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Li Y, Yang B, Ma J, Gao S, Zeng H, Wang W. Assessment of rTMS treatment effects for methamphetamine use disorder based on EEG microstates. Behav Brain Res 2024; 465:114959. [PMID: 38494128 DOI: 10.1016/j.bbr.2024.114959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 03/10/2024] [Accepted: 03/14/2024] [Indexed: 03/19/2024]
Abstract
Microstates have been proposed as topographical maps representing large-scale resting-state networks and have recently been suggested as markers for methamphetamine use disorder (MUD). However, it is unknown whether and how they change after repetitive transcranial magnetic stimulation (rTMS) intervention. This study included a comprehensive subject population to investigate the effect of rTMS on MUD microstates. 34 patients with MUD underwent a 4-week randomized, double-blind rTMS intervention (active=17, sham=17). Two resting-state EEG recordings and VAS evaluations were conducted before and after the intervention period. Additionally, 17 healthy individuals were included as baseline controls. The modified k-means clustering method was used to calculate four microstates (MS-A∼MS-D) of EEG, and the FC network was also analyzed. The differences in microstate indicators between groups and within groups were compared. The durations of MS-A and MS-B microstates in patients with MUD were significantly lower than that in HC but showed significant improvements after rTMS intervention. Changes in microstate indicators were found to be significantly correlated with changes in craving level. Furthermore, selective modulation of the resting-state network by rTMS was observed in the FC network. The findings indicate that changes in microstates in patients with MUD are associated with craving level improvement following rTMS, suggesting they may serve as valuable evaluation markers.
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Affiliation(s)
- Yongcong Li
- School of Medicine, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
| | - Banghua Yang
- School of Medicine, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
| | - Jun Ma
- School of Medicine, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
| | - Shouwei Gao
- School of Medicine, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
| | - Hui Zeng
- School of Medicine, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
| | - Wen Wang
- Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Air Force Medical University, Shaanxi 710038, China.
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Nagabhushan Kalburgi S, Kleinert T, Aryan D, Nash K, Schiller B, Koenig T. MICROSTATELAB: The EEGLAB Toolbox for Resting-State Microstate Analysis. Brain Topogr 2023:10.1007/s10548-023-01003-5. [PMID: 37697212 DOI: 10.1007/s10548-023-01003-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 08/22/2023] [Indexed: 09/13/2023]
Abstract
Microstate analysis is a multivariate method that enables investigations of the temporal dynamics of large-scale neural networks in EEG recordings of human brain activity. To meet the enormously increasing interest in this approach, we provide a thoroughly updated version of the first open source EEGLAB toolbox for the standardized identification, visualization, and quantification of microstates in resting-state EEG data. The toolbox allows scientists to (i) identify individual, mean, and grand mean microstate maps using topographical clustering approaches, (ii) check data quality and detect outlier maps, (iii) visualize, sort, and label individual, mean, and grand mean microstate maps according to published maps, (iv) compare topographical similarities of group and grand mean microstate maps and quantify shared variances, (v) obtain the temporal dynamics of the microstate classes in individual EEGs, (vi) export quantifications of these temporal dynamics of the microstates for statistical tests, and finally, (vii) test for topographical differences between groups and conditions using topographic analysis of variance (TANOVA). Here, we introduce the toolbox in a step-by-step tutorial, using a sample dataset of 34 resting-state EEG recordings that are publicly available to follow along with this tutorial. The goals of this manuscript are (a) to provide a standardized, freely available toolbox for resting-state microstate analysis to the scientific community, (b) to allow researchers to use best practices for microstate analysis by following a step-by-step tutorial, and (c) to improve the methodological standards of microstate research by providing previously unavailable functions and recommendations on critical decisions required in microstate analyses.
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Affiliation(s)
| | - Tobias Kleinert
- Laboratory for Biological Psychology, Clinical Psychology, and Psychotherapy, Albert-Ludwigs-University of Freiburg, Stefan-Meier-Straße 8, 79104, Freiburg, Germany
- Department of Ergonomics, Leibniz Research Centre for Working Environment and Human Factors, Ardeystr. 67, 44139, Dortmund, Germany
| | - Delara Aryan
- Children's Hospital Los Angeles, The Saban Research Institute, Los Angeles, CA, 90027, USA
| | - Kyle Nash
- Department of Psychology, University of Alberta, Edmonton, AB, T6G 2E9, Canada
| | - Bastian Schiller
- Laboratory for Biological Psychology, Clinical Psychology, and Psychotherapy, Albert-Ludwigs-University of Freiburg, Stefan-Meier-Straße 8, 79104, Freiburg, Germany
| | - Thomas Koenig
- Children's Hospital Los Angeles, The Saban Research Institute, Los Angeles, CA, 90027, USA.
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, CH-3000, Bern, Switzerland.
- Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Huddinge, Sweden.
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Tamano R, Ogawa T, Katagiri A, Cai C, Asai T, Kawanabe M. 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] [What about the content of this article? (0)] [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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Zhao S, Ng SC, Khoo S, Chi A. Temporal and Spatial Dynamics of EEG Features in Female College Students with Subclinical Depression. Int J Environ Res Public Health 2022; 19:1778. [PMID: 35162800 DOI: 10.3390/ijerph19031778] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [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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Li X, Dong F, Zhang Y, Wang J, Wang Z, Sun Y, Zhang M, Xue T, Ren Y, Lv X, Yuan K, Yu D. Altered resting-state electroencephalography microstate characteristics in young male smokers. Front Psychiatry 2022; 13:1008007. [PMID: 36267852 PMCID: PMC9577082 DOI: 10.3389/fpsyt.2022.1008007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 09/06/2022] [Indexed: 11/24/2022] Open
Abstract
The development of nicotine addiction was associated with the abnormalities of intrinsic functional networks during the resting state in young adult smokers. As a whole-brain imaging approach, EEG microstate analysis treated multichannel EEG recordings as a series of quasi-steady microscopic states which were related to the resting-state networks (RSNs) found by fMRI. The aim of this study was to examine whether the resting-state EEG microstate analysis may provide novel insights into the abnormal temporal properties of intrinsic brain activities in young smokers. We used 64-channel resting-state EEG datasets to investigate alterations in microstate characteristics between twenty-five young smokers and 25 age- and gender-matched non-smoking controls. Four classic EEG microstates (microstate A, B, C, and D) were obtained, and the four temporal parameters of each microstate were extracted, i.e., duration, occurrence, coverage, and transition probabilities. Compared with non-smoking controls, young smokers showed decreased occurrence of microstate C and increased duration of microstate D. Furthermore, both the duration and coverage of microstate D were significantly negatively correlated with Fagerstrom Test of Nicotine Dependence (FTND) in young smoker group. The complex changes in the microstate time-domain parameters might correspond to the abnormalities of RSNs in analyses of FC measured with fMRI in the previous studies and indicate the altered specific brain functions in young smokers. Microstate D could be potentially represented as a selective biomarker for predicting the dependence degree of adolescent smokers on cigarettes. These results suggested that EEG microstate analysis might detect the deviant functions of large-scale cortical activities in young smokers and provide a new perspective for the study of brain networks of adolescent smokers.
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Affiliation(s)
- Xiaojian Li
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Fang Dong
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Yunmiao Zhang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Juan Wang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Zhengxi Wang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Yaning Sun
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Ming Zhang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Ting Xue
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Yan Ren
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Xiaoqi Lv
- College of Information Engineering, Inner Mongolia University of Technology, Hohhot, China
| | - Kai Yuan
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China.,School of Life Sciences and Technology, Xidian University, Xi'an, Shaanxi, China
| | - Dahua Yu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
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Deolindo CS, Ribeiro MW, de Aratanha MAA, Scarpari JRS, Forster CHQ, da Silva RGA, Machado BS, Amaro Junior E, König T, Kozasa EH. Microstates in complex and dynamical environments: Unraveling situational awareness in critical helicopter landing maneuvers. Hum Brain Mapp 2021; 42:3168-3181. [PMID: 33942444 PMCID: PMC8193508 DOI: 10.1002/hbm.25426] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 02/19/2021] [Accepted: 03/13/2021] [Indexed: 01/20/2023] Open
Abstract
Understanding decision-making in complex and dynamic environments is relevant for designing strategies targeting safety improvements and error rate reductions. However, studies evaluating brain dynamics in realistic situations are scarce in the literature. Given the evidence that specific microstates may be associated with perception and attention, in this work we explored for the first time the application of the microstate model in an ecological, dynamic and complex scenario. More specifically, we evaluated elite helicopter pilots during engine-failure missions in the vicinity of the so called "dead man's curve," which establishes the operational limits for a safe landing after the execution of a recovery maneuver (autorotation). Pilots from the Brazilian Air Force flew a AS-350 helicopter in a certified aerodrome and physiological sensor data were synchronized with the aircraft's flight test instrumentation. We assessed these neural correlates during maneuver execution, by comparing their modulations and source reconstructed activity with baseline epochs before and after flights. We show that the topographies of our microstate templates with 4, 5, and 6 classes resemble the literature, and that a distinct modulation characterizes decision-making intervals. Moreover, the source reconstruction result points to a differential activity in the medial prefrontal cortex, which is associated to emotional regulation circuits in the brain. Our results suggest that microstates are promising neural correlates to evaluate realistic situations, even in a challenging and intrinsically noisy environment. Furthermore, it strengthens their usage and expands their application for studying cognition under more realistic conditions.
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Affiliation(s)
| | | | | | - José R S Scarpari
- Instituto Tecnológico de Aeronáutica, São José dos Campos, Brazil.,Instituto de Pesquisas e Ensaio em Voos (IPEV), São José dos Campos, Brazil
| | | | | | | | - Edson Amaro Junior
- Hospital Israelita Albert Einstein, São Paulo, Brazil.,Hospital das Clínicas, University of São Paulo Medical School, São Paulo, Brazil
| | - Thomas König
- University Hospital of Psychiatry, Bern, Switzerland
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Pan DN, Hoid D, Gu RL, Li X. Emotional working memory training reduces rumination and alters the EEG microstate in anxious individuals. Neuroimage Clin 2020; 28:102488. [PMID: 33395979 DOI: 10.1016/j.nicl.2020.102488] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 10/26/2020] [Indexed: 12/17/2022]
Abstract
Rumination is an important etiological factor of anxiety pathology, with its mechanism related to the deficit of working memory. The current study examined whether working memory training (WM-T) and emotional working memory training (EWM-T) could reduce rumination in anxious individuals. The participants with high trait anxiety underwent 21 days of mobile applications-based WM-T (n = 34), EWM-T (n = 36) or placebo control (n = 36), with questionnaires, cognitive tasks, and resting electroencephalogram (EEG) as the pre-post-test indicators. The results revealed that two training groups obtained comparable operation span increases (WM-T: d = 0.53; EWM-T: d = 0.65), updating improvement (WM-T: d = 0.43; EWM-T: d = 0.60) and shifting improvement (WM-T: d = 0.49; EWM-T: d = 0.72). Furthermore, compared to the control group, the EWM-T showed significant self-reported rumination reduction (d = 0.69), increased inhibition ability (d = 0.72), as well as modification of resting EEG microstate C parameters (Duration C: d = 0.42, Coverage C: d = 0.39), which were closely related to rumination level (r ~ 0.4). The WM-T group also showed the potential to reduced self-reported rumination (d = 0.45), but with the absence of the observable inhibition improvement and resting EEG changes. The correlation analysis suggested that the emotional benefits of WM-T depending more on improved updating and shifting, and that of EWM-T depending more on improved inhibition ability. The advantage to add emotional distractions into general working memory training for targeting rumination related anxiety has been discussed.
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Chen T, Su H, Zhong N, Tan H, Li X, Meng Y, Duan C, Zhang C, Bao J, Xu D, Song W, Zou J, Liu T, Zhan Q, Jiang H, Zhao M. Disrupted brain network dynamics and cognitive functions in methamphetamine use disorder: insights from EEG microstates. BMC Psychiatry 2020; 20:334. [PMID: 32580716 PMCID: PMC7315471 DOI: 10.1186/s12888-020-02743-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 06/18/2020] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Dysfunction in brain network dynamics has been found to correlate with many psychiatric disorders. However, there is limited research regarding resting electroencephalogram (EEG) brain network and its association with cognitive process for patients with methamphetamine use disorder (MUD). This study aimed at using EEG microstate analysis to determine whether brain network dynamics in patients with MUD differ from those of healthy controls (HC). METHODS A total of 55 MUD patients and 27 matched healthy controls were included for analysis. The resting brain activity was recorded by 64-channel electroencephalography. EEG microstate parameters and intracerebral current sources of each EEG microstate were compared between the two groups. Generalized linear regression model was used to explore the correlation between significant microstates with drug history and cognitive functions. RESULTS MUD patients showed lower mean durations of the microstate classes A and B, and a higher global explained variance of the microstate class C. Besides, MUD patients presented with different current density power in microstates A, B, and C relative to the HC. The generalized linear model showed that MA use frequency is negatively correlated with the MMD of class A. Further, the generalized linear model showed that MA use frequency, scores of Two-back task, and the error rate of MA word are correlated with the MMD and GEV of class B, respectively. CONCLUSIONS Intracranial current source densities of resting EEG microstates are disrupted in MUD patients, hence causing temporal changes in microstate topographies, which are correlated with attention bias and history of drug use.
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Affiliation(s)
- Tianzhen Chen
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030 China
| | - Hang Su
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030 China
| | - Na Zhong
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030 China
| | - Haoye Tan
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030 China
| | - Xiaotong Li
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030 China
| | - Yiran Meng
- Yunnan Institute on Drug Dependence, Kunming, Yunnan China
| | - Chunmei Duan
- Yunnan Institute on Drug Dependence, Kunming, Yunnan China
| | - Congbin Zhang
- Yunnan Institute on Drug Dependence, Kunming, Yunnan China
| | - Juwang Bao
- grid.28703.3e0000 0000 9040 3743Institute of Higher Education, Beijing University of Technology, Beijing, China
| | - Ding Xu
- Shanghai Bureau of Drug Rehabilitation Administration, Shanghai, China
| | - Weidong Song
- Shanghai Bureau of Drug Rehabilitation Administration, Shanghai, China
| | - Jixue Zou
- Department of Health, Yunnan Bureau of Drug Rehabilitation Administration, Kunming, Yunnan China
| | - Tao Liu
- Yunnan Third Compulsory Drug Dependence Rehablitation Center Hospital, Kunming, Yunnan China
| | - Qingqing Zhan
- Yunnan Third Compulsory Drug Dependence Rehablitation Center Hospital, Kunming, Yunnan China
| | - Haifeng Jiang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030, China. .,Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China.
| | - Min Zhao
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030 China ,grid.415630.50000 0004 1782 6212Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China ,grid.16821.3c0000 0004 0368 8293Institute of Psychological and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China ,grid.9227.e0000000119573309CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences, Shanghai, China
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Kong W, Wang L, Zhang J, Zhao Q, Sun J. The Dynamic EEG Microstates in Mental Rotation. Sensors (Basel) 2018; 18:s18092920. [PMID: 30177611 PMCID: PMC6165343 DOI: 10.3390/s18092920] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2018] [Revised: 08/20/2018] [Accepted: 08/30/2018] [Indexed: 11/25/2022]
Abstract
Mental rotation is generally analyzed based on event-related potential (ERP) in a time domain with several characteristic electrodes, but neglects the whole spatial-temporal brain pattern in the cognitive process which may reflect the underlying cognitive mechanism. In this paper, we mainly proposed an approach based on microstates to examine the encoding of mental rotation from the spatial-temporal changes of EEG signals. In particular, we collected EEG data from 11 healthy subjects in a mental rotation cognitive task using 12 different stimulus pictures representing left and right hands at various rotational angles. We applied the microstate method to investigate the microstates conveyed by the event-related potential extracted from EEG data during mental rotation, and obtained four microstate modes (referred to as modes A, B, C, D, respectively). Subsequently, we defined several measures, including microstate sequences, topographical map, hemispheric lateralization, and duration of microstate, to characterize the dynamics of microstates during mental rotation. We observed that (1) the microstates sequence had a specified progressing mode, i.e., A→B→A; (2) the activation of the right parietal occipital region was stronger than that of the left parietal occipital region according to the hemispheric lateralization of the microstates mode A; and (3) the duration of the second microstates mode A showed the shorter duration in the vertical stimuli, named “angle effect”.
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Affiliation(s)
- Wanzeng Kong
- School of Computer and Technology, Hangzhou Dianzi University, Hangzhou 310000, China.
- Fujian Key Laboratory of Rehabilitation Technology, Fuzhou 350000, China.
| | - Luyun Wang
- School of Computer and Technology, Hangzhou Dianzi University, Hangzhou 310000, China.
| | - Jianhai Zhang
- School of Computer and Technology, Hangzhou Dianzi University, Hangzhou 310000, China.
| | - Qibin Zhao
- Tensor Learning Unit, RIKEN AIP, Tokyo 103-0027, Japan.
| | - Junfeng Sun
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200000, China.
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10
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Pedroni A, Gianotti LRR, Koenig T, Lehmann D, Faber P, Knoch D. Temporal Characteristics of EEG Microstates Mediate Trial-by-Trial Risk Taking. Brain Topogr 2016; 30:149-159. [PMID: 27933418 DOI: 10.1007/s10548-016-0539-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Accepted: 11/26/2016] [Indexed: 11/26/2022]
Abstract
People seem to have difficulties when perceiving events whose outcome has no influence on the outcome of future events. This illusion that patterns exist where there are none may lead to adverse consequences, such as escalating losses in financial trading or gambling debt. Despite the enormous social consequences of these cognitive biases, however, their neural underpinnings are poorly understood. Attempts to investigate them have so far relied on evoked neural activity, whereas spontaneous brain activity has been treated as noise to be averaged out. Here, we focus on the spontaneous electroencephalographic (EEG) activity during inter-trial-intervals (ITI) in a sequential risky decision-making task. Using multilevel mediation analyses, our results show that the percentage of time covered by two EEG microstates (i.e., functional brain-states of coherent activity) mediate the influence of outcomes of prior decisions on subsequent risk taking on a trial-by-trial basis. The devised multilevel mediation analysis of the temporal characteristics of EEG microstates during ITI provides a new window into the neurobiology of decision making by bringing the spontaneous brain activity to the forefront of the analysis.
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Affiliation(s)
- Andreas Pedroni
- Department of Psychology, Methods of Plasticity Research, University of Zurich, Andreasstrasse 15, 8050, Zurich, Switzerland.
| | - Lorena R R Gianotti
- Department of Social Psychology and Social Neuroscience, Institute of Psychology, University of Bern, Fabrikstrasse 8, 3012, Bern, Switzerland.
- Department of Psychiatry, Psychotherapy and Psychosomatics, The KEY Institute for Brain-Mind Research, University Hospital of Psychiatry, Zurich, Switzerland.
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Dietrich Lehmann
- Department of Psychiatry, Psychotherapy and Psychosomatics, The KEY Institute for Brain-Mind Research, University Hospital of Psychiatry, Zurich, Switzerland
| | - Pascal Faber
- Department of Psychiatry, Psychotherapy and Psychosomatics, The KEY Institute for Brain-Mind Research, University Hospital of Psychiatry, Zurich, Switzerland
| | - Daria Knoch
- Department of Social Psychology and Social Neuroscience, Institute of Psychology, University of Bern, Fabrikstrasse 8, 3012, Bern, Switzerland
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11
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Abstract
Human perception fluctuates with the phase of neural oscillations in the presence of environmental rhythmic structure by which neural oscillations become entrained. However, in the absence of predictability afforded by rhythmic structure, we hypothesize that the neural dynamical states associated with optimal psychophysical performance are more complex than what has been described previously for rhythmic stimuli. The current electroencephalography study characterized the brain dynamics associated with optimal detection of gaps embedded in narrow-band acoustic noise stimuli lacking low-frequency rhythmic structure. Optimal gap detection was associated with three spectrotemporally distinct delta-governed neural microstates. Individual microstates were characterized by unique instantaneous combinations of neural phase in the delta, theta, and alpha frequency bands. Critically, gap detection was not predictable from local fluctuations in stimulus acoustics. The current results suggest that, in the absence of rhythmic structure to entrain neural oscillations, good performance hinges on complex neural states that vary from moment to moment. Significance statement: Our ability to hear faint sounds fluctuates together with slow brain activity that synchronizes with environmental rhythms. However, it is so far not known how brain activity at different time scales might interact to influence perception when there is no rhythm with which brain activity can synchronize. Here, we used electroencephalography to measure brain activity while participants listened for short silences that interrupted ongoing noise. We examined brain activity in three different frequency bands: delta, theta, and alpha. Participants' ability to detect gaps depended on different numbers of frequency bands--sometimes one, sometimes two, and sometimes three--at different times. Changes in the number of frequency bands that predict perception are a hallmark of a complex neural system.
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Rieger K, Diaz Hernandez L, Baenninger A, Koenig T. 15 Years of Microstate Research in Schizophrenia - Where Are We? A Meta-Analysis. Front Psychiatry 2016; 7:22. [PMID: 26955358 PMCID: PMC4767900 DOI: 10.3389/fpsyt.2016.00022] [Citation(s) in RCA: 98] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Accepted: 02/09/2016] [Indexed: 01/24/2023] Open
Abstract
Schizophrenia patients show abnormalities in a broad range of task demands. Therefore, an explanation common to all these abnormalities has to be sought independently of any particular task, ideally in the brain dynamics before a task takes place or during resting state. For the neurobiological investigation of such baseline states, EEG microstate analysis is particularly well suited, because it identifies subsecond global states of stable connectivity patterns directly related to the recruitment of different types of information processing modes (e.g., integration of top-down and bottom-up information). Meanwhile, there is an accumulation of evidence that particular microstate networks are selectively affected in schizophrenia. To obtain an overall estimate of the effect size of these microstate abnormalities, we present a systematic meta-analysis over all studies available to date relating EEG microstates to schizophrenia. Results showed medium size effects for two classes of microstates, namely, a class labeled C that was found to be more frequent in schizophrenia and a class labeled D that was found to be shortened. These abnormalities may correspond to core symptoms of schizophrenia, e.g., insufficient reality testing and self-monitoring as during auditory verbal hallucinations. As interventional studies have shown that these microstate features may be systematically affected using antipsychotic drugs or neurofeedback interventions, these findings may help introducing novel diagnostic and treatment options.
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Affiliation(s)
- Kathryn Rieger
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland; Center for Cognition, Learning and Memory, University of Bern, Bern, Switzerland
| | - Laura Diaz Hernandez
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland; Center for Cognition, Learning and Memory, University of Bern, Bern, Switzerland
| | - Anja Baenninger
- Translational Research Center, University Hospital of Psychiatry, University of Bern , Bern , Switzerland
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland; Center for Cognition, Learning and Memory, University of Bern, Bern, Switzerland
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Khanna A, Pascual-Leone A, Farzan F. Reliability of resting-state microstate features in electroencephalography. PLoS One 2014; 9:e114163. [PMID: 25479614 PMCID: PMC4257589 DOI: 10.1371/journal.pone.0114163] [Citation(s) in RCA: 121] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2014] [Accepted: 11/05/2014] [Indexed: 01/17/2023] Open
Abstract
Background Electroencephalographic (EEG) microstate analysis is a method of identifying quasi-stable functional brain states (“microstates”) that are altered in a number of neuropsychiatric disorders, suggesting their potential use as biomarkers of neurophysiological health and disease. However, use of EEG microstates as neurophysiological biomarkers requires assessment of the test-retest reliability of microstate analysis. Methods We analyzed resting-state, eyes-closed, 30-channel EEG from 10 healthy subjects over 3 sessions spaced approximately 48 hours apart. We identified four microstate classes and calculated the average duration, frequency, and coverage fraction of these microstates. Using Cronbach's α and the standard error of measurement (SEM) as indicators of reliability, we examined: (1) the test-retest reliability of microstate features using a variety of different approaches; (2) the consistency between TAAHC and k-means clustering algorithms; and (3) whether microstate analysis can be reliably conducted with 19 and 8 electrodes. Results The approach of identifying a single set of “global” microstate maps showed the highest reliability (mean Cronbach's α>0.8, SEM ≈10% of mean values) compared to microstates derived by each session or each recording. There was notably low reliability in features calculated from maps extracted individually for each recording, suggesting that the analysis is most reliable when maps are held constant. Features were highly consistent across clustering methods (Cronbach's α>0.9). All features had high test-retest reliability with 19 and 8 electrodes. Conclusions High test-retest reliability and cross-method consistency of microstate features suggests their potential as biomarkers for assessment of the brain's neurophysiological health.
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Affiliation(s)
- Arjun Khanna
- Berenson-Allen Center for Non-invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America
| | - Alvaro Pascual-Leone
- Berenson-Allen Center for Non-invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America
| | - Faranak Farzan
- Berenson-Allen Center for Non-invasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States of America; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
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Cook ND, Carvalho GB, Damasio A. From membrane excitability to metazoan psychology. Trends Neurosci 2014; 37:698-705. [DOI: 10.1016/j.tins.2014.07.011] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2014] [Revised: 07/25/2014] [Accepted: 07/31/2014] [Indexed: 11/28/2022]
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Mathes B, Khalaidovski K, Schmiedt-Fehr C, Basar-Eroglu C. Frontal theta activity is pronounced during illusory perception. Int J Psychophysiol 2014; 94:445-54. [DOI: 10.1016/j.ijpsycho.2014.08.585] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2014] [Revised: 08/14/2014] [Accepted: 08/18/2014] [Indexed: 10/24/2022]
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Fingelkurts AA, Fingelkurts AA. EEG oscillatory states: universality, uniqueness and specificity across healthy-normal, altered and pathological brain conditions. PLoS One 2014; 9:e87507. [PMID: 24505292 DOI: 10.1371/journal.pone.0087507] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
For the first time the dynamic repertoires and oscillatory types of local EEG states in 13 diverse conditions (examined over 9 studies) that covered healthy-normal, altered and pathological brain states were quantified within the same methodological and conceptual framework. EEG oscillatory states were assessed by the probability-classification analysis of short-term EEG spectral patterns. The results demonstrated that brain activity consists of a limited repertoire of local EEG states in any of the examined conditions. The size of the state repertoires was associated with changes in cognition and vigilance or neuropsychopathologic conditions. Additionally universal, optional and unique EEG states across 13 diverse conditions were observed. It was demonstrated also that EEG oscillations which constituted EEG states were characteristic for different groups of conditions in accordance to oscillations' functional significance. The results suggested that (a) there is a limit in the number of local states available to the cortex and many ways in which these local states can rearrange themselves and still produce the same global state and (b) EEG individuality is determined by varying proportions of universal, optional and unique oscillatory states. The results enriched our understanding about dynamic microstructure of EEG-signal.
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Fingelkurts AA, Fingelkurts AA. EEG oscillatory states: universality, uniqueness and specificity across healthy-normal, altered and pathological brain conditions. PLoS One 2014; 9:e87507. [PMID: 24505292 PMCID: PMC3914824 DOI: 10.1371/journal.pone.0087507] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2013] [Accepted: 12/27/2013] [Indexed: 12/19/2022] Open
Abstract
For the first time the dynamic repertoires and oscillatory types of local EEG states in 13 diverse conditions (examined over 9 studies) that covered healthy-normal, altered and pathological brain states were quantified within the same methodological and conceptual framework. EEG oscillatory states were assessed by the probability-classification analysis of short-term EEG spectral patterns. The results demonstrated that brain activity consists of a limited repertoire of local EEG states in any of the examined conditions. The size of the state repertoires was associated with changes in cognition and vigilance or neuropsychopathologic conditions. Additionally universal, optional and unique EEG states across 13 diverse conditions were observed. It was demonstrated also that EEG oscillations which constituted EEG states were characteristic for different groups of conditions in accordance to oscillations' functional significance. The results suggested that (a) there is a limit in the number of local states available to the cortex and many ways in which these local states can rearrange themselves and still produce the same global state and (b) EEG individuality is determined by varying proportions of universal, optional and unique oscillatory states. The results enriched our understanding about dynamic microstructure of EEG-signal.
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Fingelkurts AA, Fingelkurts AA. Operational Architectonics Methodology for EEG Analysis: Theory and Results. Modern Electroencephalographic Assessment Techniques 2013. [DOI: 10.1007/7657_2013_60] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Kornmeier J, Bach M. Ambiguous figures - what happens in the brain when perception changes but not the stimulus. Front Hum Neurosci 2012; 6:51. [PMID: 22461773 PMCID: PMC3309967 DOI: 10.3389/fnhum.2012.00051] [Citation(s) in RCA: 90] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2011] [Accepted: 02/26/2012] [Indexed: 12/05/2022] Open
Abstract
During observation of ambiguous figures our perception reverses spontaneously although the visual information stays unchanged. Research on this phenomenon so far suffered from the difficulty to determine the instant of the endogenous reversals with sufficient temporal precision. A novel experimental paradigm with discontinuous stimulus presentation improved on previous temporal estimates of the reversal event by a factor of three. It revealed that disambiguation of ambiguous visual information takes roughly 50 ms or two loops of recurrent neural activity. Further, the decision about the perceptual outcome has taken place at least 340 ms before the observer is able to indicate the consciously perceived reversal manually. We provide a short review about physiological studies on multistable perception with a focus on electrophysiological data. We further present a new perspective on multistable perception that can easily integrate previous apparently contradicting explanatory approaches. Finally we propose possible extensions toward other research fields where ambiguous figure perception may be useful as an investigative tool.
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Affiliation(s)
- Jürgen Kornmeier
- Institute for Frontier Areas of Psychology and Mental Health Freiburg, Germany
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Abstract
The temporal dynamics and anatomical correlates underlying human visual cognition are traditionally assessed as a function of stimulus properties and task demands. Any non-stimulus related activity is commonly dismissed as noise and eliminated to extract an evoked signal that is only a small fraction of the magnitude of the measured signal. We review studies that challenge this view by showing that non-stimulus related activity is not mere noise but that it has a well-structured organization which can largely determine the processing of upcoming stimuli. We review recent evidence from human electrophysiology that shows how different aspects of pre-stimulus activity such as pre-stimulus EEG frequency power and phase and pre-stimulus EEG microstates can determine qualitative and quantitative properties of both lower and higher-level visual processing. These studies show that low-level sensory processes depend on the momentary excitability of sensory cortices whereas perceptual processes leading to stimulus awareness depend on momentary pre-stimulus activity in higher-level non-visual brain areas. Also speed and accuracy of stimulus identification have likewise been shown to be modulated by pre-stimulus brain states.
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Affiliation(s)
- Juliane Britz
- Functional Brain Mapping Laboratory, Department of Fundamental Neuroscience and Clinic of Neurology, University Medical School and University Hospital of Geneva Geneva, Switzerland
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Kikuchi M, Hashimoto T, Nagasawa T, Hirosawa T, Minabe Y, Yoshimura M, Strik W, Dierks T, Koenig T. Frontal areas contribute to reduced global coordination of resting-state gamma activities in drug-naïve patients with schizophrenia. Schizophr Res 2011; 130:187-94. [PMID: 21696922 DOI: 10.1016/j.schres.2011.06.003] [Citation(s) in RCA: 58] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2011] [Revised: 05/24/2011] [Accepted: 06/01/2011] [Indexed: 12/24/2022]
Abstract
Schizophrenia has been postulated to involve impaired neuronal cooperation in large-scale neural networks, including cortico-cortical circuitry. Alterations in gamma band oscillations have attracted a great deal of interest as they appear to represent a pathophysiological process of cortical dysfunction in schizophrenia. Gamma band oscillations reflect local cortical activities, and the synchronization of these activities among spatially distributed cortical areas has been suggested to play a central role in the formation of networks. To assess global coordination across spatially distributed brain regions, Omega complexity (OC) in multichannel EEG was proposed. Using OC, we investigated global coordination of resting-state EEG activities in both gamma (30-50 Hz) and below-gamma (1.5-30 Hz) bands in drug-naïve patients with schizophrenia and investigated the effects of neuroleptic treatment. We found that gamma band OC was significantly higher in drug-naïve patients with schizophrenia compared to control subjects and that a right frontal electrode (F3) contributed significantly to the higher OC. After neuroleptic treatment, reductions in the contribution of frontal electrodes to global OC in both bands correlated with the improvement of schizophrenia symptomatology. The present study suggests that frontal brain processes in schizophrenia were less coordinated with activity in the remaining brain. In addition, beneficial effects of neuroleptic treatment were accompanied by improvement of brain coordination predominantly due to changes in frontal regions. Our study provides new evidence of improper intrinsic brain integration in schizophrenia by investigating the resting-state gamma band activity.
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Affiliation(s)
- Mitsuru Kikuchi
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa 920-8641, Japan.
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Kikuchi M, Koenig T, Munesue T, Hanaoka A, Strik W, Dierks T, Koshino Y, Minabe Y. EEG microstate analysis in drug-naive patients with panic disorder. PLoS One 2011; 6:e22912. [PMID: 21829554 PMCID: PMC3146502 DOI: 10.1371/journal.pone.0022912] [Citation(s) in RCA: 72] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2011] [Accepted: 06/30/2011] [Indexed: 11/19/2022] Open
Abstract
Patients with panic disorder (PD) have a bias to respond to normal stimuli in a fearful way. This may be due to the preactivation of fear-associated networks prior to stimulus perception. Based on EEG, we investigated the difference between patients with PD and normal controls in resting state activity using features of transiently stable brain states (microstates). EEGs from 18 drug-naive patients and 18 healthy controls were analyzed. Microstate analysis showed that one class of microstates (with a right-anterior to left-posterior orientation of the mapped field) displayed longer durations and covered more of the total time in the patients than controls. Another microstate class (with a symmetric, anterior-posterior orientation) was observed less frequently in the patients compared to controls. The observation that selected microstate classes differ between patients with PD and controls suggests that specific brain functions are altered already during resting condition. The altered resting state may be the starting point of the observed dysfunctional processing of phobic stimuli.
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Affiliation(s)
- Mitsuru Kikuchi
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan.
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Abstract
The purpose of this review/opinion paper is to argue that human cognitive neuroscience has focused too little attention on how the brain may use time and time-based coding schemes to represent, process, and transfer information within and across brain regions. Instead, the majority of cognitive neuroscience studies rest on the assumption of functional localization. Although the functional localization approach has brought us a long way toward a basic characterization of brain functional organization, there are methodological and theoretical limitations of this approach. Further advances in our understanding of neurocognitive function may come from examining how the brain performs computations and forms transient functional neural networks using the rich multi-dimensional information available in time. This approach rests on the assumption that information is coded precisely in time but distributed in space; therefore, measures of rapid neuroelectrophysiological dynamics may provide insights into brain function that cannot be revealed using localization-based approaches and assumptions. Space is not an irrelevant dimension for brain organization; rather, a more complete understanding of how brain dynamics lead to behavior dynamics must incorporate how the brain uses time-based coding and processing schemes.
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Affiliation(s)
- Michael X Cohen
- Department of Psychology, University of Amsterdam Amsterdam, Netherlands
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Abstract
Ambiguous figures induce sudden transitions between rivaling percepts. We investigated electroencephalogram frequency modulations of accompanying change-related de- and rebinding processes. Presenting the stimuli discontinously, we synchronized perceptual reversals with stimulus onset, which served as a time reference for averaging. The resultant gain in temporal resolution revealed a sequence of time-frequency correlates of the reversal process. Most conspicuous was a transient right-hemispheric gamma modulation preceding endogenous reversals by at least 200 ms. No such modulation occurred with exogenously induced reversals of unambiguous stimulus variants. Post-onset components were delayed for ambiguous compared to unambiguous stimuli. The time course of oscillatory activity differed in several respects from predictions based on binding-related hypotheses. The gamma modulation preceding endogenous reversals may indicate an unstable brain state, ready to switch.
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Affiliation(s)
- Werner Ehm
- Institute for Frontier Areas of Psychology and Mental Health, Freiburg, Germany Universitäts-Augenklinik, Freiburg, Germany
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Wackermann J, Allefeld C. On the meaning and interpretation of global descriptors of brain electrical activity. Including a reply to X. Pei et al. Int J Psychophysiol 2007; 64:199-210. [PMID: 17368592 DOI: 10.1016/j.ijpsycho.2007.02.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2006] [Revised: 01/31/2007] [Accepted: 02/05/2007] [Indexed: 11/26/2022]
Abstract
Global descriptors of the brain's electrical activity, Sigma, Phi, and Omega, provide a comprehensive characterisation of brain functional states. Recently, Pei et al. [Pei, X., Zheng, C., Zhang, A., Duan, F., Bin, G., 2005. Discussion on "Towards a quantitative characterisation of functional states of the brain: from the nonlinear methodology to the global linear description" by J. Wackermann. Int. J. Psychophysiol. 56, 201-207] discussed the effects of signal power on the global measure of spatial complexity, Omega, and suggested a modification consisting in epoch-wise and channel-wise normalisation of input data to unit power. In the present paper, the basic principles of the global approach are reviewed, and the issues of Pei et al.'s approach are assessed. The original and the modified measures of spatial complexity are compared in two case studies. Numerical simulation shows that both methods veridically estimate small numbers of signal sources, but systematically underestimate as the number increases; the modified method yields a minor relative improvement. A study on real EEG data shows that the two measures sensibly differ only where artefactual inhomogeneities in channel variances affect the data; a combined procedure, consisting in record-wise equalisation of channel variances before Omega calculations, is suggested as the optimal strategy. Differences between the original objectives of the global methodology and the proposed modifications are pointed out and critically discussed.
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Affiliation(s)
- Jirí Wackermann
- Department of Empirical and Analytical Psychophysics, Institute for Frontier Areas of Psychology and Mental Health, Wilhelmstrasse 3a, D-79098 Freiburg i. Br., Germany
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Yoshimura M, Koenig T, Irisawa S, Isotani T, Yamada K, Kikuchi M, Okugawa G, Yagyu T, Kinoshita T, Strik W, Dierks T. A pharmaco-EEG study on antipsychotic drugs in healthy volunteers. Psychopharmacology (Berl) 2007; 191:995-1004. [PMID: 17333135 DOI: 10.1007/s00213-007-0737-8] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2006] [Accepted: 02/05/2007] [Indexed: 10/23/2022]
Abstract
RATIONALE Both psychotropic drugs and mental disorders have typical signatures in quantitative electroencephalography (EEG). Previous studies found that some psychotropic drugs had EEG effects opposite to the EEG effects of the mental disorders treated with these drugs (key-lock principle). OBJECTIVES We performed a placebo-controlled pharmaco-EEG study on two conventional antipsychotics (chlorpromazine and haloperidol) and four atypical antipsychotics (olanzapine, perospirone, quetiapine, and risperidone) in healthy volunteers. We investigated differences between conventional and atypical drug effects and whether the drug effects were compatible with the key-lock principle. METHODS Fourteen subjects underwent seven EEG recording sessions, one for each drug (dosage equivalent of 1 mg haloperidol). In a time-domain analysis, we quantified the EEG by identifying clusters of transiently stable EEG topographies (microstates). Frequency-domain analysis used absolute power across electrodes and the location of the center of gravity (centroid) of the spatial distribution of power in different frequency bands. RESULTS Perospirone increased duration of a microstate class typically shortened in schizophrenics. Haloperidol increased mean microstate duration of all classes, increased alpha 1 and beta 1 power, and tended to shift the beta 1 centroid posterior. Quetiapine decreased alpha 1 power and shifted the centroid anterior in both alpha bands. Olanzapine shifted the centroid anterior in alpha 2 and beta 1. CONCLUSIONS The increased microstate duration under perospirone and haloperidol was opposite to effects previously reported in schizophrenic patients, suggesting a key-lock mechanism. The opposite centroid changes induced by olanzapine and quetiapine compared to haloperidol might characterize the difference between conventional and atypical antipsychotics.
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Affiliation(s)
- Masafumi Yoshimura
- Department of Psychiatric Neurophysiology, University Hospital of Psychiatry, Bern, Switzerland.
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Koenig T, Studer D, Hubl D, Melie L, Strik WK. Brain connectivity at different time-scales measured with EEG. Philos Trans R Soc Lond B Biol Sci 2005; 360:1015-23. [PMID: 16087445 PMCID: PMC1854932 DOI: 10.1098/rstb.2005.1649] [Citation(s) in RCA: 106] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
We present an overview of different methods for decomposing a multichannel spontaneous electroencephalogram (EEG) into sets of temporal patterns and topographic distributions. All of the methods presented here consider the scalp electric field as the basic analysis entity in space. In time, the resolution of the methods is between milliseconds (time-domain analysis), subseconds (time- and frequency-domain analysis) and seconds (frequency-domain analysis). For any of these methods, we show that large parts of the data can be explained by a small number of topographic distributions. Physically, this implies that the brain regions that generated one of those topographies must have been active with a common phase. If several brain regions are producing EEG signals at the same time and frequency, they have a strong tendency to do this in a synchronized mode. This view is illustrated by several examples (including combined EEG and functional magnetic resonance imaging (fMRI)) and a selective review of the literature. The findings are discussed in terms of short-lasting binding between different brain regions through synchronized oscillations, which could constitute a mechanism to form transient, functional neurocognitive networks.
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Affiliation(s)
- T Koenig
- Department of Psychiatric Neurophysiology, University Hospital of Clinical Psychiatry Bern, Bolligenstr. 111, 3000 Bern 60, Switzerland.
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