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Kleinert T, Nash K. Trait Aggression is Reflected by a Lower Temporal Stability of EEG Resting Networks. Brain Topogr 2024; 37:514-523. [PMID: 36400856 PMCID: PMC11199292 DOI: 10.1007/s10548-022-00929-6] [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: 07/26/2022] [Accepted: 11/12/2022] [Indexed: 11/19/2022]
Abstract
Trait aggression can lead to catastrophic consequences for individuals and society. However, it remains unclear how aggressive people differ from others regarding basic, task-independent brain characteristics. We used EEG microstate analysis to investigate how the temporal organization of neural resting networks might help explain inter-individual differences in aggression. Microstates represent whole-brain networks, which are stable for short timeframes (40-120 ms) before quickly transitioning into other microstate types. Recent research demonstrates that the general temporal stability of microstates across types predicts higher levels of self-control and inhibitory control, and lower levels of risk-taking preferences. Given that these outcomes are inversely related to aggression, we investigated whether microstate stability at rest would predict lower levels of trait aggression. As males show higher levels of aggression than females, and males and females express aggression differently, we also tested for possible gender-differences. As hypothesized, people with higher levels of trait aggression showed lower microstate stability. This effect was moderated by gender, with men showing stronger associations compared to women. These findings support the notion that temporal dynamics of sub-second resting networks predict complex human traits. Furthermore, they provide initial indications of gender-differences in the functional significance of EEG microstates.
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Affiliation(s)
- Tobias Kleinert
- Department of Psychology, University of Alberta, Edmonton, AB, T6G 2E9, Canada.
- Department of Ergonomics, Leibniz Research Centre for Working Environment and Human Factors, Ardeystr. 67, 44139, Dortmund, Germany.
| | - Kyle Nash
- Department of Psychology, University of Alberta, Edmonton, AB, T6G 2E9, Canada
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Xue R, Li X, Deng W, Liang C, Chen M, Chen J, Liang S, Wei W, Zhang Y, Yu H, Xu Y, Guo W, Li T. Shared and distinct electroencephalogram microstate abnormalities across schizophrenia, bipolar disorder, and depression. Psychol Med 2024:1-8. [PMID: 38738283 DOI: 10.1017/s0033291724001132] [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] [Indexed: 05/14/2024]
Abstract
BACKGROUND Microstates of an electroencephalogram (EEG) are canonical voltage topographies that remain quasi-stable for 90 ms, serving as the foundational elements of brain dynamics. Different changes in EEG microstates can be observed in psychiatric disorders like schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD). However, the similarities and disparatenesses in whole-brain dynamics on a subsecond timescale among individuals diagnosed with SCZ, BD, and MDD are unclear. METHODS This study included 1112 participants (380 individuals diagnosed with SCZ, 330 with BD, 212 with MDD, and 190 demographically matched healthy controls [HCs]). We assembled resting-state EEG data and completed a microstate analysis of all participants using a cross-sectional design. RESULTS Our research indicates that SCZ, BD, and MDD exhibit distinct patterns of transition among the four EEG microstate states (A, B, C, and D). The analysis of transition probabilities showed a higher frequency of switching from microstates A to B and from B to A in each patient group compared to the HC group, and less frequent transitions from microstates A to C and from C to A in the SCZ and MDD groups compared to the HC group. And the probability of the microstate switching from C to D and D to C in the SCZ group significantly increased compared to those in the patient and HC groups. CONCLUSIONS Our findings provide crucial insights into the abnormalities involved in distributing neural assets and enabling proper transitions between different microstates in patients with major psychiatric disorders.
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Affiliation(s)
- Rui Xue
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Xiaojing Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Wei Deng
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Chengqian Liang
- School of Mental Health, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Mingxia Chen
- School of Mental Health, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jianning Chen
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Sugai Liang
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Wei Wei
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Yamin Zhang
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Hua Yu
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Yan Xu
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Wanjun Guo
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
| | - Tao Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou 311121, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou 310058, China
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3
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Chen J, Jin L, Lin N. Utilization of EEG microstates as a prospective biomarker for assessing the impact of ketogenic diet in GLUT1-DS. Neurol Sci 2024:10.1007/s10072-024-07519-3. [PMID: 38589768 DOI: 10.1007/s10072-024-07519-3] [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: 01/30/2024] [Accepted: 04/02/2024] [Indexed: 04/10/2024]
Abstract
OBJECTIVE The aim of the study is to analyze microstate patterns in GLUT1-DS, both before and after the ketogenic diet (KD). METHODS We conducted microstate analysis of a patient with GLUT-1 DS and 27 healthy controls. A systematic literature review and meta-analysis was done. We compared the parameters of the patients with those of healthy controls and the incorporating findings in literature. RESULTS The durations of the patient were notably shorter, and the occurrence rates were longer than those of healthy controls and incorporating findings from the review. After 10 months of KD, the patient's microstate durations exhibited an increase from 53.05 ms, 57.17 ms, 61.80 ms, and 49.49 ms to 60.53 ms, 63.27 ms, 71.11 ms, and 66.55 ms. The occurrence rates changed from 4.0774 Hz, 4.9462 Hz, 4.8006 Hz, and 4.0579 Hz to 3.3354 Hz, 3.7893 Hz, 3.5956 Hz, and 4.1672 Hz. In healthy controls, the durations of microstate class A, B, C, and D were 61.86 ms, 63.58 ms, 70.57 ms, and 72.00 ms, respectively. CONCLUSIONS Our findings suggest EEG microstates may be a promising biomarker for monitoring the effect of KD. Administration of KD may normalize the dysfunctional patterns of temporal parameters.
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Affiliation(s)
- Jianhua Chen
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, 100730, China.
| | - Liri Jin
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, 100730, China.
| | - Nan Lin
- Department of Neurology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, 100730, China
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4
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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.
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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
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5
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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.
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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
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Zanesco AP. Normative Temporal Dynamics of Resting EEG Microstates. Brain Topogr 2024; 37:243-264. [PMID: 37702825 DOI: 10.1007/s10548-023-01004-4] [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: 05/18/2023] [Accepted: 08/23/2023] [Indexed: 09/14/2023]
Abstract
The large-scale electrophysiological events known as electroencephalographic microstates provide an important window into the intrinsic activity of whole-brain neuronal networks. The spontaneous activity of coordinated brain networks, including the ongoing temporal dynamics expressed by microstates, are thought to reflect individuals' neurocognitive functioning, and predict development, disease progression, and psychological differences among varied populations. A comprehensive understanding of human brain function therefore requires characterizing typical and atypical patterns in the temporal dynamics of microstates. But population-level estimates of normative microstate temporal dynamics are still unknown. To address this gap, I conducted a systematic search of the literature and accompanying meta-analysis of the average dynamics of microstates obtained from studies investigating spontaneous brain activity in individuals during periods of eyes-closed and eyes-open rest. Meta-analyses provided estimates of the average temporal dynamics of microstates across 93 studies totaling 6583 unique individual participants drawn from diverse populations. Results quantified the expected range of plausible estimates of average microstate dynamics across study samples, as well as characterized heterogeneity resulting from sampling variability and systematic differences in development, clinical diagnoses, or other study methodological factors. Specifically, microstate dynamics significantly differed for samples with specific developmental differences or clinical diagnoses, relative to healthy, typically developing samples. This research supports the notion that microstates and their dynamics reflect functionally relevant properties of large-scale brain networks, encoding typical and atypical neurocognitive functioning.
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Affiliation(s)
- Anthony P Zanesco
- Department of Psychology, University of Miami, Coral Gables, FL, USA.
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7
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Wiemers MC, Laufs H, von Wegner F. Frequency Analysis of EEG Microstate Sequences in Wakefulness and NREM Sleep. Brain Topogr 2024; 37:312-328. [PMID: 37253955 DOI: 10.1007/s10548-023-00971-y] [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: 06/16/2022] [Accepted: 05/11/2023] [Indexed: 06/01/2023]
Abstract
The majority of EEG microstate analyses concern wakefulness, and the existing sleep studies have focused on changes in spatial microstate properties and on microstate transitions between adjacent time points, the shortest available time scale. We present a more extensive time series analysis of unsmoothed EEG microstate sequences in wakefulness and non-REM sleep stages across many time scales. Very short time scales are assessed with Markov tests, intermediate time scales by the entropy rate and long time scales by a spectral analysis which identifies characteristic microstate frequencies. During the descent from wakefulness to sleep stage N3, we find that the increasing mean microstate duration is a gradual phenomenon explained by a continuous slowing of microstate dynamics as described by the relaxation time of the transition probability matrix. The finite entropy rate, which considers longer microstate histories, shows that microstate sequences become more predictable (less random) with decreasing vigilance level. Accordingly, the Markov property is absent in wakefulness but in sleep stage N3, 10/19 subjects have microstate sequences compatible with a second-order Markov process. A spectral microstate analysis is performed by comparing the time-lagged mutual information coefficients of microstate sequences with the autocorrelation function of the underlying EEG. We find periodic microstate behavior in all vigilance states, linked to alpha frequencies in wakefulness, theta activity in N1, sleep spindle frequencies in N2, and in the delta frequency band in N3. In summary, we show that EEG microstates are a dynamic phenomenon with oscillatory properties that slow down in sleep and are coupled to specific EEG frequencies across several sleep stages.
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Affiliation(s)
- Milena C Wiemers
- Department of Neurology and Clinical Neurophysiology, Lüneburg Hospital, Bögelstrasse 1, 21339, Lüneburg, Germany
| | - Helmut Laufs
- Department of Neurology, Christian-Albrechts University Kiel, Arnold-Heller-Strasse 3, 24105, Kiel, Germany
| | - Frederic von Wegner
- School of Biomedical Sciences, University of New South Wales, Wallace Wurth Building, Kensington, NSW, 2052, Australia.
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8
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Liebrand M, Katsarakis A, Josi J, Diezig S, Michel C, Schultze-Lutter F, Rochas V, Mancini V, Kaess M, Hubl D, Koenig T, Kindler J. EEG microstate D as psychosis-specific correlate in adolescents and young adults with clinical high risk for psychosis and first-episode psychosis. Schizophr Res 2024; 264:49-57. [PMID: 38096659 DOI: 10.1016/j.schres.2023.11.014] [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] [Received: 05/17/2023] [Revised: 10/05/2023] [Accepted: 11/29/2023] [Indexed: 03/01/2024]
Abstract
Resting-state electroencephalography (EEG) microstates are brief periods (60-120 ms) of quasi-stable scalp field potentials, indicating simultaneous activity of large-scale networks. Microstates are assumed to reflect basic neuronal information processing. A common finding in psychosis spectrum disorders is that microstates classes C and D are altered. Whereas evidence in adults with schizophrenia is substantial, little is known about effects in underage patients, particularly in those at clinical high risk for psychosis (CHR) and first-episode psychosis (FEP). The present study used 74-channel EEG to investigate microstate effects in a large sample of patients with CHR (n = 100) and FEP (n = 33), clinical controls (CC, n = 18), as well as age-matched healthy controls (HC, n = 68). Subjects span an age range from 9 to 35 years, thus, covering underage patients as well as the most vulnerable period for the emergence of psychosis and its prodrome. Four EEG microstates classes were analyzed (A-D). In class D, CHR and FEP patients showed a decrease compared to HC, and CHR patients also to CC. An increase in class C was found in CHR and FEP compared to HC but not to CC. Results were independent of age and no differences were found between the psychosis spectrum groups. The findings suggest an age-independent decrease of microstate class D to be specific to the psychosis spectrum, whereas the increase in class C seems to reflect unspecific psychopathology. Overall, present data strengthens the role of microstate D as potential biomarker for psychosis, as early as in adolescence and already in CHR status.
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Affiliation(s)
- Matthias Liebrand
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Angelos Katsarakis
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Johannes Josi
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Sarah Diezig
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Chantal Michel
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Frauke Schultze-Lutter
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Switzerland; Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich-Heine-University, Düsseldorf, Germany; Department of Psychology, Faculty of Psychology, Airlangga University, Surabaya, Indonesia
| | - Vincent Rochas
- Department of Basic Neurosciences, University of Geneva, Campus Biotech, Switzerland
| | - Valentina Mancini
- Department of Basic Neurosciences, University of Geneva, Campus Biotech, Switzerland; Developmental Imaging and Psychopathology Laboratory, University of Geneva School of Medicine, Switzerland
| | - Michael Kaess
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Daniela Hubl
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Jochen Kindler
- University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Switzerland
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Qi X, Zhang X, Shen H, Su J, Gao X, Li Y, Yang H, Gao C, Ni W, Lei Y, Gu Y, Mao Y, Yu Y. Switching of brain networks across different cerebral perfusion states: insights from EEG dynamic microstate analyses. Cereb Cortex 2024; 34:bhae035. [PMID: 38342687 DOI: 10.1093/cercor/bhae035] [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: 12/07/2023] [Revised: 01/16/2024] [Accepted: 01/17/2024] [Indexed: 02/13/2024] Open
Abstract
The alteration of neural interactions across different cerebral perfusion states remains unclear. This study aimed to fulfill this gap by examining the longitudinal brain dynamic information interactions before and after cerebral reperfusion. Electroencephalogram in eyes-closed state at baseline and postoperative 7-d and 3-month follow-ups (moyamoya disease: 20, health controls: 23) were recorded. Dynamic network analyses were focused on the features and networks of electroencephalogram microstates across different microstates and perfusion states. Considering the microstate features, the parameters were disturbed of microstate B, C, and D but preserved of microstate A. The transition probabilities of microstates A-B and B-D were increased to play a complementary role across different perfusion states. Moreover, the microstate variability was decreased, but was significantly improved after cerebral reperfusion. Regarding microstate networks, the functional connectivity strengths were declined, mainly within frontal, parietal, and occipital lobes and between parietal and occipital lobes in different perfusion states, but were ameliorated after cerebral reperfusion. This study elucidates how dynamic interaction patterns of brain neurons change after cerebral reperfusion, which allows for the observation of brain network transitions across various perfusion states in a live clinical setting through direct intervention.
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Affiliation(s)
- Xiaoying Qi
- Department of Physiology, School of Medicine, Yan'an University, Yan'an 716000, China
- School of Life Science and Human Phenome Institute, Research Institute of Intelligent Complex Systems and Institute of Science and Technology for Brain-Inspired Intelligence Fudan University, Shanghai 200433, China
| | - Xin Zhang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Hao Shen
- School of Life Science and Human Phenome Institute, Research Institute of Intelligent Complex Systems and Institute of Science and Technology for Brain-Inspired Intelligence Fudan University, Shanghai 200433, China
| | - Jiabin Su
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Xinjie Gao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Yanjiang Li
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Heng Yang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Chao Gao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Wei Ni
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Yu Lei
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Yuxiang Gu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Yuguo Yu
- School of Life Science and Human Phenome Institute, Research Institute of Intelligent Complex Systems and Institute of Science and Technology for Brain-Inspired Intelligence Fudan University, Shanghai 200433, China
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10
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Zhou DD, Li HZ, Wang W, Kuang L. Changes in oscillatory patterns of microstate sequence in patients with first-episode psychosis. Sci Data 2024; 11:38. [PMID: 38182586 PMCID: PMC10770397 DOI: 10.1038/s41597-023-02892-8] [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: 07/29/2023] [Accepted: 12/27/2023] [Indexed: 01/07/2024] Open
Abstract
We aimed to utilize chaos game representation (CGR) for the investigation of microstate sequences and explore its potential as neurobiomarkers for psychiatric disorders. We applied our proposed method to a public dataset including 82 patients with first-episode psychosis (FEP) and 61 control subjects. Two time series were constructed: one using the microstate spacing distance in CGR and the other using complex numbers representing the microstate coordinates in CGR. Power spectral features of both time series and frequency matrix CGR (FCGR) were compared between groups and employed in a machine learning application. The four canonical microstates (A, B, C, and D) were identified using both shared and separate templates. Our results showed the microstate oscillatory pattern exhibited alterations in the FEP group. Using oscillatory features improved machine learning performance compared with classical features and FCGR. This study opens up new avenues for exploring the use of CGR in analyzing EEG microstate sequences. Features derived from microstate sequence CGR offer fine-grained neurobiomarkers for psychiatric disorders.
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Affiliation(s)
- Dong-Dong Zhou
- Mental Health Center, University-Town Hospital of Chongqing Medical University, Chongqing, China.
| | - Hong-Zhi Li
- 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
| | - 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.
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11
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Zhao Z, Ran X, Lv S, Wang J, Qiu M, Wang C, Xu Y, Guo X, Gao Z, Mu J, Yu Y. Causal link between prefrontal cortex and EEG microstates: evidence from patients with prefrontal lesion. Front Neurosci 2023; 17:1306120. [PMID: 38161794 PMCID: PMC10757643 DOI: 10.3389/fnins.2023.1306120] [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: 10/03/2023] [Accepted: 11/24/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction At present, elucidating the cortical origin of EEG microstates is a research hotspot in the field of EEG. Previous studies have suggested that the prefrontal cortex is closely related to EEG microstate C and D, but whether there is a causal link between the prefrontal cortex and microstate C or D remains unclear. Methods In this study, pretrial EEG data were collected from ten patients with prefrontal lesions (mainly located in inferior and middle frontal gyrus) and fourteen matched healthy controls, and EEG microstate analysis was applied. Results Our results showed that four classical EEG microstate topographies were obtained in both groups, but microstate C topography in patient group was obviously abnormal. Compared to healthy controls, the average coverage and occurrence of microstate C significantly reduced. In addition, the transition probability from microstate A to C and from microstate B to C in patient group was significantly lower than those of healthy controls. Discussion The above results demonstrated that the damage of prefrontal cortex especially inferior and middle frontal gyrus could lead to abnormalities in the spatial distribution and temporal dynamics of microstate C not D, showing that there is a causal link between the inferior and middle frontal gyrus and the microstate C. The significance of our findings lies in providing new evidence for elucidating the cortical origin of microstate C.
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Affiliation(s)
- Zongya Zhao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
- The First Affiliated Hospital of Xinxiang Medical University, Weihui, China
| | - Xiangying Ran
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
| | - Shiyang Lv
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
| | - Junming Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
| | - Mengyue Qiu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
| | - Chang Wang
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
| | - Yongtao Xu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
| | - Xiao Guo
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
| | - Zhixian Gao
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
| | - Junlin Mu
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Yi Yu
- School of Medical Engineering, Xinxiang Medical University, Xinxiang, China
- Engineering Technology Research Center of Neurosense and Control of Henan Province, Xinxiang, China
- Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design, Xinxiang, China
- Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback, Xinxiang, China
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12
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Gengeç Benli Ş. Classification of First-Episode Psychosis with EEG Signals: ciSSA and Machine Learning Approach. Biomedicines 2023; 11:3223. [PMID: 38137444 PMCID: PMC10741114 DOI: 10.3390/biomedicines11123223] [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: 10/06/2023] [Revised: 11/21/2023] [Accepted: 11/23/2023] [Indexed: 12/24/2023] Open
Abstract
First-episode psychosis (FEP) typically marks the onset of severe psychiatric disorders and represents a critical period in the field of mental health. The early diagnosis of this condition is essential for timely intervention and improved clinical outcomes. In this study, the classification of FEP was investigated using the analysis of electroencephalography (EEG) signals and circulant spectrum analysis (ciSSA) sub-band signals. FEP poses a significant diagnostic challenge in the realm of mental health, and it is aimed at introducing a novel and effective approach for early diagnosis. To achieve this, the LASSO method was utilized to select the most significant features derived from entropy, frequency, and statistical-based characteristics obtained from ciSSA sub-band signals, as well as their hybrid combinations. Subsequently, a high-performance classification model has been developed using machine learning techniques, including ensemble, support vector machine (SVM), and artificial neural network (ANN) methods. The results of this study demonstrated that the hybrid features extracted from EEG signals' ciSSA sub-bands, in combination with the SVM method, achieved a high level of performance, with an area under curve (AUC) of 0.9893, an accuracy of 96.23%, a sensitivity of 0.966, a specificity of 0.956, a precision of 0.9667, and an F1 score of 0.9666. This has revealed the effectiveness of the ciSSA-based method for classifying FEP from EEG signals.
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Affiliation(s)
- Şerife Gengeç Benli
- Department of Biomedical Engineering, Faculty of Engineering, Erciyes University, Kayseri 38280, Turkey
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13
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Li X, Kang Q, Gu H. A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder. Front Hum Neurosci 2023; 17:1280512. [PMID: 38021236 PMCID: PMC10646310 DOI: 10.3389/fnhum.2023.1280512] [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: 08/20/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
Obsessive-compulsive disorder (OCD) is a common mental disease, which can exist as a separate disease or become one of the symptoms of other mental diseases. With the development of society, statistically, the incidence rate of obsessive-compulsive disorder has been increasing year by year. At present, in the diagnosis and treatment of OCD, The clinical performance of patients measured by scales is no longer the only quantitative indicator. Clinical workers and researchers are committed to using neuroimaging to explore the relationship between changes in patient neurological function and obsessive-compulsive disorder. Through machine learning and artificial learning, medical information in neuroimaging can be better displayed. In this article, we discuss recent advancements in artificial intelligence related to neuroimaging in the context of Obsessive-Compulsive Disorder.
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Affiliation(s)
- Xuanyi Li
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Qiang Kang
- Department of Radiology, Xing’an League People’s Hospital of Inner Mongolia, Mongolia, China
| | - Hanxing Gu
- Department of Geriatric Psychiatry, Qingdao Mental Health Center, Qingdao, Shandong, China
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14
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Normative Structure of Resting-State EEG in Bipolar Derivations for Daily Clinical Practice: A Pilot Study. Brain Sci 2023; 13:brainsci13020167. [PMID: 36831710 PMCID: PMC9953767 DOI: 10.3390/brainsci13020167] [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: 12/29/2022] [Revised: 01/12/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
We used numerical methods to define the normative structure of resting-state EEG (rsEEG) in a pilot study of 37 healthy subjects (10-74 years old), using a double-banana bipolar montage. Artifact-free 120-200 s epoch lengths were visually identified and divided into 1 s windows with a 10% overlap. Differential channels were grouped by frontal, parieto-occipital, and temporal lobes. For every channel, the power spectrum was calculated and used to compute the area for delta (0-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) bands and was log-transformed. Furthermore, Shannon's spectral entropy (SSE) and coherence by bands were computed. Finally, we also calculated the main frequency and amplitude of the posterior dominant rhythm. According to the age-dependent distribution of the bands, we divided the patients in the following three groups: younger than 20; between 21 and 50; and older than 51 years old. The distribution of bands and coherence was different for the three groups depending on the brain lobes. We described the normative equations for the three age groups and for every brain lobe. We showed the feasibility of a normative structure of rsEEG picked up with a double-banana montage.
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15
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Wang J, Xu L, Ge Q, Xue L, Liu Y, Wang C, Wu Y, Liu Y, Chen L, Zhuang Y, Geng X, Chen X, Wang B, Yu Q, He J, Zhao X. EEG microstate changes during hyperbaric oxygen therapy in patients with chronic disorders of consciousness. Front Neurosci 2023; 17:1145065. [PMID: 37123360 PMCID: PMC10130513 DOI: 10.3389/fnins.2023.1145065] [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: 01/15/2023] [Accepted: 03/14/2023] [Indexed: 05/02/2023] Open
Abstract
Hyperbaric oxygen (HBO) therapy is an effective treatment for patients with disorders of consciousness (DOC). In this study, real-time electroencephalogram (EEG) recordings were obtained from patients with DOC during HBO therapy. EEG microstate indicators including mean microstate duration (MMD), ratio of total time covered (RTT), global explained variance (GEV), transition probability, mean occurrence, and mean global field power (GFP) were compared before and during HBO therapy. The results showed that the duration of microstate C in all patients with DOC increased after 20 min of HBO therapy (p < 0.05). Further statistical analysis found that the duration of microstate C was longer in the higher CRS-R group (≥8, 17 cases) than in the lower group (<8, 24 cases) during HBO treatment. In the higher CRS-R group, the transition probabilities from microstate A to microstate C and from microstate C to microstate A also increased significantly compared with the probability before treatment (p < 0.05). Microstate C is generally considered to be related to a salience network; an increase in the transition probability between microstate A and microstate C indicates increased information exchange between the auditory network and the salience network. The results of this study show that HBO therapy has a specific activating effect on attention and cognitive control in patients and causes increased activity in the primary sensory cortex (temporal lobe and occipital lobe). This study demonstrates that real-time EEG detection and analysis during HBO is a clinically feasible method for assessing brain function in patients with DOC. During HBO therapy, some EEG microstate indicators show significant changes related to the state of consciousness in patients with chronic DOC. This will be complementary to important electrophysiological indicators for assessing consciousness and may also provide an objective foundation for the precise treatment of patients with DOC.
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Affiliation(s)
- Jiameng Wang
- AHU-IAI AI Joint Laboratory, Anhui University, Hefei, China
- Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei, China
| | - Long Xu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Qianqian Ge
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Lianbi Xue
- Department of Hyperbaric Oxygenation, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yaling Liu
- Department of Hyperbaric Oxygenation, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Cong Wang
- Department of Hyperbaric Oxygenation, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yue Wu
- AHU-IAI AI Joint Laboratory, Anhui University, Hefei, China
- Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei, China
| | - Yun Liu
- Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei, China
| | - Lichao Chen
- Department of Hyperbaric Oxygenation, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Department of Hyperbaric Oxygen, Liaocheng People’s Hospital, Liaocheng, China
| | - Yutong Zhuang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiaoli Geng
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xueling Chen
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Bo Wang
- Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei, China
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qiuhong Yu
- Department of Hyperbaric Oxygenation, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- *Correspondence: Qiuhong Yu,
| | - Jianghong He
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Jianghong He,
| | - Xudong Zhao
- Hefei Comprehensive National Science Center, Institute of Artificial Intelligence, Hefei, China
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Xudong Zhao,
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16
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Event-related microstate dynamics represents working memory performance. Neuroimage 2022; 263:119669. [PMID: 36206941 DOI: 10.1016/j.neuroimage.2022.119669] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 09/30/2022] [Accepted: 10/03/2022] [Indexed: 11/21/2022] Open
Abstract
In recent years, EEG microstate analysis has attracted much attention as a tool for characterizing the spatial and temporal dynamics of large-scale electrophysiological activities in the human brain. Canonical 4 states (classes A, B, C, and D) have been widely reported, and they have been pointed out for their relationships with cognitive functions and several psychiatric disorders such as schizophrenia, in particular, through their static parameters such as average duration, occurrence, coverage, and transition probability. However, the relationships between event-related microstate changes and their related cognitive functions, as is often analyzed in event-related potentials under time-locked frameworks, is still not well understood. Furthermore, not enough attention has been paid to the relationship between microstate dynamics and static characteristics. To clarify the relationships between the static microstate parameters and dynamic microstate changes, and between the dynamics and working memory (WM) function, we first examined the temporal profiles of the microstates during the N-back task. We found significant event-related microstate dynamics that differed predominantly with WM loads, which were not clearly observed in the static parameters. Furthermore, in the 2-back condition, patterns of state transitions from class A to C in the high- and low-performance groups showed prominent differences at 50-300 ms after stimulus onset. We also confirmed that the transition patterns of the specific time periods were able to predict the performance level (low or high) in the 2-back condition at a significant level, where a specific transition between microstates, namely from class A to C with specific polarity, contributed to the prediction robustly. Taken together, our findings indicate that event-related microstate dynamics at 50-300 ms after onset may be essential for WM function. This suggests that event-related microstate dynamics can reflect more highly-refined brain functions.
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17
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Recognition of the Multi-class Schizophrenia Based on the Resting-State EEG Network Topology. Brain Topogr 2022; 35:495-506. [PMID: 35849250 DOI: 10.1007/s10548-022-00907-y] [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: 04/12/2021] [Accepted: 06/02/2022] [Indexed: 11/02/2022]
Abstract
The clinical therapy of schizophrenia (SCZ) replies on the corresponding accurate and reliable recognition. Although efforts have been paid, the diagnosis of SCZ is still roughly subjective, it is thus urgent to search for related objective physiological parameters. Motivated by the great potential of resting-state networks in underling the brain deficits among different SCZ groups, in this study, we then developed a multi-class feature extraction approach that could effectively extract the spatial network topology and facilitate the recognition of the SCZ, by combining a network structure based supervised learning with an ensemble co-decision strategy. The results demonstrated that the multi-class spatial pattern of the network (MSPN) features outperformed the other conventional electrophysiological features, such as relative power spectrums and network properties, and achieved the highest classification accuracy of 71.58% in the alpha band. These findings did validate that the resting-state MSPN is a promising tool for the clinical assessment of the SCZ.
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18
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Phogat R, Parmananda P, Prasad A. Intensity dependence of sub-harmonics in cortical response to photic stimulation. J Neural Eng 2022; 19. [PMID: 35839731 DOI: 10.1088/1741-2552/ac817f] [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: 03/25/2022] [Accepted: 07/15/2022] [Indexed: 11/11/2022]
Abstract
Objective. Periodic photic stimulation of human volunteers at 10 Hz is known to entrain their Electroencephalography (EEG) signals. This entrainment manifests as an increment in power at 10, 20, 30 Hz. We observed that this entrainment is accompanied by the emergence of sub-harmonics, but only at specific frequencies and higher intensities of the stimulating signal. Thereafter, we describe our results and explain them using the physiologically inspired Jansen and Rit Neural Mass Model (NMM).Approach. Four human volunteers were separately exposed to both high and low intensity 10 Hz and 6 Hz stimulation. A total of 4 experiments per subject were therefore performed. Simulations and bifurcation analysis of the NMM were carried out and compared with the experimental findings. <i> Main results. High intensity 10 Hz stimulation led to an increment in power at 5 Hz across all the 4 subjects. No increment of power was observed with low intensity stimulation. However, when the same protocol was repeated with a 6 Hz photic stimulation, neither high nor low intensity stimulation were found to cause a discernible change in power at 3 Hz. We found that the NMM was able to recapitulate these results. A further numerical analysis indicated that this arises from the underlying bifurcation structure of the NMM. <i> Significance. The excellent match between theory and experiment suggest that the bifurcation properties of the NMM are mirroring similar features possessed by the actual neural masses producing the EEG dynamics. Neural Mass Models could thus be valuable for understanding properties and pathologies of EEG dynamics, and may contribute to the engineering of brain-computer interface technologies.
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Affiliation(s)
- Richa Phogat
- Indian Institute of Technology Bombay, Department of Physics, Indian Institute of Technology - Bombay, Mumbai, 400076, INDIA
| | - P Parmananda
- Indian Institute of Technology Bombay, Department of Physics, Indian Institute of Technology - Bombay, Mumbai, Maharashtra, 400076, INDIA
| | - Ashok Prasad
- Colorado State University, Department of Chemical and Biological Engineering and School of Biomedical Engineering, Colorado State University, Fort Collins, Colorado, 80523-1019, UNITED STATES
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Li Y, Chen G, Lv J, Hou L, Dong Z, Wang R, Su M, Yu S. Abnormalities in resting-state EEG microstates are a vulnerability marker of migraine. J Headache Pain 2022; 23:45. [PMID: 35382739 PMCID: PMC8981824 DOI: 10.1186/s10194-022-01414-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 03/15/2022] [Indexed: 12/31/2022] Open
Abstract
Background Resting-state EEG microstates are thought to reflect brief activations of several interacting components of resting-state brain networks. Surprisingly, we still know little about the role of these microstates in migraine. In the present study, we attempted to address this issue by examining EEG microstates in patients with migraine without aura (MwoA) during the interictal period and comparing them with those of a group of healthy controls (HC). Methods Resting-state EEG was recorded in 61 MwoA patients (50 females) and 66 HC (50 females). Microstate parameters were compared between the two groups. We computed four widely identified canonical microstate classes A-D. Results Microstate classes B and D displayed higher time coverage and occurrence in the MwoA patient group than in the HC group, while microstate class C exhibited significantly lower time coverage and occurrence in the MwoA patient group. Meanwhile, the mean duration of microstate class C was significantly shorter in the MwoA patient group than in the HC group. Moreover, among the MwoA patient group, the duration of microstate class C correlated negatively with clinical measures of headache-related disability as assessed by the six-item Headache Impact Test (HIT-6). Finally, microstate syntax analysis showed significant differences in transition probabilities between the two groups, primarily involving microstate classes B, C, and D. Conclusions By exploring EEG microstate characteristics at baseline we were able to explore the neurobiological mechanisms underlying altered cortical excitability and aberrant sensory, affective, and cognitive processing, thus deepening our understanding of migraine pathophysiology.
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Asai T, Hamamoto T, Kashihara S, Imamizu H. Real-Time Detection and Feedback of Canonical Electroencephalogram Microstates: Validating a Neurofeedback System as a Function of Delay. Front Syst Neurosci 2022; 16:786200. [PMID: 35283737 PMCID: PMC8913511 DOI: 10.3389/fnsys.2022.786200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 02/04/2022] [Indexed: 11/13/2022] Open
Abstract
Recent neurotechnology has developed various methods for neurofeedback (NF), in which participants observe their own neural activity to be regulated in an ideal direction. EEG-microstates (EEGms) are spatially featured states that can be regulated through NF training, given that they have recently been indicated as biomarkers for some disorders. The current study was conducted to develop an EEG-NF system for detecting “canonical 4 EEGms” in real time. There are four representative EEG states, regardless of the number of channels, preprocessing procedures, or participants. Accordingly, our 10 Hz NF system was implemented to detect them (msA, B, C, and D) and audio-visually inform participants of its detection. To validate the real-time effect of this system on participants’ performance, the NF was intentionally delayed for participants to prevent their cognitive control in learning. Our results suggest that the feedback effect was observed only under the no-delay condition. The number of Hits increased significantly from the baseline period and increased from the 1- or 20-s delay conditions. In addition, when the Hits were compared among the msABCD, each cognitive or perceptual function could be characterized, though the correspondence between each microstate and psychological ability might not be that simple. For example, msD should be generally task-positive and less affected by the inserted delay, whereas msC is more delay-sensitive. In this study, we developed and validated a new EEGms-NF system as a function of delay. Although the participants were naive to the inserted delay, the real-time NF successfully increased their Hit performance, even within a single-day experiment, although target specificity remains unclear. Future research should examine long-term training effects using this NF system.
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Affiliation(s)
- Tomohisa Asai
- Cognitive Mechanisms Laboratories, Advanced Telecommunications Research Institute International (ATR), Kyoto, Japan
- *Correspondence: Tomohisa Asai,
| | - Takamasa Hamamoto
- Cognitive Mechanisms Laboratories, Advanced Telecommunications Research Institute International (ATR), Kyoto, Japan
- Graduate School of Frontier Biosciences, Osaka University, Osaka, Japan
| | - Shiho Kashihara
- Cognitive Mechanisms Laboratories, Advanced Telecommunications Research Institute International (ATR), Kyoto, Japan
| | - Hiroshi Imamizu
- Cognitive Mechanisms Laboratories, Advanced Telecommunications Research Institute International (ATR), Kyoto, Japan
- Department of Psychology, Graduate School of Humanities and Sociology, The University of Tokyo, Tokyo, Japan
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21
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Temporal and Spatial Dynamics of EEG Features in Female College Students with Subclinical Depression. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031778. [PMID: 35162800 PMCID: PMC8835158 DOI: 10.3390/ijerph19031778] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 01/18/2022] [Accepted: 02/02/2022] [Indexed: 12/27/2022]
Abstract
Synchronization of the dynamic processes in structural networks connect the brain across a wide range of temporal and spatial scales, creating a dynamic and complex functional network. Microstate and omega complexity are two reference-free electroencephalography (EEG) measures that can represent the temporal and spatial complexities of EEG data. Few studies have focused on potential brain spatiotemporal dynamics in the early stages of depression to use as an early screening feature for depression. Thus, this study aimed to explore large-scale brain network dynamics of individuals both with and without subclinical depression, from the perspective of temporal and spatial dimensions and to input them as features into a machine learning framework for the automatic diagnosis of early-stage depression. To achieve this, spatio–temporal dynamics of rest-state EEG signals in female college students (n = 40) with and without (n = 38) subclinical depression were analyzed using EEG microstate and omega complexity analysis. Then, based on differential features of EEGs between the two groups, a support vector machine was utilized to compare performances of spatio–temporal features and single features in the classification of early depression. Microstate results showed that the occurrence rate of microstate class B was significantly higher in the group with subclinical depression when compared with the group without. Moreover, the duration and contribution of microstate class C in the subclinical group were both significantly lower than in the group without subclinical depression. Omega complexity results showed that the global omega complexity of β-2 and γ band was significantly lower for the subclinical depression group compared with the other group (p < 0.05). In addition, the anterior and posterior regional omega complexities were lower for the subclinical depression group compared to the comparison group in α-1, β-2 and γ bands. It was found that AUC of 81% for the differential indicators of EEG microstates and omega complexity was deemed better than a single index for predicting subclinical depression. Thus, since temporal and spatial complexity of EEG signals were manifestly altered in female college students with subclinical depression, it is possible that this characteristic could be adopted as an early auxiliary diagnostic indicator of depression.
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22
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Nash K, Kleinert T, Leota J, Scott A, Schimel J. Resting-state networks of believers and non-believers: An EEG microstate study. Biol Psychol 2022; 169:108283. [PMID: 35114302 DOI: 10.1016/j.biopsycho.2022.108283] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 01/25/2022] [Accepted: 01/26/2022] [Indexed: 11/02/2022]
Abstract
Atheism and agnosticism are becoming increasingly popular, yet the neural processes underpinning individual differences in religious belief and non-belief remain poorly understood. In the current study, we examined differences between Believers and Non-Believers with regard to fundamental neural resting networks using EEG microstate analysis. Results demonstrated that Non-Believers show increased contribution from a resting-state network associated with deliberative or analytic processing (Microstate D), and Believers show increased contribution from a network associated with intuitive or automatic processing (Microstate C). Further, analysis of resting-state network communication suggested that Non-Believers may process visual information in a more deliberative or top-down manner, and Believers may process visual information in a more intuitive or bottom-up manner. These results support dual process explanations of individual differences in religious belief and add to the representation of non-belief as more than merely a lack of belief.
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Affiliation(s)
- Kyle Nash
- Department of Psychology, University of Alberta, Edmonton AB T6G 2R3, Canada.
| | - Tobias Kleinert
- Department of Psychology, University of Alberta, Edmonton AB T6G 2R3, Canada
| | - Josh Leota
- Department of Psychology, University of Alberta, Edmonton AB T6G 2R3, Canada
| | - Andy Scott
- Department of Psychology, University of Alberta, Edmonton AB T6G 2R3, Canada
| | - Jeff Schimel
- Department of Psychology, University of Alberta, Edmonton AB T6G 2R3, Canada
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23
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Kenny A, Wright D, Stanfield AC. EEG as a translational biomarker and outcome measure in fragile X syndrome. Transl Psychiatry 2022; 12:34. [PMID: 35075104 PMCID: PMC8786970 DOI: 10.1038/s41398-022-01796-2] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 12/01/2021] [Accepted: 01/12/2022] [Indexed: 01/08/2023] Open
Abstract
Targeted treatments for fragile X syndrome (FXS) have frequently failed to show efficacy in clinical testing, despite success at the preclinical stages. This has highlighted the need for more effective translational outcome measures. EEG differences observed in FXS, including exaggerated N1 ERP amplitudes, increased resting gamma power and reduced gamma phase-locking in the sensory cortices, have been suggested as potential biomarkers of the syndrome. These abnormalities are thought to reflect cortical hyper excitability resulting from an excitatory (glutamate) and inhibitory (GABAergic) imbalance in FXS, which has been the target of several pharmaceutical remediation studies. EEG differences observed in humans also show similarities to those seen in laboratory models of FXS, which may allow for greater translational equivalence and better predict clinical success of putative therapeutics. There is some evidence from clinical trials showing that treatment related changes in EEG may be associated with clinical improvements, but these require replication and extension to other medications. Although the use of EEG characteristics as biomarkers is still in the early phases, and further research is needed to establish its utility in clinical trials, the current research is promising and signals the emergence of an effective translational biomarker.
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Affiliation(s)
- Aisling Kenny
- Patrick Wild Centre, Division of Psychiatry, Kennedy Tower, Royal Edinburgh Hospital, University of Edinburgh, EH10 5HF, Edinburgh, UK.
| | - Damien Wright
- grid.4305.20000 0004 1936 7988Patrick Wild Centre, Division of Psychiatry, Kennedy Tower, Royal Edinburgh Hospital, University of Edinburgh, EH10 5HF Edinburgh, UK
| | - Andrew C. Stanfield
- grid.4305.20000 0004 1936 7988Patrick Wild Centre, Division of Psychiatry, Kennedy Tower, Royal Edinburgh Hospital, University of Edinburgh, EH10 5HF Edinburgh, UK
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24
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Lee HS, Kim JS. Implication of Electrophysiological Biomarkers in Psychosis: Focusing on Diagnosis and Treatment Response. J Pers Med 2022; 12:jpm12010031. [PMID: 35055346 PMCID: PMC8779239 DOI: 10.3390/jpm12010031] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 12/19/2021] [Accepted: 12/28/2021] [Indexed: 11/16/2022] Open
Abstract
Precision medicine has been considered a promising approach to diagnosis, treatment, and various interventions, considering the individual clinical and biological characteristics. Recent advances in biomarker development hold promise for guiding a new era of precision medicine style trials for psychiatric illnesses, including psychosis. Electroencephalography (EEG) can directly measure the full spatiotemporal dynamics of neural activation associated with a wide variety of cognitive processes. This manuscript reviews three aspects: prediction of diagnosis, prognostic aspects of disease progression and outcome, and prediction of treatment response that might be helpful in understanding the current status of electrophysiological biomarkers in precision medicine for patients with psychosis. Although previous EEG analysis could not be a powerful method for the diagnosis of psychiatric illness, recent methodological advances have shown the possibility of classifying and detecting mental illness. Some event-related potentials, such as mismatch negativity, have been associated with neurocognition, functioning, and illness progression in schizophrenia. Resting state studies, sophisticated ERP measures, and machine-learning approaches could make technical progress and provide important knowledge regarding neurophysiology, disease progression, and treatment response in patients with schizophrenia. Identifying potential biomarkers for the diagnosis and treatment response in schizophrenia is the first step towards precision medicine.
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Affiliation(s)
- Ho Sung Lee
- Department of Pulmonology and Allergy, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Korea;
| | - Ji Sun Kim
- Department of Psychiatry, Soonchunhyang University Cheonan Hospital, Cheonan 31151, Korea
- Correspondence: ; Tel.: +82-41-570-2983; Fax: +82-41-592-3804
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25
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Das S, Zomorrodi R, Enticott PG, Kirkovski M, Blumberger DM, Rajji TK, Desarkar P. Resting state electroencephalography microstates in autism spectrum disorder: A mini-review. Front Psychiatry 2022; 13:988939. [PMID: 36532178 PMCID: PMC9752812 DOI: 10.3389/fpsyt.2022.988939] [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: 07/07/2022] [Accepted: 11/09/2022] [Indexed: 12/04/2022] Open
Abstract
Atypical spatial organization and temporal characteristics, found via resting state electroencephalography (EEG) microstate analysis, have been associated with psychiatric disorders but these temporal and spatial parameters are less known in autism spectrum disorder (ASD). EEG microstates reflect a short time period of stable scalp potential topography. These canonical microstates (i.e., A, B, C, and D) and more are identified by their unique topographic map, mean duration, fraction of time covered, frequency of occurrence and global explained variance percentage; a measure of how well topographical maps represent EEG data. We reviewed the current literature for resting state microstate analysis in ASD and identified eight publications. This current review indicates there is significant alterations in microstate parameters in ASD populations as compared to typically developing (TD) populations. Microstate parameters were also found to change in relation to specific cognitive processes. However, as microstate parameters are found to be changed by cognitive states, the differently acquired data (e.g., eyes closed or open) resting state EEG are likely to produce disparate results. We also review the current understanding of EEG sources of microstates and the underlying brain networks.
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Affiliation(s)
- Sushmit Das
- Centre for Addiction and Mental Health, Toronto, ON, Canada.,Azrieli Adult Neurodevelopmental Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Reza Zomorrodi
- Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Peter G Enticott
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, Australia
| | - Melissa Kirkovski
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC, Australia.,Institute for Health and Sport, Victoria University, Melbourne, VIC, Australia
| | - Daniel M Blumberger
- Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Tarek K Rajji
- Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Pushpal Desarkar
- Centre for Addiction and Mental Health, Toronto, ON, Canada.,Azrieli Adult Neurodevelopmental Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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26
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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.
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27
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Lin Q, Li D, Hu C, Shen Z, Wang Y. Altered EEG Microstates Dynamics During Cue-Induced Methamphetamine Craving in Virtual Reality Environments. Front Psychiatry 2022; 13:891719. [PMID: 35599773 PMCID: PMC9114476 DOI: 10.3389/fpsyt.2022.891719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 04/06/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Cue-induced craving is widely considered to be the most important risk factor for relapse during abstinence from methamphetamine (Meth). There is limited research regarding electroencephalography (EEG) microstates of Meth-dependent patients under exposure to drug-related cues. Our objective was to investigate whether EEG microstate temporal characteristics could capture neural correlates of cue-induced Meth craving in virtual reality (VR) environments. METHODS EEG recordings of 35 Meth-dependent patients and 30 healthy controls (HCs) were collected during eyes-open state and cue-induced state, respectively. Group differences and condition differences in temporal parameters of four microstate classes were compared. RESULTS The results demonstrated the greater presence of microstate B in both Meth-dependent patients and HCs during the cue-induced condition, compared to resting state. In addition, for Meth-dependent patients, microstate C occurred significantly less frequently, along with a tendency of increased occurrence for class D during the cue-induced condition, compared to resting state. However, the change direction of class C and class D in HCs was completely opposite to that of Meth-dependent patients. The cue-induced condition also elicited different changes in transition probability between Meth-dependent patients and HCs. CONCLUSION This study explored the features of EEG microstates in Meth-dependent patients during the cue-induced condition, which can improve our understanding of Meth addiction and contribute to the development of effective assessments and intervention tools.
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Affiliation(s)
- Qianqian Lin
- Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Dongxu Li
- Anhui Psychiatric Medical Center, Anhui Medical University, Hefei, China
| | - Cheng Hu
- Shiliping Compulsory Rehabilitation Center, Zhejiang, China
| | - Zhihua Shen
- Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yongguang Wang
- Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, China.,Anhui Psychiatric Medical Center, Anhui Medical University, Hefei, China.,Zhejiang Provincial Institute of Drug Abuse Research, Hangzhou, China
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28
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Liu Y, Pu C, Xia S, Deng D, Wang X, Li M. Machine learning approaches for diagnosing depression using EEG: A review. Transl Neurosci 2022; 13:224-235. [PMID: 36045698 PMCID: PMC9375981 DOI: 10.1515/tnsci-2022-0234] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/18/2022] [Accepted: 07/04/2022] [Indexed: 12/02/2022] Open
Abstract
Depression has become one of the most crucial public health issues, threatening the quality of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of depression is now still hampered by behavioral diagnostic methods. Due to the lack of objective laboratory diagnostic criteria, accurate identification and diagnosis of depression remained elusive. With the rise of computational psychiatry, a growing number of studies have combined resting-state electroencephalography with machine learning (ML) to alleviate diagnosis of depression in recent years. Despite the exciting results, these were worrisome of these studies. As a result, ML prediction models should be continuously improved to better screen and diagnose depression. Finally, this technique would be used for the diagnosis of other psychiatric disorders in the future.
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Affiliation(s)
- Yuan Liu
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang 330006, Jiangxi Province, China
| | - Changqin Pu
- Queen Mary College, Nanchang University, Nanchang 330031, Jiangxi Province, China
| | - Shan Xia
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang 330006, Jiangxi Province, China
| | - Dingyu Deng
- Department of Internal Neurology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi Province, China
| | - Xing Wang
- School of Life Sciences, Nanchang University, No.999 Xuefu Avenue, Honggutan District, Nanchang 330036, Jiangxi Province, China.,Clinical Diagnostics Laboratory, Clinical Medical Experiment Center, Nanchang University, Nanchang 330036, China
| | - Mengqian Li
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang 330006, Jiangxi Province, China
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29
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Sun Q, Zhao L, Tan L. Abnormalities of Electroencephalography Microstates in Drug-Naïve, First-Episode Schizophrenia. Front Psychiatry 2022; 13:853602. [PMID: 35360139 PMCID: PMC8964053 DOI: 10.3389/fpsyt.2022.853602] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 02/22/2022] [Indexed: 01/12/2023] Open
Abstract
OBJECTIVE Microstate analysis is a powerful tool to probe the brain functions, and changes in microstates under electroencephalography (EEG) have been repeatedly reported in patients with schizophrenia. This study aimed to investigate the dynamics of EEG microstates in drug-naïve, first-episode schizophrenia (FE-SCH) and to test the relationship between EEG microstates and clinical symptoms. METHODS Resting-state EEG were recorded for 23 patients with FE-SCH and 23 healthy controls using a 64-channel cap. Three parameters, i.e., contribution, duration, and occurrence, of the four microstate classes were calculated. Group differences in EEG microstates and their clinical symptoms [assessed using the Positive and Negative Syndrome Scale (PANSS)] were analyzed. RESULTS Compared with healthy controls, patients with FE-SCH showed increased duration, occurrence and contribution of microstate class C and decreased contribution and occurrence of microstate class D. In addition, the score of positive symptoms in PANSS was negatively correlated with the occurrence of microstate D. CONCLUSION Our findings showed abnormal patterns of EEG microstates in drug-naïve, first-episode schizophrenia, which might help distinguish individuals with schizophrenia in the early stage and develop early intervention strategies.
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Affiliation(s)
- Qiaoling Sun
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, China National Technology Institute on Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Linlin Zhao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, China National Technology Institute on Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Liwen Tan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, China National Technology Institute on Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
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30
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Westhoff MLS, Ladwig J, Heck J, Schülke R, Groh A, Deest M, Bleich S, Frieling H, Jahn K. Early Detection and Prevention of Schizophrenic Psychosis-A Review. Brain Sci 2021; 12:11. [PMID: 35053755 PMCID: PMC8774083 DOI: 10.3390/brainsci12010011] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 12/16/2021] [Accepted: 12/20/2021] [Indexed: 01/04/2023] Open
Abstract
Psychotic disorders often run a chronic course and are associated with a considerable emotional and social impact for patients and their relatives. Therefore, early recognition, combined with the possibility of preventive intervention, is urgently warranted since the duration of untreated psychosis (DUP) significantly determines the further course of the disease. In addition to established diagnostic tools, neurobiological factors in the development of schizophrenic psychoses are increasingly being investigated. It is shown that numerous molecular alterations already exist before the clinical onset of the disease. As schizophrenic psychoses are not elicited by a single mutation in the deoxyribonucleic acid (DNA) sequence, epigenetics likely constitute the missing link between environmental influences and disease development and could potentially serve as a biomarker. The results from transcriptomic and proteomic studies point to a dysregulated immune system, likely evoked by epigenetic alterations. Despite the increasing knowledge of the neurobiological mechanisms involved in the development of psychotic disorders, further research efforts with large population-based study designs are needed to identify suitable biomarkers. In conclusion, a combination of blood examinations, functional imaging techniques, electroencephalography (EEG) investigations and polygenic risk scores should be considered as the basis for predicting how subjects will transition into manifest psychosis.
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Affiliation(s)
- Martin Lennart Schulze Westhoff
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, D-30625 Hannover, Germany; (J.L.); (R.S.); (A.G.); (M.D.); (S.B.); (H.F.); (K.J.)
| | - Johannes Ladwig
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, D-30625 Hannover, Germany; (J.L.); (R.S.); (A.G.); (M.D.); (S.B.); (H.F.); (K.J.)
| | - Johannes Heck
- Institute for Clinical Pharmacology, Hannover Medical School, D-30625 Hannover, Germany;
| | - Rasmus Schülke
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, D-30625 Hannover, Germany; (J.L.); (R.S.); (A.G.); (M.D.); (S.B.); (H.F.); (K.J.)
| | - Adrian Groh
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, D-30625 Hannover, Germany; (J.L.); (R.S.); (A.G.); (M.D.); (S.B.); (H.F.); (K.J.)
| | - Maximilian Deest
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, D-30625 Hannover, Germany; (J.L.); (R.S.); (A.G.); (M.D.); (S.B.); (H.F.); (K.J.)
| | - Stefan Bleich
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, D-30625 Hannover, Germany; (J.L.); (R.S.); (A.G.); (M.D.); (S.B.); (H.F.); (K.J.)
| | - Helge Frieling
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, D-30625 Hannover, Germany; (J.L.); (R.S.); (A.G.); (M.D.); (S.B.); (H.F.); (K.J.)
| | - Kirsten Jahn
- Department of Psychiatry, Social Psychiatry and Psychotherapy, Hannover Medical School, D-30625 Hannover, Germany; (J.L.); (R.S.); (A.G.); (M.D.); (S.B.); (H.F.); (K.J.)
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EEG microstate in obstructive sleep apnea patients. Sci Rep 2021; 11:17178. [PMID: 34433839 PMCID: PMC8387348 DOI: 10.1038/s41598-021-95749-2] [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: 12/03/2020] [Accepted: 07/28/2021] [Indexed: 02/07/2023] Open
Abstract
Obstructive sleep apnea (OSA) is a common sleep respiratory disease. Previous studies have found that the wakefulness electroencephalogram (EEG) of OSA patients has changed, such as increased EEG power. However, whether the microstates reflecting the transient state of the brain is abnormal is unclear during obstructive hypopnea (OH). We investigated the microstates of sleep EEG in 100 OSA patients. Then correlation analysis was carried out between microstate parameters and EEG markers of sleep disturbance, such as power spectrum, sample entropy and detrended fluctuation analysis (DFA). OSA_OH patients showed that the microstate C increased presence and the microstate D decreased presence compared to OSA_withoutOH patients and controls. The fifth microstate E appeared during N1-OH, but the probability of other microstates transferring to microstate E was small. According to the correlation analysis, OSA_OH patients in N1-OH showed that the microstate D was positively correlated with delta power, and negatively correlated with beta and alpha power; the transition probability of the microstate B → C and E → C was positively correlated with alpha power. In other sleep stages, the microstate parameters were not correlated with power, sample entropy and FDA. We might interpret that the abnormal transition of brain active areas of OSA patients in N1-OH stage leads to abnormal microstates, which might be related to the change of alpha activity in the cortex.
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Kim K, Duc NT, Choi M, Lee B. EEG microstate features for schizophrenia classification. PLoS One 2021; 16:e0251842. [PMID: 33989352 PMCID: PMC8121321 DOI: 10.1371/journal.pone.0251842] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 05/04/2021] [Indexed: 12/11/2022] Open
Abstract
Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes in brain state in neuropsychiatric diseases. However, previous studies have only reported differences in each microstate feature and have not determined whether microstate features are suitable for schizophrenia classification. Therefore, it is necessary to validate microstate features for schizophrenia classification. Nineteen microstate features, including duration, occurrence, and coverage as well as thirty-one conventional EEG features, including statistical, frequency, and temporal characteristics were obtained from resting-state EEG recordings of 14 patients diagnosed with schizophrenia and from 14 healthy (control) subjects. Machine-learning based multivariate analysis was used to evaluate classification performance. EEG recordings of patients and controls showed different microstate features. More importantly, when differentiating among patients and controls, EEG microstate features outperformed conventional EEG ones. The performance of the microstate features exceeded that of conventional EEG, even after optimization using recursive feature elimination. EEG microstate features applied with conventional EEG features also showed better classification performance than conventional EEG features alone. The current study is the first to validate the use of microstate features to discriminate schizophrenia, suggesting that EEG microstate features are useful for schizophrenia classification.
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Affiliation(s)
- Kyungwon Kim
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Cheomdan-gwagiro, Gwangju, South Korea
- Department of Psychiatry and Biomedical Research Institute, Pusan National University Hospital, Busan, South Korea
| | - Nguyen Thanh Duc
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Cheomdan-gwagiro, Gwangju, South Korea
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada
- McConnel Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Montreal, Canada
| | - Min Choi
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Cheomdan-gwagiro, Gwangju, South Korea
| | - Boreom Lee
- Department of Biomedical Science and Engineering (BMSE), Institute Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Cheomdan-gwagiro, Gwangju, South Korea
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Resting-State EEG Microstates Parallel Age-Related Differences in Allocentric Spatial Working Memory Performance. Brain Topogr 2021; 34:442-460. [PMID: 33871737 PMCID: PMC8195770 DOI: 10.1007/s10548-021-00835-3] [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: 10/10/2020] [Accepted: 03/30/2021] [Indexed: 11/08/2022]
Abstract
Alterations of resting-state EEG microstates have been associated with various neurological disorders and behavioral states. Interestingly, age-related differences in EEG microstate organization have also been reported, and it has been suggested that resting-state EEG activity may predict cognitive capacities in healthy individuals across the lifespan. In this exploratory study, we performed a microstate analysis of resting-state brain activity and tested allocentric spatial working memory performance in healthy adult individuals: twenty 25–30-year-olds and twenty-five 64–75-year-olds. We found a lower spatial working memory performance in older adults, as well as age-related differences in the five EEG microstate maps A, B, C, C′ and D, but especially in microstate maps C and C′. These two maps have been linked to neuronal activity in the frontal and parietal brain regions which are associated with working memory and attention, cognitive functions that have been shown to be sensitive to aging. Older adults exhibited lower global explained variance and occurrence of maps C and C′. Moreover, although there was a higher probability to transition from any map towards maps C, C′ and D in young and older adults, this probability was lower in older adults. Finally, although age-related differences in resting-state EEG microstates paralleled differences in allocentric spatial working memory performance, we found no evidence that any individual or combination of resting-state EEG microstate parameter(s) could reliably predict individual spatial working memory performance. Whether the temporal dynamics of EEG microstates may be used to assess healthy cognitive aging from resting-state brain activity requires further investigation.
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Perrottelli A, Giordano GM, Brando F, Giuliani L, Mucci A. EEG-Based Measures in At-Risk Mental State and Early Stages of Schizophrenia: A Systematic Review. Front Psychiatry 2021; 12:653642. [PMID: 34017273 PMCID: PMC8129021 DOI: 10.3389/fpsyt.2021.653642] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 04/06/2021] [Indexed: 12/17/2022] Open
Abstract
Introduction: Electrophysiological (EEG) abnormalities in subjects with schizophrenia have been largely reported. In the last decades, research has shifted to the identification of electrophysiological alterations in the prodromal and early phases of the disorder, focusing on the prediction of clinical and functional outcome. The identification of neuronal aberrations in subjects with a first episode of psychosis (FEP) and in those at ultra high-risk (UHR) or clinical high-risk (CHR) to develop a psychosis is crucial to implement adequate interventions, reduce the rate of transition to psychosis, as well as the risk of irreversible functioning impairment. The aim of the review is to provide an up-to-date synthesis of the electrophysiological findings in the at-risk mental state and early stages of schizophrenia. Methods: A systematic review of English articles using Pubmed, Scopus, and PsychINFO was undertaken in July 2020. Additional studies were identified by hand-search. Electrophysiological studies that included at least one group of FEP or subjects at risk to develop psychosis, compared to healthy controls (HCs), were considered. The heterogeneity of the studies prevented a quantitative synthesis. Results: Out of 319 records screened, 133 studies were included in a final qualitative synthesis. Included studies were mainly carried out using frequency analysis, microstates and event-related potentials. The most common findings included an increase in delta and gamma power, an impairment in sensory gating assessed through P50 and N100 and a reduction of Mismatch Negativity and P300 amplitude in at-risk mental state and early stages of schizophrenia. Progressive changes in some of these electrophysiological measures were associated with transition to psychosis and disease course. Heterogeneous data have been reported for indices evaluating synchrony, connectivity, and evoked-responses in different frequency bands. Conclusions: Multiple EEG-indices were altered during at-risk mental state and early stages of schizophrenia, supporting the hypothesis that cerebral network dysfunctions appear already before the onset of the disorder. Some of these alterations demonstrated association with transition to psychosis or poor functional outcome. However, heterogeneity in subjects' inclusion criteria, clinical measures and electrophysiological methods prevents drawing solid conclusions. Large prospective studies are needed to consolidate findings concerning electrophysiological markers of clinical and functional outcome.
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Affiliation(s)
- Andrea Perrottelli
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | | | - Francesco Brando
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Luigi Giuliani
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Armida Mucci
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
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35
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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.
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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
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36
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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.
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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
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37
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Sun Q, Zhou J, Guo H, Gou N, Lin R, Huang Y, Guo W, Wang X. EEG Microstates and Its Relationship With Clinical Symptoms in Patients With Schizophrenia. Front Psychiatry 2021; 12:761203. [PMID: 34777062 PMCID: PMC8581189 DOI: 10.3389/fpsyt.2021.761203] [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: 08/19/2021] [Accepted: 10/08/2021] [Indexed: 12/15/2022] Open
Abstract
Schizophrenia is a complex and devastating disorder with unclear pathogenesis. Electroencephalogram (EEG) microstates have been suggested as a potential endophenotype for this disorder. However, no clear dynamic pattern of microstates has been found. This study aims to identify the dynamics of EEG microstates in schizophrenia and to test whether schizophrenia patients with altered clinical symptoms severity showed different microstates abnormalities compared with healthy controls. Resting-state EEG data in 46 individuals who met the ICD-10 diagnostic criteria for schizophrenia and 39 healthy controls was recorded. The patients with schizophrenia were divided into subgroups based on the level of their negative or positive symptoms assessed using the Positive and Negative Syndrome Scale. Microstate parameters (contribution, occurrence, and duration) of four prototypical microstate classes (A-D) were investigated. Compared with healthy controls, individuals with schizophrenia showed increased duration and contribution of microstate class C, decreased contribution and occurrence of microstate class B. Different microstate patterns were found between subgroups and healthy controls. Results in this study support the consistent observation of abnormal EEG microstates patterns in patients with schizophrenia and highlight the necessity to divide subjects into subgroups according to their clinical symptoms.
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Affiliation(s)
- Qiaoling Sun
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Jiansong Zhou
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Huijuan Guo
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Ningzhi Gou
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Ruoheng Lin
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Ying Huang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Weilong Guo
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xiaoping Wang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
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38
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Mackintosh AJ, Borgwardt S, Studerus E, Riecher-Rössler A, de Bock R, Andreou C. EEG Microstate Differences in Medicated vs. Medication-Naïve First-Episode Psychosis Patients. Front Psychiatry 2020; 11:600606. [PMID: 33329154 PMCID: PMC7732503 DOI: 10.3389/fpsyt.2020.600606] [Citation(s) in RCA: 12] [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: 08/30/2020] [Accepted: 10/30/2020] [Indexed: 12/21/2022] Open
Abstract
There has been considerable interest in the role of synchronous brain activity abnormalities in the pathophysiology of psychotic disorders and their relevance for treatment; one index of such activity are EEG resting-state microstates. These reflect electric field configurations of the brain that persist over 60-120 ms time periods. A set of quasi-stable microstates classes A, B, C, and D have been repeatedly identified across healthy participants. Changes in microstate parameters coverage, duration and occurrence have been found in medication-naïve as well as medicated patients with psychotic disorders compared to healthy controls. However, to date, only two studies have directly compared antipsychotic medication effects on EEG microstates either pre- vs. post-treatment or between medicated and unmedicated chronic schizophrenia patients. The aim of this study was therefore to directly compare EEG resting-state microstates between medicated and medication-naïve (untreated) first-episode (FEP) psychosis patients (mFEP vs. uFEP). We used 19-channel clinical EEG recordings to compare temporal parameters of four prototypical microstate classes (A-D) within an overall sample of 47 patients (mFEP n = 17; uFEP n = 30). The results demonstrated significant decreases of microstate class A and significant increases of microstate class B in mFEP compared to uFEP. No significant differences between groups were found for microstate classes C and D. Further studies are needed to replicate these results in longitudinal designs that assess antipsychotic medication effects on neural networks at the onset of the disorder and over time during illness progression. As treatment response and compliance in FEP patients are relatively low, such studies could contribute to better understand treatment outcomes and ultimately improve treatment strategies.
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Affiliation(s)
- Amatya J Mackintosh
- Division of Clinical Psychology and Epidemiology, Department of Psychology, University of Basel, Basel, Switzerland
| | - Stefan Borgwardt
- University Psychiatric Clinics (UPK) Basel, University of Basel, Basel, Switzerland.,Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Erich Studerus
- Division of Personality and Developmental Psychology, Department of Psychology, University of Basel, Basel, Switzerland
| | | | - Renate de Bock
- Division of Clinical Psychology and Epidemiology, Department of Psychology, University of Basel, Basel, Switzerland.,University Psychiatric Clinics (UPK) Basel, University of Basel, Basel, Switzerland
| | - Christina Andreou
- Division of Clinical Psychology and Epidemiology, Department of Psychology, University of Basel, Basel, Switzerland.,Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
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Ren Y, Li W, Liu S, Li Z, Wang J, Yang H, Xu Y. A Weighted Gene Co-expression Network Analysis Reveals lncRNA Abnormalities in the Peripheral Blood Associated With Ultra-High-Risk for Psychosis. Front Psychiatry 2020; 11:580307. [PMID: 33384626 PMCID: PMC7769947 DOI: 10.3389/fpsyt.2020.580307] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Accepted: 11/16/2020] [Indexed: 12/11/2022] Open
Abstract
Objective: The primary study aim was to identify long non-coding RNA (lncRNA) abnormalities associated with ultra-high-risk (UHR) for psychosis based on a weighted gene co-expression network analysis. Methods: UHR patients were screened by the structured interview for prodromal syndromes (SIPS). We performed a WGCNA analysis on lncRNA and mRNA microarray profiles generated from the peripheral blood samples in 14 treatment-seeking patients with UHR who never received psychiatric medication and 18 demographically matched typically developing controls. Gene Ontology (GO) analysis and canonical correlation analysis were then applied to reveal functions and correlation between lncRNAs and mRNAs. Results: The lncRNAs were organized into co-expressed modules by WGCNA, two modules of which were strongly associated with UHR. The mRNA networks were constructed and two disease-associated mRNA modules were identified. A functional enrichment analysis showed that mRNAs were highly enriched for immune regulation and inflammation. Moreover, a significant correlation between lncRNAs and mRNAs were verified by a canonical correlation analysis. Conclusion: We identified novel lncRNA modules related to UHR. These results contribute to our understanding of the molecular basis of UHR from the perspective of systems biology and provide a theoretical basis for early intervention in the assumed development of schizophrenia.
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Affiliation(s)
- Yan Ren
- Department of Psychiatry, Shanxi Bethune Hospital, Taiyuan, China.,Shanxi Academy of Medical Science, Taiyuan, China
| | - Wei Li
- Department of Psychiatry, Shanxi Bethune Hospital, Taiyuan, China.,Shanxi Academy of Medical Science, Taiyuan, China
| | - Sha Liu
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China.,Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
| | - Zhi Li
- Department of Hematology, Taiyuan Central Hospital of Shanxi Medical University, Taiyuan, China
| | - Jiaying Wang
- Department of Oncology, The Second Hospital of Shanxi Medical University, Taiyuan, China
| | - Hong Yang
- Department of Psychiatry, Shanxi Bethune Hospital, Taiyuan, China.,Shanxi Academy of Medical Science, Taiyuan, China
| | - Yong Xu
- Shanxi Key Laboratory of Artificial Intelligence Assisted Diagnosis and Treatment for Mental Disorder, First Hospital of Shanxi Medical University, Taiyuan, China.,Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China
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