| | - Ioana R Podina
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania; Department of Psychology and Educational Sciences, University of Bucharest, Bucharest, Romania
| | - Catalin I Nedelcea
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania; Department of Psychology and Educational Sciences, University of Bucharest, Bucharest, Romania
| | - Alexandru I Berceanu
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania
| | - Ioana Carcea
- CINETic Center, National University of Theatre and Film "I.L. Caragiale" Bucharest, Bucharest, Romania; Department of Pharmacology, Physiology and Neuroscience, Rutgers Brain Health Institute, Rutgers, The State University of New Jersey, USA.
| |
Collapse
|
38
|
Al Zoubi O, Mayeli A, Misaki M, Tsuchiyagaito A, Zotev V, Refai H, Paulus M, Bodurka J. Canonical EEG microstates transitions reflect switching among BOLD resting state networks and predict fMRI signal. J Neural Eng 2022; 18:10.1088/1741-2552/ac4595. [PMID: 34937003 PMCID: PMC11008726 DOI: 10.1088/1741-2552/ac4595] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 12/22/2021] [Indexed: 11/12/2022]
Abstract
Objective.Electroencephalography (EEG) microstates (MSs), which reflect a large topographical representation of coherent electrophysiological brain activity, are widely adopted to study cognitive processes mechanisms and aberrant alterations in brain disorders. MS topographies are quasi-stable lasting between 60-120 ms. Some evidence suggests that MS are the electrophysiological signature of resting-state networks (RSNs). However, the spatial and functional interpretation of MS and their association with functional magnetic resonance imaging (fMRI) remains unclear.Approach. In a cohort of healthy subjects (n= 52), we conducted several statistical and machine learning (ML) approaches analyses on the association among MS spatio-temporal dynamics and the blood-oxygenation-level dependent (BOLD) simultaneous EEG-fMRI data using statistical and ML approaches.Main results.Our results using a generalized linear model showed that MS transitions were largely and negatively associated with BOLD signals in the somatomotor, visual, dorsal attention, and ventral attention fMRI networks with limited association within the default mode network. Additionally, a novel recurrent neural network (RNN) confirmed the association between MS transitioning and fMRI signal while revealing that MS dynamics can model BOLD signals and vice versa.Significance.Results suggest that MS transitions may represent the deactivation of fMRI RSNs and provide evidence that both modalities measure common aspects of undergoing brain neuronal activities. These results may help to better understand the electrophysiological interpretation of MS.
Collapse
Affiliation(s)
- Obada Al Zoubi
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
- Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, United States of America
- Harvard Medical School, Boston, United States of America
| | - Ahmad Mayeli
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
- Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, United States of America
| | - Masaya Misaki
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
| | - Aki Tsuchiyagaito
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
| | - Vadim Zotev
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
| | | | - Hazem Refai
- Electrical and Computer Engineering, University of Oklahoma, Tulsa, OK, United States of America
| | - Martin Paulus
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, OK, United States of America
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, United States of America
- Deceased
| |
Collapse
|
39
|
Terpou BA, Shaw SB, Théberge J, Férat V, Michel CM, McKinnon MC, Lanius RA, Ros T. Spectral decomposition of EEG microstates in post-traumatic stress disorder. NEUROIMAGE: CLINICAL 2022; 35:103135. [PMID: 36002969 PMCID: PMC9421541 DOI: 10.1016/j.nicl.2022.103135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 06/09/2022] [Accepted: 07/27/2022] [Indexed: 11/25/2022] Open
Abstract
EEG microstates reveal significant temporal differences in PTSD. Microstate E (with centro-posterior maximum) is temporally underrepresented in PTSD. In PTSD, microstate E has a reduced occurrence and a shorter mean duration. Spectral decomposition of EEG microstates improves microstate-based classification. Alpha band SVM features yield the highest classification accuracy of PTSD (76%).
Microstates offer a promising framework to study fast-scale brain dynamics in the resting-state electroencephalogram (EEG). However, microstate dynamics have yet to be investigated in post-traumatic stress disorder (PTSD), despite research demonstrating resting-state alterations in PTSD. We performed microstate-based segmentation of resting-state EEG in a clinical population of participants with PTSD (N = 61) and a non-traumatized, healthy control group (N = 61). Microstate-based measures (i.e., occurrence, mean duration, time coverage) were compared group-wise using broadband (1–30 Hz) and frequency-specific (i.e., delta, theta, alpha, beta bands) decompositions. In the broadband comparisons, the centro-posterior maximum microstate (map E) occurred significantly less frequently (d = -0.64, pFWE = 0.03) and had a significantly shorter mean duration in participants with PTSD as compared to controls (d = -0.71, pFWE < 0.01). These differences were reflected in the narrow frequency bands as well, with lower frequency bands like delta (d = -0.78, pFWE < 0.01), theta (d = -0.74, pFWE = 0.01), and alpha (d = -0.65, pFWE = 0.02) repeating these group-level trends, only with larger effect sizes. Interestingly, a support vector machine classification analysis comparing broadband and frequency-specific measures revealed that models containing only alpha band features significantly out-perform broadband models. When classifying PTSD, the classification accuracy was 76 % and 65 % for the alpha band and the broadband model, respectively (p = 0.03). Taken together, we provide original evidence supporting the clinical utility of microstates as diagnostic markers of PTSD and demonstrate that filtering EEG into distinct frequency bands significantly improves microstate-based classification of a psychiatric disorder.
Collapse
|
40
|
Gold MC, Yuan S, Tirrell E, Kronenberg EF, Kang JWD, Hindley L, Sherif M, Brown JC, Carpenter LL. Large-scale EEG neural network changes in response to therapeutic TMS. Brain Stimul 2022; 15:316-325. [PMID: 35051642 PMCID: PMC8957581 DOI: 10.1016/j.brs.2022.01.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 01/04/2022] [Accepted: 01/10/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Transcranial magnetic stimulation (TMS) is an effective therapy for patients with treatment-resistant depression. TMS likely induces functional connectivity changes in aberrant circuits implicated in depression. Electroencephalography (EEG) "microstates" are topographies hypothesized to represent large-scale resting networks. Canonical microstates have recently been proposed as markers for major depressive disorder (MDD), but it is not known if or how they change following TMS. METHODS Resting EEG was obtained from 49 MDD patients at baseline and following six weeks of daily TMS. Polarity-insensitive modified k-means clustering was used to segment EEGs into constituent microstates. Microstates were localized via sLORETA. Repeated-measures mixed models tested for within-subject differences over time and t-tests compared microstate features between TMS responder and non-responder groups. RESULTS Six microstates (MS-1 - MS-6) were identified from all available EEG data. Clinical response to TMS was associated with increases in features of MS-2, along with decreased metrics of MS-3. Nonresponders showed no significant changes in any microstate. Change in occurrence and coverage of both MS-2 (increased) and MS-3 (decreased) correlated with symptom change magnitude over the course of TMS treatment. CONCLUSIONS We identified EEG microstates associated with clinical improvement following a course of TMS therapy. Results suggest selective modulation of resting networks observable by EEG, which is inexpensive and easily acquired in the clinic setting.
Collapse
Affiliation(s)
- Michael C. Gold
- Butler Hospital TMS Clinic and Neuromodulation Research Facility, Providence RI,Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University
| | - Shiwen Yuan
- Butler Hospital TMS Clinic and Neuromodulation Research Facility, Providence RI,Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University
| | - Eric Tirrell
- Butler Hospital TMS Clinic and Neuromodulation Research Facility, Providence RI
| | | | - Jee Won D. Kang
- Butler Hospital TMS Clinic and Neuromodulation Research Facility, Providence RI
| | - Lauren Hindley
- Butler Hospital TMS Clinic and Neuromodulation Research Facility, Providence RI
| | - Mohamed Sherif
- Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University,Lifespan Physician Group, Rhode Island Hospital, Providence RI
| | - Joshua C. Brown
- Butler Hospital TMS Clinic and Neuromodulation Research Facility, Providence RI,Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University
| | - Linda L. Carpenter
- Butler Hospital TMS Clinic and Neuromodulation Research Facility, Providence RI,Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University
| |
Collapse
|
41
|
Nikolin S, Tan YY, Martin D, Moffa A, Loo CK, Boonstra TW. Behavioural and neurophysiological differences in working memory function of depressed patients and healthy controls. J Affect Disord 2021; 295:559-568. [PMID: 34509071 DOI: 10.1016/j.jad.2021.08.083] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 08/24/2021] [Accepted: 08/26/2021] [Indexed: 11/24/2022]
Abstract
OBJECTIVE Major depressive disorder (MDD) is associated with deficits in working memory. Several cognitive subprocesses interact to produce working memory, including attention, encoding, maintenance and manipulation. We sought to clarify the contribution of functional deficits in these subprocesses in MDD by varying cognitive load during a working memory task. METHODS 41 depressed participants and 41 age and gender-matched healthy controls performed the n-back working memory task at three levels of difficulty (0-, 1-, and 2-back) in a pregistered study. We assessed response times, accuracy, and event-related electroencephalography (EEG), including P2 and P3 amplitudes, and frontal theta power (4-8 Hz). RESULTS MDD participants had prolonged response times and more positive frontal P3 amplitudes (i.e., Fz) relative to controls, mainly in the most difficult 2-back condition. Working memory accuracy, P2 amplitudes and frontal theta event-related synchronisation did not differ between groups at any level of task difficulty. CONCLUSIONS Depression is associated with generalized psychomotor slowing of working memory processes, and may involve compensatory hyperactivity in frontal and parietal regions. SIGNIFICANCE These findings provide insights into MDD working memory deficits, indicating that depressed individuals dedicate greater levels of cortical processing and cognitive resources to achieve comparable working memory performance to controls.
Collapse
Affiliation(s)
- Stevan Nikolin
- School of Psychiatry, University of New South Wales, Sydney, Australia; Black Dog Institute, Hospital Road, Sydney, Randwick NSW 2031, Australia.
| | - Yi Yin Tan
- School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Donel Martin
- School of Psychiatry, University of New South Wales, Sydney, Australia; Black Dog Institute, Hospital Road, Sydney, Randwick NSW 2031, Australia
| | - Adriano Moffa
- School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Colleen K Loo
- School of Psychiatry, University of New South Wales, Sydney, Australia; Black Dog Institute, Hospital Road, Sydney, Randwick NSW 2031, Australia; St. George Hospital, Sydney, Australia
| | - Tjeerd W Boonstra
- School of Psychiatry, University of New South Wales, Sydney, Australia; Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| |
Collapse
|
42
|
Relationship between Spatiotemporal Dynamics of the Brain at Rest and Self-Reported Spontaneous Thoughts: An EEG Microstate Approach. J Pers Med 2021; 11:jpm11111216. [PMID: 34834568 PMCID: PMC8625384 DOI: 10.3390/jpm11111216] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/12/2021] [Accepted: 11/13/2021] [Indexed: 11/17/2022] Open
Abstract
Rationale: The resting-state paradigm is frequently applied in electroencephalography (EEG) research; however, it is associated with the inability to control participants’ thoughts. To quantify subjects’ subjective experiences at rest, the Amsterdam Resting-State Questionnaire (ARSQ) was introduced covering ten dimensions of mind wandering. We aimed to estimate associations between subjective experiences and resting-state microstates of EEG. Methods: 5 min resting-state EEG data of 197 subjects was used to evaluate temporal properties of seven microstate classes. Bayesian correlation approach was implemented to assess associations between ARSQ domains assessed after resting and parameters of microstates. Results: Several associations between Comfort, Self and Somatic Awareness domains and temporal properties of neuroelectric microstates were revealed. The positive correlation between Comfort and duration of microstates E showed the strongest evidence (BF10 > 10); remaining correlations showed substantial evidence (10 > BF10 > 3). Conclusion: Our study indicates the relevance of assessments of spontaneous thought occurring during the resting-state for the understanding of the intrinsic brain activity reflected in microstates.
Collapse
|
43
|
Armonaite K, Bertoli M, Paulon L, Gianni E, Balsi M, Conti L, Tecchio F. Neuronal Electrical Ongoing Activity as Cortical Areas Signature: An Insight from MNI Intracerebral Recording Atlas. Cereb Cortex 2021; 32:2895-2906. [PMID: 34727186 DOI: 10.1093/cercor/bhab389] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 09/28/2021] [Accepted: 09/29/2021] [Indexed: 11/14/2022] Open
Abstract
The time course of the neuronal activity in the brain network, the neurodynamics, reflects the structure and functionality of the generating neuronal pools. Here, using the intracranial stereo-electroencephalographic (sEEG) recordings of the public Montreal Neurological Institute (MNI) atlas, we investigated the neurodynamics of primary motor (M1), somatosensory (S1) and auditory (A1) cortices measuring power spectral densities (PSD) and Higuchi fractal dimension (HFD) in the same subject (M1 vs. S1 in 16 subjects, M1 vs. A1 in 9, S1 vs. A1 in 6). We observed specific spectral features in M1, which prevailed above beta band, S1 in the alpha band, and A1 in the delta band. M1 HFD was higher than S1, both higher than A1. A clear distinction of neurodynamics properties of specific primary cortices supports the efforts in cortical parceling based on this expression of the local cytoarchitecture and connectivity. In this perspective, we selected within the MNI intracortical database a first set of primary motor, somatosensory and auditory cortices' representatives to query in recognizing ongoing patterns of neuronal communication. Potential clinical impact stands primarily in exploiting such exchange patterns to enhance the efficacy of neuromodulation intervention to cure symptoms secondary to neuronal activity unbalances.
Collapse
Affiliation(s)
| | - Massimo Bertoli
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Rome 00185, Italy.,Department of Neuroscience, Imaging and Clinical Sciences, University 'Gabriele D'Annunzio' of Chieti-Pescara, Chieti 66100, Italy
| | - Luca Paulon
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Rome 00185, Italy
| | - Eugenia Gianni
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Rome 00185, Italy.,Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, Rome 00128, Italy
| | - Marco Balsi
- Department of Information Engineering, Electronics and Telecommunications, Sapienza University, Rome 00185, Italy
| | - Livio Conti
- Faculty of Engineering, Uninettuno University, Rome 00186, Italy.,INFN - Istituto Nazionale di Fisica Nucleare, Sezione Roma Tor Vergata, Rome 00133, Italy
| | - Franca Tecchio
- Laboratory of Electrophysiology for Translational NeuroScience (LET'S), Institute of Cognitive Sciences and Technologies - Consiglio Nazionale delle Ricerche, Rome 00185, Italy
| |
Collapse
|
44
|
Ouchani M, Gharibzadeh S, Jamshidi M, Amini M. A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5425569. [PMID: 34746303 PMCID: PMC8566072 DOI: 10.1155/2021/5425569] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 06/20/2021] [Accepted: 10/18/2021] [Indexed: 01/27/2023]
Abstract
This study will concentrate on recent research on EEG signals for Alzheimer's diagnosis, identifying and comparing key steps of EEG-based Alzheimer's disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting general approaches, variations, and agreement in the use of EEG identified shortcomings and guidelines for multiple experimental stages ranging from demographic characteristics to outcomes monitoring for future research. Two main targets have been defined based on the article's purpose: (1) discriminative (or detection), i.e., look for differences in EEG-based features across groups, such as MCI, moderate Alzheimer's disease, extreme Alzheimer's disease, other forms of dementia, and stable normal elderly controls; and (2) progression determination, i.e., look for correlations between EEG-based features and clinical markers linked to MCI-to-AD conversion and Alzheimer's disease intensity progression. Limitations mentioned in the reviewed papers were also gathered and explored in this study, with the goal of gaining a better understanding of the problems that need to be addressed in order to advance the use of EEG in Alzheimer's disease science.
Collapse
Affiliation(s)
- Mahshad Ouchani
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Shahriar Gharibzadeh
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Mahdieh Jamshidi
- Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Morteza Amini
- Shahid Beheshti University, Tehran, Iran
- Institute for Cognitive Science Studies (ICSS), Tehran, Iran
| |
Collapse
|
45
|
Ke M, Li J, Wang L. Alteration in Resting-State EEG Microstates Following 24 Hours of Total Sleep Deprivation in Healthy Young Male Subjects. Front Hum Neurosci 2021; 15:636252. [PMID: 33912019 PMCID: PMC8075097 DOI: 10.3389/fnhum.2021.636252] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 03/15/2021] [Indexed: 11/13/2022] Open
Abstract
Purpose: The cognitive effects of total sleep deprivation (TSD) on the brain remain poorly understood. Electroencephalography (EEG) is a very useful tool for detecting spontaneous brain activity in the resting state. Quasi-stable electrical distributions, known as microstates, carry useful information about the dynamics of large-scale brain networks. In this study, microstate analysis was used to study changes in brain activity after 24 h of total sleep deprivation. Participants and Methods: Twenty-seven healthy volunteers were recruited and underwent EEG scans before and after 24 h of TSD. Microstate analysis was applied, and six microstate classes (A–F) were identified. Topographies and temporal parameters of the microstates were compared between the rested wakefulness (RW) and TSD conditions. Results: Microstate class A (a right-anterior to left-posterior orientation of the mapped field) showed lower global explained variance (GEV), frequency of occurrence, and time coverage in TSD than RW, whereas microstate class D (a fronto-central extreme location of the mapped field) displayed higher GEV, frequency of occurrence, and time coverage in TSD compared to RW. Moreover, subjective sleepiness was significantly negatively correlated with the microstate parameters of class A and positively correlated with the microstate parameters of class D. Transition analysis revealed that class B exhibited a higher probability of transition than did classes D and F in TSD compared to RW. Conclusion: The observation suggests alterations of the dynamic brain-state properties of TSD in healthy young male subjects, which may serve as system-level neural underpinnings for cognitive declines in sleep-deprived subjects.
Collapse
Affiliation(s)
- Ming Ke
- College of Computer and Communication, Lanzhou University of Technology, Gansu, China
| | - Jianpan Li
- College of Computer and Communication, Lanzhou University of Technology, Gansu, China.,Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing, China
| | - Lubin Wang
- Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, Beijing, China
| |
Collapse
|
46
|
Kim K, Duc NT, Choi M, Lee B. EEG microstate features according to performance on a mental arithmetic task. Sci Rep 2021; 11:343. [PMID: 33431963 PMCID: PMC7801706 DOI: 10.1038/s41598-020-79423-7] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Accepted: 11/30/2020] [Indexed: 11/16/2022] Open
Abstract
In this study, we hypothesized that task performance could be evaluated applying EEG microstate to mental arithmetic task. This pilot study also aimed at evaluating the efficacy of microstates as novel features to discriminate task performance. Thirty-six subjects were divided into good and poor performers, depending on how well they performed the task. Microstate features were derived from EEG recordings during resting and task states. In the good performers, there was a decrease in type C and an increase in type D features during the task compared to the resting state. Mean duration and occurrence decreased and increased, respectively. In the poor performers, occurrence of type D feature, mean duration and occurrence showed greater changes. We investigated whether microstate features were suitable for task performance classification and eleven features including four archetypes were selected by recursive feature elimination (RFE). The model that implemented them showed the highest classification performance for differentiating between groups. Our pilot findings showed that the highest mean Area Under Curve (AUC) was 0.831. This study is the first to apply EEG microstate features to specific cognitive tasks in healthy subjects, suggesting that EEG microstate features can reflect task achievement.
Collapse
Affiliation(s)
- Kyungwon Kim
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), 123 Cheomdan-gwagiro, Buk-gu, Gwangju, 61005, South Korea
| | - Nguyen Thanh Duc
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), 123 Cheomdan-gwagiro, Buk-gu, Gwangju, 61005, South Korea
| | - Min Choi
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), 123 Cheomdan-gwagiro, Buk-gu, Gwangju, 61005, South Korea
| | - Boreom Lee
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), 123 Cheomdan-gwagiro, Buk-gu, Gwangju, 61005, South Korea.
| |
Collapse
|
47
|
He Y, Yu Q, Yang T, Zhang Y, Zhang K, Jin X, Wu S, Gao X, Huang C, Cui X, Luo X. Abnormalities in Electroencephalographic Microstates Among Adolescents With First Episode Major Depressive Disorder. Front Psychiatry 2021; 12:775156. [PMID: 34975577 PMCID: PMC8718790 DOI: 10.3389/fpsyt.2021.775156] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 11/24/2021] [Indexed: 12/28/2022] Open
Abstract
Background: Recent studies have reported changes in the electroencephalograms (EEG) of patients with major depressive disorder (MDD). However, little research has explored EEG differences between adolescents with MDD and healthy controls, particularly EEG microstates differences. The aim of the current study was to characterize EEG microstate activity in adolescents with MDD and healthy controls (HCs). Methods: A total of 35 adolescents with MDD and 35 HCs were recruited in this study. The depressive symptoms were assessed by Hamilton Depression Scale (HAMD) and Children's Depression Inventory (CDI), and the anxiety symptoms were assessed by Chinese version of DSM-5 Level 2-Anxiety-Child scale. A 64-channel EEG was recorded for 5 min (eye closed, resting-state) and analyzed using microstate analysis. Microstate properties were compared between groups and correlated with patients' depression scores. Results: We found increased occurrence and contribution of microstate B in MDD patients compared to HCs, and decreased occurrence and contribution of microstate D in MDD patients compared to HCs. While no significant correlation between depression severity (HAMD score) and the microstate metrics (occurrence and contribution of microstate B and D) differing between MDD adolescents and HCs was found. Conclusions: Adolescents with MDD showed microstate B and microstate D changes. The obtained results may deepen our understanding of dynamic EEG changes among adolescents with MDD and provide some evidence of changes in brain development in adolescents with MDD.
Collapse
Affiliation(s)
- Yuqiong He
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Qianting Yu
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Tingyu Yang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yaru Zhang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Kun Zhang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xingyue Jin
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Shuxian Wu
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xueping Gao
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Autism Center of the Second Xiangya Hospital, Central South University, Changsha, China
| | - Chunxiang Huang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Autism Center of the Second Xiangya Hospital, Central South University, Changsha, China
| | - Xilong Cui
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Autism Center of the Second Xiangya Hospital, Central South University, Changsha, China
| | - Xuerong Luo
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Autism Center of the Second Xiangya Hospital, Central South University, Changsha, China
| |
Collapse
|
48
|
Yan D, Liu J, Liao M, Liu B, Wu S, Li X, Li H, Ou W, Zhang L, Li Z, Zhang Y, Li L. Prediction of Clinical Outcomes With EEG Microstate in Patients With Major Depressive Disorder. Front Psychiatry 2021; 12:695272. [PMID: 34483990 PMCID: PMC8415359 DOI: 10.3389/fpsyt.2021.695272] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 07/19/2021] [Indexed: 02/05/2023] Open
Abstract
Background: The difficulty in timely evaluating patient response to antidepressants has brought great challenge to the treatment of major depressive disorder (MDD). Some studies found that the electroencephalogram (EEG) microstates might be a reliable marker to evaluate patient response to treatment. The present study aims to evaluate the relationship between EEG microstate parameters and MDD symptoms before and after treatment to identify predictive biological markers for patient response. Methods: Thirty drug-naïve MDD patients (20 females and 10 males) were enrolled in this study. All the patients received effective dosages of selective serotonin reuptake inhibitors, and EEG recordings were collected at baseline and 2 weeks of treatment. Brain activities during the eyes-closed state were recorded using 64-channel electroencephalography, and the patients' microstates were clustered into four maps according to their topography (labeled A, B, C, and D). The differences of EEG microstates before and after treatment were compared using paired t-test. Spearman correlation coefficients were calculated to identify the relationships between the improvement of depression and anxiety symptoms and microstate parameters. Results: The mean duration (69.67 ± 10.33 vs. 64.00 ± 7.70, p < 0.001) and occurrence (4.06 ± 0.69, vs. 3.69 ± 0.70, p = 0.002) of microstate B decreased significantly after treatment. The proportion of microstate B also decreased (27.53 ± 5.81, vs. 23.23 ± 4.61, p < 0.001), while the occurrence of microstate A increased after treatment. A significant negative correlation was found between the change of score of Hamilton Rating Scale for Anxiety and the increase of the occurrence of microstate A (r = -0.431, p < 0.05) after 2 weeks of treatment. The reduction of the duration of microstate B was found to be predictive of patient response to antidepressants after 3 months. Conclusion: This study explored the relationship between changes of EEG microstates and patient response to antidepressants. Depression symptoms might be associated with the duration of microstate B and anxiety symptoms related to the occurrence of microstate A. Therefore, the duration of microstate B and the occurrence of microstate A are potential biological markers for MDD patients' early response and further clinical outcomes.
Collapse
Affiliation(s)
- Danfeng Yan
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China.,Shanxi Mental Health Center, Taiyuan, China
| | - Jin Liu
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| | - Mei Liao
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| | - Bangshan Liu
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| | - Shibin Wu
- Department of Psychiatry, Nanning Fifth People's Hospital, Nanning, China
| | - Xueqin Li
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| | - Haolun Li
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| | - Wenwen Ou
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| | - Li Zhang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| | - Zexuan Li
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| | - Yan Zhang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| | - Lingjiang Li
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Hunan Key Laboratory of Psychiatry and Mental Health, Institute of Mental Health, Hunan Medical Center of Mental Health, China National Technology Institute on Mental Disorders, Changsha, China
| |
Collapse
|
49
|
Campanella S, Arikan K, Babiloni C, Balconi M, Bertollo M, Betti V, Bianchi L, Brunovsky M, Buttinelli C, Comani S, Di Lorenzo G, Dumalin D, Escera C, Fallgatter A, Fisher D, Giordano GM, Guntekin B, Imperatori C, Ishii R, Kajosch H, Kiang M, López-Caneda E, Missonnier P, Mucci A, Olbrich S, Otte G, Perrottelli A, Pizzuti A, Pinal D, Salisbury D, Tang Y, Tisei P, Wang J, Winkler I, Yuan J, Pogarell O. Special Report on the Impact of the COVID-19 Pandemic on Clinical EEG and Research and Consensus Recommendations for the Safe Use of EEG. Clin EEG Neurosci 2021; 52:3-28. [PMID: 32975150 PMCID: PMC8121213 DOI: 10.1177/1550059420954054] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
INTRODUCTION The global COVID-19 pandemic has affected the economy, daily life, and mental/physical health. The latter includes the use of electroencephalography (EEG) in clinical practice and research. We report a survey of the impact of COVID-19 on the use of clinical EEG in practice and research in several countries, and the recommendations of an international panel of experts for the safe application of EEG during and after this pandemic. METHODS Fifteen clinicians from 8 different countries and 25 researchers from 13 different countries reported the impact of COVID-19 on their EEG activities, the procedures implemented in response to the COVID-19 pandemic, and precautions planned or already implemented during the reopening of EEG activities. RESULTS Of the 15 clinical centers responding, 11 reported a total stoppage of all EEG activities, while 4 reduced the number of tests per day. In research settings, all 25 laboratories reported a complete stoppage of activity, with 7 laboratories reopening to some extent since initial closure. In both settings, recommended precautions for restarting or continuing EEG recording included strict hygienic rules, social distance, and assessment for infection symptoms among staff and patients/participants. CONCLUSIONS The COVID-19 pandemic interfered with the use of EEG recordings in clinical practice and even more in clinical research. We suggest updated best practices to allow safe EEG recordings in both research and clinical settings. The continued use of EEG is important in those with psychiatric diseases, particularly in times of social alarm such as the COVID-19 pandemic.
Collapse
Affiliation(s)
- Salvatore Campanella
- Laboratoire de Psychologie Médicale et d'Addictologie, ULB Neuroscience Institute (UNI), CHU Brugmann-Université Libre de Bruxelles (U.L.B.), Belgium
| | - Kemal Arikan
- Kemal Arıkan Psychiatry Clinic, Istanbul, Turkey
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "Erspamer", Sapienza University of Rome, Italy.,San Raffaele Cassino, Cassino (FR), Italy
| | - Michela Balconi
- Research Unit in Affective and Social Neuroscience, Department of Psychology, Catholic University of Milan, Milan, Italy
| | - Maurizio Bertollo
- BIND-Behavioral Imaging and Neural Dynamics Center, Department of Neuroscience, Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Viviana Betti
- Department of Psychology, Sapienza University of Rome, Fondazione Santa Lucia, Rome, Italy
| | - Luigi Bianchi
- Dipartimento di Ingegneria Civile e Ingegneria Informatica (DICII), University of Rome Tor Vergata, Rome, Italy
| | - Martin Brunovsky
- National Institute of Mental Health, Klecany Czech Republic.,Third Medical Faculty, Charles University, Prague, Czech Republic
| | - Carla Buttinelli
- Department of Neurosciences, Public Health and Sense Organs (NESMOS), Sapienza University of Rome, Rome, Italy
| | - Silvia Comani
- BIND-Behavioral Imaging and Neural Dynamics Center, Department of Neuroscience, Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Giorgio Di Lorenzo
- Laboratory of Psychophysiology and Cognitive Neuroscience, Chair of Psychiatry, Department of Systems Medicine, School of Medicine and Surgery, University of Rome Tor Vergata, Rome, Italy.,IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Daniel Dumalin
- AZ Sint-Jan Brugge-Oostende AV, Campus Henri Serruys, Lab of Neurophysiology, Department Neurology-Psychiatry, Ostend, Belgium
| | - Carles Escera
- Brainlab-Cognitive Neuroscience Research Group, Department of Clinical Psychology and Psychobiology, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
| | - Andreas Fallgatter
- Department of Psychiatry, University of Tübingen, Germany; LEAD Graduate School and Training Center, Tübingen, Germany.,German Center for Neurodegenerative Diseases DZNE, Tübingen, Germany
| | - Derek Fisher
- Department of Psychology, Mount Saint Vincent University, and Department of Psychiatry, Nova Scotia Health Authority, Halifax, Nova Scotia, Canada
| | | | - Bahar Guntekin
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Claudio Imperatori
- Cognitive and Clinical Psychology Laboratory, Department of Human Science, European University of Rome, Rome, Italy
| | - Ryouhei Ishii
- Department of Psychiatry Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hendrik Kajosch
- Laboratoire de Psychologie Médicale et d'Addictologie, ULB Neuroscience Institute (UNI), CHU Brugmann-Université Libre de Bruxelles (U.L.B.), Belgium
| | - Michael Kiang
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Eduardo López-Caneda
- Psychological Neuroscience Laboratory, Center for Research in Psychology, School of Psychology, University of Minho, Braga, Portugal
| | - Pascal Missonnier
- Mental Health Network Fribourg (RFSM), Sector of Psychiatry and Psychotherapy for Adults, Marsens, Switzerland
| | - Armida Mucci
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Sebastian Olbrich
- Psychotherapy and Psychosomatics, Department for Psychiatry, University Hospital Zurich, Zurich, Switzerland
| | | | - Andrea Perrottelli
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Alessandra Pizzuti
- Department of Psychology, Sapienza University of Rome, Fondazione Santa Lucia, Rome, Italy
| | - Diego Pinal
- Psychological Neuroscience Laboratory, Center for Research in Psychology, School of Psychology, University of Minho, Braga, Portugal
| | - Dean Salisbury
- Clinical Neurophysiology Research Laboratory, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Paolo Tisei
- Department of Neurosciences, Public Health and Sense Organs (NESMOS), Sapienza University of Rome, Rome, Italy
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Istvan Winkler
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - Jiajin Yuan
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Oliver Pogarell
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| |
Collapse
|
50
|
Wang F, Hujjaree K, Wang X. Electroencephalographic Microstates in Schizophrenia and Bipolar Disorder. Front Psychiatry 2021; 12:638722. [PMID: 33716831 PMCID: PMC7952514 DOI: 10.3389/fpsyt.2021.638722] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 02/08/2021] [Indexed: 12/19/2022] Open
Abstract
Schizophrenia (SCH) and bipolar disorder (BD) are characterized by many types of symptoms, damaged cognitive function, and abnormal brain connections. The microstates are considered to be the cornerstones of the mental states shown in EEG data. In our study, we investigated the use of microstates as biomarkers to distinguish patients with bipolar disorder from those with schizophrenia by analyzing EEG data measured in an eyes-closed resting state. The purpose of this article is to provide an electron directional physiological explanation for the observed brain dysfunction of schizophrenia and bipolar disorder patients. Methods: We used microstate resting EEG data to explore group differences in the duration, coverage, occurrence, and transition probability of 4 microstate maps among 20 SCH patients, 26 BD patients, and 35 healthy controls (HCs). Results: Microstate analysis revealed 4 microstates (A-D) in global clustering across SCH patients, BD patients, and HCs. The samples were chosen to be matched. We found the greater presence of microstate B in BD patients, and the less presence of microstate class A and B, the greater presence of microstate class C, and less presence of D in SCH patients. Besides, a greater frequent switching between microstates A and B and between microstates B and A in BD patients than in SCH patients and HCs and less frequent switching between microstates C and D and between microstates D and C in BD patients compared with SCH patients. Conclusion: We found abnormal features of microstate A, B in BD patients and abnormal features of microstate A, B, C, and D in SCH patients. These features may indicate the potential abnormalities of SCH patients and BD patients in distributing neural resources and influencing opportune transitions between different states of activity.
Collapse
Affiliation(s)
- Fanglan Wang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Khamlesh Hujjaree
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xiaoping Wang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| |
Collapse
|