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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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Li H, Wang C, Ma L, Xu C, Li H. EEG analysis in patients with schizophrenia based on microstate semantic modeling method. Front Hum Neurosci 2024; 18:1372985. [PMID: 38638803 PMCID: PMC11024310 DOI: 10.3389/fnhum.2024.1372985] [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: 01/19/2024] [Accepted: 03/19/2024] [Indexed: 04/20/2024] Open
Abstract
Introduction Microstate analysis enables the characterization of quasi-stable scalp potential fields on a sub-second timescale, preserving the temporal dynamics of EEG and spatial information of scalp potential distributions. Owing to its capacity to provide comprehensive pathological insights, it has been widely applied in the investigation of schizophrenia (SCZ). Nevertheless, previous research has primarily concentrated on differences in individual microstate temporal characteristics, neglecting potential distinctions in microstate semantic sequences and not fully considering the issue of the universality of microstate templates between SCZ patients and healthy individuals. Methods This study introduced a microstate semantic modeling analysis method aimed at schizophrenia recognition. Firstly, microstate templates corresponding to both SCZ patients and healthy individuals were extracted from resting-state EEG data. The introduction of a dual-template strategy makes a difference in the quality of microstate sequences. Quality features of microstate sequences were then extracted from four dimensions: Correlation, Explanation, Residual, and Dispersion. Subsequently, the concept of microstate semantic features was proposed, decomposing the microstate sequence into continuous sub-sequences. Specific semantic sub-sequences were identified by comparing the time parameters of sub-sequences. Results The SCZ recognition test was performed on the public dataset for both the quality features and semantic features of microstate sequences, yielding an impressive accuracy of 97.2%. Furthermore, cross-subject experimental validation was conducted, demonstrating that the method proposed in this paper achieves a recognition rate of 96.4% between different subjects. Discussion This research offers valuable insights for the clinical diagnosis of schizophrenia. In the future, further studies will seek to augment the sample size to enhance the effectiveness and reliability of this method.
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Affiliation(s)
- Hongwei Li
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Changming Wang
- Department of Neurosurgery, XuanWu Hospital, Capital Medical University, Beijing, China
| | - Lin Ma
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Cong Xu
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
| | - Haifeng Li
- Faculty of Computing, Harbin Institute of Technology, Harbin, China
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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: 2] [Impact Index Per Article: 2.0] [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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Zhu M, Gong Q. EEG spectral and microstate analysis originating residual inhibition of tinnitus induced by tailor-made notched music training. Front Neurosci 2023; 17:1254423. [PMID: 38148944 PMCID: PMC10750374 DOI: 10.3389/fnins.2023.1254423] [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: 07/07/2023] [Accepted: 11/21/2023] [Indexed: 12/28/2023] Open
Abstract
Tailor-made notched music training (TMNMT) is a promising therapy for tinnitus. Residual inhibition (RI) is one of the few interventions that can temporarily inhibit tinnitus, which is a useful technique that can be applied to tinnitus research and explore tinnitus mechanisms. In this study, RI effect of TMNMT in tinnitus was investigated mainly using behavioral tests, EEG spectral and microstate analysis. To our knowledge, this study is the first to investigate RI effect of TMNMT. A total of 44 participants with tinnitus were divided into TMNMT group (22 participants; ECnm, NMnm, RInm represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by TMNMT music, respectively) and Placebo control group (22 participants; ECpb, PBpb, RIpb represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by Placebo music, respectively) in a single-blind manner. Behavioral tests, EEG spectral analysis (covering delta, theta, alpha, beta, gamma frequency bands) and microstate analysis (involving four microstate classes, A to D) were employed to evaluate RI effect of TMNMT. The results of the study showed that TMNMT had a stronger inhibition ability and longer inhibition time according to the behavioral tests compared to Placebo. Spectral analysis showed that RI effect of TMNMT increased significantly the power spectral density (PSD) of delta, theta bands and decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of TMNMT had shorter duration (microstate B, microstate C), higher Occurrence (microstate A, microstate C, microstate D), Coverage (microstate A) and transition probabilities (microstate A to microstate B, microstate A to microstate D and microstate D to microstate A). Meanwhile, RI effect of Placebo decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of Placebo had shorter duration (microstate C, microstate D), higher occurrence (microstate B, microstate C), lower coverage (microstate C, microstate D), higher transition probabilities (microstate A to microstate B, microstate B to microstate A). It was also found that the intensity of tinnitus symptoms was significant positively correlated with the duration of microstate B in five subgroups (ECnm, NMnm, RInm, ECpb, PBpb). Our study provided valuable experimental evidence and practical applications for the effectiveness of TMNMT as a novel music therapy for tinnitus. The observed stronger residual inhibition (RI) ability of TMNMT supported its potential applications in tinnitus treatment. Furthermore, the temporal dynamics of EEG microstates serve as novel functional and trait markers of synchronous brain activity that contribute to a deep understanding of the neural mechanism underlying TMNMT treatment for tinnitus.
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Affiliation(s)
- Min Zhu
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Qin Gong
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
- School of Medicine, Shanghai University, Shanghai, China
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5
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Neuner B, Wolter S, McCarthy WJ, Spies C, Cunningham C, Radtke FM, Franck M, Koenig T. EEG microstate quantifiers and state space descriptors during anaesthesia in patients with postoperative delirium: a descriptive analysis. Brain Commun 2023; 5:fcad270. [PMID: 37942086 PMCID: PMC10629467 DOI: 10.1093/braincomms/fcad270] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 08/21/2023] [Accepted: 10/16/2023] [Indexed: 11/10/2023] Open
Abstract
Postoperative delirium is a serious sequela of surgery and surgery-related anaesthesia. One recommended method to prevent postoperative delirium is using bi-frontal EEG recording. The single, processed index of depth of anaesthesia allows the anaesthetist to avoid episodes of suppression EEG and excessively deep anaesthesia. The study data presented here were based on multichannel (19 channels) EEG recordings during anaesthesia. This enabled the analysis of various parameters of global electrical brain activity. These parameters were used to compare microstate topographies under anaesthesia with those in healthy volunteers and to analyse changes in microstate quantifiers and EEG global state space descriptors with increasing exposure to anaesthesia. Seventy-three patients from the Surgery Depth of Anaesthesia and Cognitive Outcome study (SRCTN 36437985) received intraoperative multichannel EEG recordings. Altogether, 720 min of artefact-free EEG data, including 210 min (29.2%) of suppression EEG, were analysed. EEG microstate topographies, microstate quantifiers (duration, frequency of occurrence and global field power) and the state space descriptors sigma (overall EEG power), phi (generalized frequency) and omega (number of uncorrelated brain processes) were evaluated as a function of duration of exposure to anaesthesia, suppression EEG and subsequent development of postoperative delirium. The major analyses involved covariate-adjusted linear mixed-effects models. The older (71 ± 7 years), predominantly male (60%) patients received a median exposure of 210 (range: 75-675) min of anaesthesia. During seven postoperative days, 21 patients (29%) developed postoperative delirium. Microstate topographies under anaesthesia resembled topographies from healthy and much younger awake persons. With increasing duration of exposure to anaesthesia, single microstate quantifiers progressed differently in suppression or non-suppression EEG and in patients with or without subsequent postoperative delirium. The most pronounced changes occurred during enduring suppression EEG in patients with subsequent postoperative delirium: duration and frequency of occurrence of microstates C and D progressed in opposite directions, and the state space descriptors showed a pattern of declining uncorrelated brain processes (omega) combined with increasing EEG variance (sigma). With increasing exposure to general anaesthesia, multiple changes in the dynamics of microstates and global EEG parameters occurred. These changes varied partly between suppression and non-suppression EEG and between patients with or without subsequent postoperative delirium. Ongoing suppression EEG in patients with subsequent postoperative delirium was associated with reduced network complexity in combination with increased overall EEG power. Additionally, marked changes in quantifiers in microstate C and in microstate D occurred. These putatively adverse intraoperative trajectories in global electrical brain activity may be seen as preceding and ultimately predicting postoperative delirium.
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Affiliation(s)
- Bruno Neuner
- Department of Anaesthesiology and Intensive Care Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, 10117 Berlin, Germany
| | - Simone Wolter
- Department of Anaesthesiology and Intensive Care Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, 10117 Berlin, Germany
| | - William J McCarthy
- Centre for Cancer Prevention and Control Research, Fielding School of Public Health and Jonsson Comprehensive Cancer Centre, University of California Los Angeles (UCLA), Los Angeles, CA 90095-1781, USA
| | - Claudia Spies
- Department of Anaesthesiology and Intensive Care Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, 10117 Berlin, Germany
| | - Colm Cunningham
- School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute & Trinity College Institute of Neuroscience, Trinity College Dublin, 2 D02 R590 Dublin, Ireland
| | - Finn M Radtke
- Department of Anaesthesiology and Intensive Care Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, 10117 Berlin, Germany
- Department of Anaesthesia and Intensive Care, Hospital of Nykøbing Falster, Fjordvej 15, 4800 Nykøbing Falster, Denmark
- University of Southern Denmark (SDU), Campusvej 55, 5230 Odense, Denmark
| | - Martin Franck
- Department of Anaesthesiology and Intensive Care Medicine, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, 10117 Berlin, Germany
- Department of Anaesthesia, Alexianer St.Hedwig Hospital, 10115 Berlin, Germany
| | - Thomas Koenig
- University Hospital of Psychiatry, Translational Research Centre, University of Bern, 3000 Bern, Switzerland
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Zhao S, Lin H, Chi A, Gao Y. Effects of acute exercise fatigue on the spatiotemporal dynamics of resting-state large-scale brain networks. Front Neurosci 2023; 17:986368. [PMID: 36743803 PMCID: PMC9895387 DOI: 10.3389/fnins.2023.986368] [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: 07/05/2022] [Accepted: 01/05/2023] [Indexed: 01/21/2023] Open
Abstract
Introduction Various approaches have been used to explore different aspects of the regulation of brain activity by acute exercise, but few studies have been conducted on the effects of acute exercise fatigue on large-scale brain functional networks. Therefore, the present study aimed to explore the effects of acute exercise fatigue on resting-state electroencephalogram (EEG) microstates and large-scale brain network rhythm energy. Methods The Bruce protocol was used as the experimental exercise model with a self-controlled experimental design. Thirty males performed incremental load exercise tests on treadmill until exhaustion. EEG signal acquisition was completed before and after exercise. EEG microstates and resting-state cortical rhythm techniques were used to analyze the EEG signal. Results The microstate results showed that the duration, occurrence, and contribution of Microstate C were significantly higher after exhaustive exercise (p's < 0.01). There was a significantly lower contribution of Microstate D (p < 0.05), a significant increase in transition probabilities between Microstate A and C (p < 0.05), and a significant decrease in transition probabilities between Microstate B and D (p < 0.05). The results of EEG rhythm energy on the large-scale brain network showed that the energy in the high-frequency β band was significantly higher in the visual network (p < 0.05). Discussion Our results suggest that frequently Microstate C associated with the convexity network are important for the organism to respond to internal and external information stimuli and thus regulate motor behavior in time to protect organism integrity. The decreases in Microstate D parameters, associated with the attentional network, are an important neural mechanism explaining the decrease in attention-related cognitive or behavioral performance due to acute exercise fatigue. The high energy in the high-frequency β band on the visual network can be explained in the sense of the neural efficiency hypothesis, which indicates a decrease in neural efficiency.
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Affiliation(s)
- Shanguang Zhao
- Institute of Physical Education, Shaanxi Normal University, Xi’an, China,Faculty of Sports and Exercise Science, Universiti Malaya, Kuala Lumpur, Malaysia
| | - Hao Lin
- Institute of Physical Education, Shaanxi Normal University, Xi’an, China
| | - Aiping Chi
- Institute of Physical Education, Shaanxi Normal University, Xi’an, China,*Correspondence: Aiping Chi,
| | - Yuanyuan Gao
- Institute of Physical Education, Shaanxi Normal University, Xi’an, China,Yuanyuan Gao,
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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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Keihani A, Sajadi SS, Hasani M, Ferrarelli F. Bayesian Optimization of Machine Learning Classification of Resting-State EEG Microstates in Schizophrenia: A Proof-of-Concept Preliminary Study Based on Secondary Analysis. Brain Sci 2022; 12:1497. [PMID: 36358423 PMCID: PMC9688063 DOI: 10.3390/brainsci12111497] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/22/2022] [Accepted: 11/02/2022] [Indexed: 01/19/2024] Open
Abstract
Resting-state electroencephalography (EEG) microstates reflect sub-second, quasi-stable states of brain activity. Several studies have reported alterations of microstate features in patients with schizophrenia (SZ). Based on these findings, it has been suggested that microstates may represent neurophysiological biomarkers for the classification of SZ. To explore this possibility, machine learning approaches can be employed. Bayesian optimization is a machine learning approach that selects the best-fitted machine learning model with tuned hyperparameters from existing models to improve the classification. In this proof-of-concept preliminary study based on secondary analysis, 20 microstate features were extracted from 14 SZ patients and 14 healthy controls' EEG signals. These parameters were then ranked as predictors based on their importance, and an optimized machine learning approach was applied to evaluate the performance of the classification. SZ patients had altered microstate features compared to healthy controls. Furthermore, Bayesian optimization outperformed conventional multivariate analyses and showed the highest accuracy (90.93%), AUC (0.90), sensitivity (91.37%), and specificity (90.48%), with reliable results using just six microstate predictors. Altogether, in this proof-of-concept study, we showed that machine learning with Bayesian optimization can be utilized to characterize EEG microstate alterations and contribute to the classification of SZ patients.
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Affiliation(s)
- Ahmadreza Keihani
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Seyed Saman Sajadi
- Department of Medical Physics & Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran 1416634793, Iran
| | - Mahsa Hasani
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran 1985717443, Iran
| | - Fabio Ferrarelli
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
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Chen PH, Ku HL, Wang JK, Kang JH, Hsu TY. Electroencephalographic Microstates are Correlated with Global Functioning in Schizophrenia But Not in Bipolar Disorder. Clin EEG Neurosci 2022; 54:215-223. [PMID: 35491557 DOI: 10.1177/15500594221098286] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Objectives. Microstate studies of electroencephalograms (EEGs) on schizophrenia (SCZ) and bipolar disorder (BD) demonstrated categorical differences. The relationship between microstate indices and clinical symptoms in each group, however, remained unclear. Our objective was to examine associations between EEG microstates and the core features of SCZ and BD. Methods. This study examined the resting EEG data of 40 patients with SCZ, 19 patients with BD (12 BD type I and 7 BD type II), and 16 healthy controls. EEG topographic maps were divided into four canonical microstate classes: A, B, C, and D. The Positive and Negative Syndrome Scale (PANSS), Young Mania Rating Scale, Hamilton Depression Rating Scale (HAMD), and Global Assessment of Functioning (GAF) were used to measure clinical symptoms and global functioning. Results. There was a significant inverse correlation between the proportion of time spent in microstate class A and GAF in patients with SCZ but not BD. Furthermore, the occurrence of microstate class A was positively correlated with the Positive Scale scores of the PANSS. Nevertheless, there were no group differences between the microstate classes. Conclusions. The results of this study indicate a negative correlation between microstate class A and global functioning in SCZ but not in BD. The association may be mediated by positive symptoms of SZ. Neural mechanisms underlying this relationship require further investigation.
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Affiliation(s)
- Pao-Huan Chen
- Department of Psychiatry, 63474Taipei Medical University Hospital, Taipei.,Department of Psychiatry, School of Medicine, College of Medicine, 38032Taipei Medical University, Taipei
| | - Hsiao-Lun Ku
- Department of Psychiatry, School of Medicine, College of Medicine, 38032Taipei Medical University, Taipei.,Department of Psychiatry, 38032Taipei Medical University Shuang-Ho Hospital, New Taipei City.,Brain and Consciousness Research Centre, TMU Shuang-Ho Hospital, New Taipei City
| | - Jiunn-Kae Wang
- Department of Psychiatry, School of Medicine, College of Medicine, 38032Taipei Medical University, Taipei.,Department of Psychiatry, 38032Taipei Medical University Shuang-Ho Hospital, New Taipei City.,Brain and Consciousness Research Centre, TMU Shuang-Ho Hospital, New Taipei City
| | - Jiunn-Horng Kang
- Department of Physical Medicine and Rehabilitation, 63474Taipei Medical University Hospital, Taipei.,Graduate Institute of Nanomedicine and Medical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei.,Research Center of Artificial Intelligence in Medicine, 38032Taipei Medical University, Taipei
| | - Tzu-Yu Hsu
- Brain and Consciousness Research Centre, TMU Shuang-Ho Hospital, New Taipei City.,Graduate Institute of Mind, Brain and Consciousness, 38032Taipei Medical University, Taipei
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10
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Lin G, Wu Z, Chen B, Zhang M, Wang Q, Liu M, Zhang S, Yang M, Ning Y, Zhong X. Altered Microstate Dynamics and Spatial Complexity in Late-Life Schizophrenia. Front Psychiatry 2022; 13:907802. [PMID: 35832599 PMCID: PMC9271628 DOI: 10.3389/fpsyt.2022.907802] [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: 03/30/2022] [Accepted: 06/03/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Resting-state EEG microstate and omega complexity analyses have been widely used to explore deviant brain function in various neuropsychiatric disorders. This study aimed to investigate the features of microstate dynamics and spatial complexity in patients with late-life schizophrenia (LLS). METHOD Microstate and omega complexity analyses were performed on resting-state EEG data from 39 in patients with LLS and compared with 40 elderly normal controls (NCs). RESULT The duration of microstate classes A and D were significantly higher in patients with LLS compared with NCs. The occurrence of microstate classes A, B, and C was significantly lower in patients with LLS compared with NCs. LLS patients have a lower time coverage of microstate class A and a higher time coverage of class D than NCs. Transition probabilities from microstate class A to B and from class A to C were significantly lower in patients with LLS compared with NCs. Transition probabilities between microstate class B and D were significantly higher in patients with LLS compared with NCs. Global omega complexity and anterior omega complexity were significantly higher in patients with LLS compared with NCs. CONCLUSION This study revealed an altered pattern of microstate dynamics and omega complexity in patients with LLS. This may reflect the disturbed neural basis underlying LLS and enhance the understanding of the pathophysiology of LLS.
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Affiliation(s)
- Gaohong Lin
- Center for Geriatric Neuroscience, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Zhangying Wu
- Center for Geriatric Neuroscience, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Ben Chen
- Center for Geriatric Neuroscience, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Min Zhang
- Center for Geriatric Neuroscience, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Qiang Wang
- Center for Geriatric Neuroscience, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Meiling Liu
- Center for Geriatric Neuroscience, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Si Zhang
- Center for Geriatric Neuroscience, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Mingfeng Yang
- The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Yuping Ning
- Center for Geriatric Neuroscience, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China.,The First School of Clinical Medicine, Southern Medical University, Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Xiaomei Zhong
- Center for Geriatric Neuroscience, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
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11
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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: 7] [Impact Index Per Article: 3.5] [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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