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Li F, Zhang S, Jiang L, Duan K, Feng R, Zhang Y, Zhang G, Zhang Y, Li P, Yao D, Xie J, Xu W, Xu P. Recognition of autism spectrum disorder in children based on electroencephalogram network topology. Cogn Neurodyn 2024; 18:1033-1045. [PMID: 38826670 PMCID: PMC11143134 DOI: 10.1007/s11571-023-09962-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 02/24/2023] [Accepted: 03/17/2023] [Indexed: 06/04/2024] Open
Abstract
Although our knowledge of autism spectrum disorder (ASD) has been deepened, the accurate diagnosis of ASD from normal individuals is still left behind. In this study, we proposed to apply the spatial pattern of the network topology (SPN) to identify children with ASD from normal ones. Based on two independent batches of electroencephalogram datasets collected separately, the accurate recognition of ASD from normal children was achieved by applying the proposed SPN features. Since decreased long-range connectivity was identified for children with ASD, the SPN features extracted from the distinctive topological architecture between two groups in the first dataset were used to validate the capacity of SPN in classifying ASD, and the SPN features achieved the highest accuracy of 92.31%, which outperformed the other features e.g., power spectrum density (84.62%), network properties (76.92%), and sample entropy (73.08%). Moreover, within the second dataset, by using the model trained in the first dataset, the SPN also acquired the highest sensitivity in recognizing ASD, when compared to the other features. These results consistently illustrated that the functional brain network, especially the intrinsic spatial network topology, might be the potential biomarker for the diagnosis of ASD.
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Affiliation(s)
- Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- Research Unit of NeuroInformation, 2019RU035, Chinese Academy of Medical Sciences, Chengdu, China
| | - Shu Zhang
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Lin Jiang
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Keyi Duan
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Rui Feng
- Rainbow Biotechnology Co., Ltd., Chengdu, 610041 China
| | - Yingli Zhang
- Rainbow Biotechnology Co., Ltd., Chengdu, 610041 China
| | - Gao Zhang
- The Preston Robert Tisch Brain Tumor Center, Department of Neurosurgery, Department of Pathology, Duke University School of Medicine, Durham, NC 27710 USA
| | - Yangsong Zhang
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, 621010 China
| | - Peiyang Li
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, 400065 China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- Research Unit of NeuroInformation, 2019RU035, Chinese Academy of Medical Sciences, Chengdu, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001 China
| | - Jiang Xie
- Chengdu Third People’s Hospital, Affiliated Hospital of Southwest JiaoTong University Medical School, Chengdu, 610031 China
| | - Wenming Xu
- Department of Obstetrics/Gynecology, Joint Laboratory of Reproductive Medicine (SCU-CUHK), Key Laboratory of Obstetric, Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, 610041 China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731 China
- School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731 China
- Research Unit of NeuroInformation, 2019RU035, Chinese Academy of Medical Sciences, Chengdu, China
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, 610042 China
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2
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Xin X, Yu J, Gao X. The brain entropy dynamics in resting state. Front Neurosci 2024; 18:1352409. [PMID: 38595975 PMCID: PMC11002175 DOI: 10.3389/fnins.2024.1352409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/08/2024] [Indexed: 04/11/2024] Open
Abstract
As a novel measure for irregularity and complexity of the spontaneous fluctuations of brain activities, brain entropy (BEN) has attracted much attention in resting-state functional magnetic resonance imaging (rs-fMRI) studies during the last decade. Previous studies have shown its associations with cognitive and mental functions. While most previous research assumes BEN is approximately stationary during scan sessions, the brain, even at its resting state, is a highly dynamic system. Such dynamics could be characterized by a series of reoccurring whole-brain patterns related to cognitive and mental processes. The present study aims to explore the time-varying feature of BEN and its potential links with general cognitive ability. We adopted a sliding window approach to derive the dynamical brain entropy (dBEN) of the whole-brain functional networks from the HCP (Human Connectome Project) rs-fMRI dataset that includes 812 young healthy adults. The dBEN was further clustered into 4 reoccurring BEN states by the k-means clustering method. The fraction window (FW) and mean dwell time (MDT) of one BEN state, characterized by the extremely low overall BEN, were found to be negatively correlated with general cognitive abilities (i.e., cognitive flexibility, inhibitory control, and processing speed). Another BEN state, characterized by intermediate overall BEN and low within-state BEN located in DMN, ECN, and part of SAN, its FW, and MDT were positively correlated with the above cognitive abilities. The results of our study advance our understanding of the underlying mechanism of BEN dynamics and provide a potential framework for future investigations in clinical populations.
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Affiliation(s)
- Xiaoyang Xin
- Center for Psychological Sciences, Zhejiang University, Hangzhou, China
- Preschool College, Luoyang Normal University, Luoyang, China
| | - Jiaqian Yu
- Center for Psychological Sciences, Zhejiang University, Hangzhou, China
| | - Xiaoqing Gao
- Center for Psychological Sciences, Zhejiang University, Hangzhou, China
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3
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Chen C, Chen Z, Hu M, Zhou S, Xu S, Zhou G, Zhou J, Li Y, Chen B, Yao D, Li F, Liu Y, Su S, Xu P, Ma X. EEG brain network variability is correlated with other pathophysiological indicators of critical patients in neurology intensive care unit. Brain Res Bull 2024; 207:110881. [PMID: 38232779 DOI: 10.1016/j.brainresbull.2024.110881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 12/13/2023] [Accepted: 01/13/2024] [Indexed: 01/19/2024]
Abstract
Continuous electroencephalogram (cEEG) plays a crucial role in monitoring and postoperative evaluation of critical patients with extensive EEG abnormalities. Recently, the temporal variability of dynamic resting-state functional connectivity has emerged as a novel approach to understanding the pathophysiological mechanisms underlying diseases. However, little is known about the underlying temporal variability of functional connections in critical patients admitted to neurology intensive care unit (NICU). Furthermore, considering the emerging field of network physiology that emphasizes the integrated nature of human organisms, we hypothesize that this temporal variability in brain activity may be potentially linked to other physiological functions. Therefore, this study aimed to investigate network variability using fuzzy entropy in 24-hour dynamic resting-state networks of critical patients in NICU, with an emphasis on exploring spatial topology changes over time. Our findings revealed both atypical flexible and robust architectures in critical patients. Specifically, the former exhibited denser functional connectivity across the left frontal and left parietal lobes, while the latter showed predominantly short-range connections within anterior regions. These patterns of network variability deviating from normality may underlie the altered network integrity leading to loss of consciousness and cognitive impairment observed in these patients. Additionally, we explored changes in 24-hour network properties and found simultaneous decreases in brain efficiency, heart rate, and blood pressure between approximately 1 pm and 5 pm. Moreover, we observed a close relationship between temporal variability of resting-state network properties and other physiological indicators including heart rate as well as liver and kidney function. These findings suggest that the application of a temporal variability-based cEEG analysis method offers valuable insights into underlying pathophysiological mechanisms of critical patients in NICU, and may present novel avenues for their condition monitoring, intervention, and treatment.
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Affiliation(s)
- Chunli Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Zhaojin Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Meiling Hu
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, People's Republic of China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, People's Republic of China
| | - Sha Zhou
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, People's Republic of China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, People's Republic of China
| | - Shiyun Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Guan Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Jixuan Zhou
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Yuqin Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Baodan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Yizhou Liu
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, People's Republic of China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, People's Republic of China
| | - Simeng Su
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, People's Republic of China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, People's Republic of China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China; School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.
| | - Xuntai Ma
- Clinical Medical College of Chengdu Medical College, Chengdu 610500, People's Republic of China; The First Affiliated Hospital of Chengdu Medical College, Chengdu 610599, People's Republic of China.
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Ouyang G, Wang S, Liu M, Zhang M, Zhou C. Multilevel and multifaceted brain response features in spiking, ERP and ERD: experimental observation and simultaneous generation in a neuronal network model with excitation-inhibition balance. Cogn Neurodyn 2023; 17:1417-1431. [PMID: 37969943 PMCID: PMC10640466 DOI: 10.1007/s11571-022-09889-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 08/26/2022] [Accepted: 09/14/2022] [Indexed: 11/25/2022] Open
Abstract
Brain as a dynamic system responds to stimulations with specific patterns affected by its inherent ongoing dynamics. The patterns are manifested across different levels of organization-from spiking activity of neurons to collective oscillations in local field potential (LFP) and electroencephalogram (EEG). The multilevel and multifaceted response activities show patterns seemingly distinct and non-comparable from each other, but they should be coherently related because they are generated from the same underlying neural dynamic system. A coherent understanding of the interrelationships between different levels/aspects of activity features is important for understanding the complex brain functions. Here, based on analysis of data from human EEG, monkey LFP and neuronal spiking, we demonstrated that the brain response activities from different levels of neural system are highly coherent: the external stimulus simultaneously generated event-related potentials, event-related desynchronization, and variation in neuronal spiking activities that precisely match with each other in the temporal unfolding. Based on a biologically plausible but generic network of conductance-based integrate-and-fire excitatory and inhibitory neurons with dense connections, we showed that the multiple key features can be simultaneously produced at critical dynamical regimes supported by excitation-inhibition (E-I) balance. The elucidation of the inherent coherency of various neural response activities and demonstration of a simple dynamical neural circuit system having the ability to simultaneously produce multiple features suggest the plausibility of understanding high-level brain function and cognition from elementary and generic neuronal dynamics. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09889-w.
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Affiliation(s)
- Guang Ouyang
- Faculty of Education, The University of Hong Kong, Pok Fu Lam, Hong Kong China
| | - Shengjun Wang
- Department of Physics, Shaanxi Normal University, Xi’an, 710119 China
| | - Mianxin Liu
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong China
| | - Mingsha Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875 China
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong China
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5
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Si Y, Li P, Wang X, Yao G, Liu C, Liu Y, Zhang J, Zhang H, Luo Y. Cueing effect of attention among nurses with different anxiety levels: an EEG study. Med Biol Eng Comput 2023; 61:2269-2279. [PMID: 36988789 DOI: 10.1007/s11517-023-02829-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 03/08/2023] [Indexed: 03/30/2023]
Abstract
The attention to cueing among nurses with anxiety affects their nursing quality seriously. Nevertheless, the neural mechanism of attention under anxiety among nurses has not been revealed. In this study, we utilized the event-related potential (ERP) and functional brain networks to investigate the neural mechanism of the cueing attention differences between anxiety and non-anxiety nurse groups (AG-20 nurses; NAG-20 nurses) in the spatial cueing task. The results revealed that in the invalid cues (144 trials), longer reaction times, larger P2 amplitudes, and more linkages between the right frontal and parietal areas were found in AG compared to NAG. In the valid cues (288 trials), there were no significant behavioral and neural differences between the two groups. The AG in the invalid cues showed slower response times, larger P2 and N5 amplitudes, and denser linkages originating from the occipital cortex than those in the valid cues. The convolutional neural network was trained for discriminating between the anxiety nurses and the normal ones, with the average accuracy being 0.76. The findings provided a potential physiological biomarker to predict the anxiety group who need to give more psychological attention. Nurse leaders maybe get more information for offering solutions to retain mental health among nurses.
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Affiliation(s)
- Yajing Si
- Department of Psychology, Xinxiang Medical University, Xinxiang, Henan, China
- Xinxiang Municipal Key Laboratory of Psychopathology and Cognitive Neuroscience, Xinxiang, Henan, China
| | - Peiyang Li
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Xinge Wang
- Department of Nursing, Guangdong Provincial People's Hospital, Guangzhou, Guangdong, China
| | - Guiying Yao
- School of Nursing, Xinxiang Medical University, Xinxiang, Henan, China
| | - Congcong Liu
- Department of Psychology, Xinxiang Medical University, Xinxiang, Henan, China
| | - Yize Liu
- School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China
| | - Jiajia Zhang
- Department of Psychology, Xinxiang Medical University, Xinxiang, Henan, China
| | - Hongxing Zhang
- Department of Psychology, Xinxiang Medical University, Xinxiang, Henan, China.
- Xinxiang Municipal Key Laboratory of Psychopathology and Cognitive Neuroscience, Xinxiang, Henan, China.
| | - Yanyan Luo
- School of Nursing, Xinxiang Medical University, Xinxiang, Henan, China.
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Guo F, Li Y, Jian Z, Cui Y, Gong W, Li A, Jing W, Xu P, Chen K, Guo D, Yao D, Xia Y. Dose-related adaptive reconstruction of DMN in isoflurane administration: a study in the rat. BMC Anesthesiol 2023; 23:224. [PMID: 37380958 PMCID: PMC10303294 DOI: 10.1186/s12871-023-02153-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 05/26/2023] [Indexed: 06/30/2023] Open
Abstract
BACKGROUND The anesthetic states are accompanied by functional alterations. However, the dose-related adaptive alterations in the higher-order network under anesthesia, e. g. default mode network (DMN), are poorly revealed. METHODS We implanted electrodes in brain regions of the rat DMN to acquire local field potentials to investigate the perturbations produced by anesthesia. Relative power spectral density, static functional connectivity (FC), fuzzy entropy of dynamic FC, and topological features were computed from the data. RESULTS The results showed that adaptive reconstruction was induced by isoflurane, exhibiting reduced static and stable long-range FC, and altered topological features. These reconstruction patterns were in a dose-related fashion. CONCLUSION These results might impart insights into the neural network mechanisms underlying anesthesia and suggest the potential of monitoring the depth of anesthesia based on the parameters of DMN.
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Affiliation(s)
- Fengru Guo
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Yuqin Li
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Zhaoxin Jian
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Yan Cui
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Wenhui Gong
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Airui Li
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Wei Jing
- Department of Physiology, School of Basic Medicine and Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 4030030 China
| | - Peng Xu
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Ke Chen
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Daqing Guo
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Dezhong Yao
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
| | - Yang Xia
- Department of Neurosurgery, MOE Key Lab for Neuroinformation, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, 611731 China
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Zhang X, Lu B, Chen C, Yang L, Chen W, Yao D, Hou J, Qiu J, Li F, Xu P. The correlation between upper body grip strength and resting-state EEG network. Med Biol Eng Comput 2023:10.1007/s11517-023-02865-4. [PMID: 37338738 DOI: 10.1007/s11517-023-02865-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 06/07/2023] [Indexed: 06/21/2023]
Abstract
Current research in the field of neuroscience primarily focuses on the analysis of electroencephalogram (EEG) activities associated with movement within the central nervous system. However, there is a dearth of studies investigating the impact of prolonged individual strength training on the resting state of the brain. Therefore, it is crucial to examine the correlation between upper body grip strength and resting-state EEG networks. In this study, coherence analysis was utilized to construct resting-state EEG networks using the available datasets. A multiple linear regression model was established to examine the correlation between the brain network properties of individuals and their maximum voluntary contraction (MVC) during gripping tasks. The model was used to predict individual MVC. The beta and gamma frequency bands showed significant correlation between RSN connectivity and MVC (p < 0.05), particularly in left hemisphere frontoparietal and fronto-occipital connectivity. RSN properties were consistently correlated with MVC in both bands, with correlation coefficients greater than 0.60 (p < 0.01). Additionally, predicted MVC positively correlated with actual MVC, with a coefficient of 0.70 and root mean square error of 5.67 (p < 0.01). The results show that the resting-state EEG network is closely related to upper body grip strength, which can indirectly reflect an individual's muscle strength through the resting brain network.
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Affiliation(s)
- Xiabing Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bin Lu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Chunli Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Lei Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Wanjun Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 611731, China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Jingming Hou
- Department of Rehabilitation, Southwest Hospital, Army Medical University, Chongqing, 400038, China
| | - Jing Qiu
- Robotics Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China.
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 611731, China.
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China.
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 611731, China.
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, 610041, China.
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8
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Repetitive transcranial magnetic stimulation modulates long-range functional connectivity in autism spectrum disorder. J Psychiatr Res 2023; 160:187-194. [PMID: 36841084 DOI: 10.1016/j.jpsychires.2023.02.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 02/12/2023] [Accepted: 02/20/2023] [Indexed: 02/23/2023]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is growingly applied in autism spectrum disorder (ASD) due to its potential therapeutic value, however, its effects on functional network configuration and the mechanism underlying clinical improvement are still unclear. In this study, we examined the alternations of functional connectivity induced by rTMS using resting-state electroencephalogram (EEG) in children with ASD. Resting-state EEG was obtained from 24 children with ASD before and after rTMS intervention and from 24 age- and gender-matched typically developing (TD) children. The rTMS intervention course consisted of five 5-s trains at 15 Hz, with 10-min inter-train intervals, on the left parietal lobe each consecutive weekday for 3 weeks (15 sessions in total). Children with ASD showed significantly hypo-connected networks and sub-optimal network properties at both global and local levels, compared with TD peers. After rTMS intervention, long-range intra- and inter-hemispheric connections were significantly promoted, especially those within the alpha band. Meanwhile, network properties at both local and global levels were greatly promoted in the delta, theta, and alpha bands. Consistent with the changes in the network connectivities and properties, the core symptoms in ASD were also relieved measured by clinical scales after treatment. The findings of this study demonstrate that high-frequency rTMS over the parietal lobe is potentially an effective strategy to improve core symptoms by enhancing long-range connectivity reorganization in ASD.
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Zhu Y, Gong W, Lu X, Wang H. Effective connectivity analysis of brain networks of mathematically gifted adolescents using transfer entropy. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-223819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
Using functional neuroimaging, electrophysiological techniques and neural data processing techniques, neuroscientists have found that mathematically gifted adolescents exhibit unusual neurocognitive features in the activation of task-related brain regions. Hemispheric information interaction, functional reorganization of networks, and utilization of task-related brain regions are beneficial to rapid and efficient task processing. Based on Granger causality channel selection, the transfer entropy (TE) value between effective channels was computed, and the information flow patterns in the directed functional brain networks derived from electroencephalography (EEG) data during deductive reasoning tasks were explored. We evaluated the workspace configuration patterns of the brain network and the global integration characteristics of separated brain regions using node strength, motif, directed clustering coefficient and characteristic path length in the brain networks of mathematically gifted adolescents with effective connectivity. The empirical results demonstrated that a more integrated functional network at the global level and a more efficient clique at the local level support a pattern of workspace configuration in the mathematically gifted brain that is more conducive to task-related information processing.
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10
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Dorokhov VB, Runnova A, Tkachenko ON, Taranov AO, Arseniev GN, Kiselev A, Selskii A, Orlova A, Zhuravlev M. Analysis two types of K complexes on the human EEG based on classical continuous wavelet transform. CHAOS (WOODBURY, N.Y.) 2023; 33:031102. [PMID: 37003802 DOI: 10.1063/5.0143284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 02/20/2023] [Indexed: 06/19/2023]
Abstract
In our work, we compare EEG time-frequency features for two types of K-complexes detected in volunteers performing the monotonous psychomotor test with their eyes closed. Type I K-complexes preceded spontaneous awakenings, while after type II K-complexes, subjects continued to sleep at least for 10 s after. The total number of K-complexes in the group of 18 volunteers was 646, of which of which type I K-complexes was 150 and type II K-complexes was 496. Time-frequency analysis was performed using continuous wavelet transform. EEG wavelet spectral power was averaged upon several brain zones for each of the classical frequency ranges (slow wave, δ, θ, α, β1, β2, γ bands). The low-frequency oscillatory activity ( δ-band) preceding type I K-complexes was asymmetrical and most prominent in the left hemisphere. Statistically significant differences were obtained by averaging over the left and right hemispheres, as well as projections of the motor area of the brain, p<0.05. The maximal differences between the types I and II of K-complexes were demonstrated in δ-, θ-bands in the occipital and posterior temporal regions. The high amplitude of the motor cortex projection response in β2-band, [20;30] Hz, related to the sensory-motor modality of task in monotonous psychomotor test. The δ-oscillatory activity preceding type I K-complexes was asymmetrical and most prominent in the left hemisphere may be due to the important role of the left hemisphere in spontaneous awakening from sleep during monotonous work, which is an interesting issue for future research.
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Affiliation(s)
- V B Dorokhov
- Laboratory of Sleep/Wake Neurobiology, Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, 117865 Moscow, Russia
| | - A Runnova
- Center for Coordination of Fundamental Scientific Activities, National Medical Research Center for Therapy and Preventive Medicine, 101990 Moscow, Russia
| | - O N Tkachenko
- Laboratory of Sleep/Wake Neurobiology, Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, 117865 Moscow, Russia
| | - A O Taranov
- Laboratory of Sleep/Wake Neurobiology, Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, 117865 Moscow, Russia
| | - G N Arseniev
- Laboratory of Sleep/Wake Neurobiology, Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, 117865 Moscow, Russia
| | - A Kiselev
- Center for Coordination of Fundamental Scientific Activities, National Medical Research Center for Therapy and Preventive Medicine, 101990 Moscow, Russia
| | - A Selskii
- Institute of Physics, Saratov State University, 410012 Saratov, Russia
| | - A Orlova
- Center for Coordination of Fundamental Scientific Activities, National Medical Research Center for Therapy and Preventive Medicine, 101990 Moscow, Russia
| | - M Zhuravlev
- Center for Coordination of Fundamental Scientific Activities, National Medical Research Center for Therapy and Preventive Medicine, 101990 Moscow, Russia
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11
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Feng Y, Kang X, Wang H, Cong J, Zhuang W, Xue K, Li F, Yao D, Xu P, Zhang T. The relationships between dynamic resting-state networks and social behavior in autism spectrum disorder revealed by fuzzy entropy-based temporal variability analysis of large-scale network. Cereb Cortex 2023; 33:764-776. [PMID: 35297491 DOI: 10.1093/cercor/bhac100] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 02/13/2022] [Accepted: 02/14/2022] [Indexed: 02/03/2023] Open
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by a core deficit in social processes. However, it is still unclear whether the core clinical symptoms of the disorder can be reflected by the temporal variability of resting-state network functional connectivity (FC). In this article, we examined the large-scale network FC temporal variability at the local region, within-network, and between-network levels using the fuzzy entropy technique. Then, we correlated the network FC temporal variability to social-related scores. We found that the social behavior correlated with the FC temporal variability of the precuneus, parietal, occipital, temporal, and precentral. Our results also showed that social behavior was significantly negatively correlated with the temporal variability of FC within the default mode network, between the frontoparietal network and cingulo-opercular task control network, and the dorsal attention network. In contrast, social behavior correlated significantly positively with the temporal variability of FC within the subcortical network. Finally, using temporal variability as a feature, we construct a model to predict the social score of ASD. These findings suggest that the network FC temporal variability has a close relationship with social behavioral inflexibility in ASD and may serve as a potential biomarker for predicting ASD symptom severity.
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Affiliation(s)
- Yu Feng
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Xiaodong Kang
- The Department of Sichuan 81 Rehabilitation Center, Chengdu University of TCM, No.37, Twelfth Bridge Road,Chengdu 610075, China
| | - Hesong Wang
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Institute of Gastroenterology of Guangdong Province, Nanfang Hospital, Southern Medical University, No. 1023-1063, Shatai South Road, Baiyun District, Guangzhou 510515, China
| | - Jing Cong
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Wenwen Zhuang
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Kaiqing Xue
- School of Computer and Software Engineering, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
| | - Tao Zhang
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
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12
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Si Y, Liu C, Kou Y, Dong Z, Zhang J, Wang J, Lu C, Luo Y, Ni T, Du Y, Zhang H. Antipsychotics-induced improvement of cool executive function in individuals living with schizophrenia. Front Psychiatry 2023; 14:1154011. [PMID: 37181875 PMCID: PMC10172485 DOI: 10.3389/fpsyt.2023.1154011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 04/07/2023] [Indexed: 05/16/2023] Open
Abstract
Cool executive dysfunction is a crucial feature in people living with schizophrenia which is related to cognition impairment and the severity of the clinical symptoms. Based on electroencephalogram (EEG), our current study explored the change of brain network under the cool executive tasks in individuals living with schizophrenia before and after atypical antipsychotic treatment (before_TR vs. after_TR). 21 patients with schizophrenia and 24 healthy controls completed the cool executive tasks, involving the Tower of Hanoi Task (THT) and Trail-Marking Test A-B (TMT A-B). The results of this study uncovered that the reaction time of the after_TR group was much shorter than that of the before_TR group in the TMT-A and TMT-B. And the after_TR group showed fewer error numbers in the TMT-B than those of the before_TR group. Concerning the functional network, stronger DMN-like linkages were found in the before_TR group compared to the control group. Finally, we adopted a multiple linear regression model based on the change network properties to predict the patient's PANSS change ratio. Together, the findings deepened our understanding of cool executive function in individuals living with schizophrenia and might provide physiological information to reliably predict the clinical efficacy of schizophrenia after atypical antipsychotic treatment.
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Affiliation(s)
- Yajing Si
- School of Psychology, Xinxiang Medical University, Xinxiang, Henan, China
- Xinxiang Key Lab for Psychopathology and Cognitive Neuroscience, Xinxiang, Henan, China
| | - Congcong Liu
- School of Psychology, Xinxiang Medical University, Xinxiang, Henan, China
| | - Yanna Kou
- Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
| | - Zhao Dong
- Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
- Zhumadian Second People's Hospital, Zhumadian, Henan, China
| | - Jiajia Zhang
- School of Psychology, Xinxiang Medical University, Xinxiang, Henan, China
| | - Juan Wang
- Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
| | - Chengbiao Lu
- Henan International Key Laboratory for Non-invasive Neuromodulation, Xinxiang, Henan, China
| | - Yanyan Luo
- School of Nursing, Xinxiang Medical University, Xinxiang, Henan, China
| | - Tianjun Ni
- School of Basic Medical Sciences, Xinxiang Medical University, Xinxiang, Henan, China
| | - Yunhong Du
- Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
| | - Hongxing Zhang
- School of Psychology, Xinxiang Medical University, Xinxiang, Henan, China
- Xinxiang Key Lab for Psychopathology and Cognitive Neuroscience, Xinxiang, Henan, China
- Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
- Henan International Key Laboratory for Non-invasive Neuromodulation, Xinxiang, Henan, China
- *Correspondence: Hongxing Zhang,
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13
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Jiang L, He R, Li Y, Yi C, Peng Y, Yao D, Wang Y, Li F, Xu P, Yang Y. Predicting the long-term after-effects of rTMS in autism spectrum disorder using temporal variability analysis of scalp EEG. J Neural Eng 2022; 19. [PMID: 36223728 DOI: 10.1088/1741-2552/ac999d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 10/12/2022] [Indexed: 12/24/2022]
Abstract
Objective.Repetitive transcranial magnetic stimulation (rTMS) emerges as a useful therapy for autism spectrum disorder (ASD) clinically. Whereas the mechanisms of action of rTMS on ASD are not fully understood, and no biomarkers until now are available to reliably predict the follow-up rTMS efficacy in clinical practice.Approach.In the current work, the temporal variability was investigated in resting-state electroencephalogram of ASD patients, and the nonlinear complexity of related time-varying networks was accordingly evaluated by fuzzy entropy.Main results.The results showed the hyper-variability in the resting-state networks of ASD patients, while three week rTMS treatment alleviates the hyper fluctuations occurring in the frontal-parietal and frontal-occipital connectivity and further contributes to the ameliorative ASD symptoms. In addition, the changes in variability network properties are closely correlated with clinical scores, which further serve as potential predictors to reliably track the long-term rTMS efficacy for ASD.Significance.The findings consistently demonstrated that the temporal variability of time-varying networks of ASD patients could be modulated by rTMS, and related variability properties also help predict follow-up rTMS efficacy, which provides the potential for formulating individualized treatment strategies for ASD (ChiCTR2000033586).
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Affiliation(s)
- Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Runyang He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Yuqin Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Chanlin Yi
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Yueheng Peng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, People's Republic of China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, People's Republic of China
| | - Yuping Wang
- Department of Neurology, Xuanwu Hospital Capital Medical University, Beijing, People's Republic of China.,Beijing Key Laboratory of Neuromodulation, Beijing, People's Republic of China.,Center of Epilepsy, Beijing Institute for Brain Disorders, Capital Medical University, Beijing, People's Republic of China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, People's Republic of China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.,Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035 Chengdu, People's Republic of China.,Radiation Oncology Key Laboratory of Sichuan Province, 610041 Chengdu, People's Republic of China
| | - Yingxue Yang
- Department of Neurology, Xuanwu Hospital Capital Medical University, Beijing, People's Republic of China.,Beijing Key Laboratory of Neuromodulation, Beijing, People's Republic of China.,Center of Epilepsy, Beijing Institute for Brain Disorders, Capital Medical University, Beijing, People's Republic of China
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14
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Pei C, Qiu Y, Li F, Huang X, Si Y, Li Y, Zhang X, Chen C, Liu Q, Cao Z, Ding N, Gao S, Alho K, Yao D, Xu P. The different brain areas occupied for integrating information of hierarchical linguistic units: a study based on EEG and TMS. Cereb Cortex 2022; 33:4740-4751. [PMID: 36178127 DOI: 10.1093/cercor/bhac376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 11/13/2022] Open
Abstract
Human language units are hierarchical, and reading acquisition involves integrating multisensory information (typically from auditory and visual modalities) to access meaning. However, it is unclear how the brain processes and integrates language information at different linguistic units (words, phrases, and sentences) provided simultaneously in auditory and visual modalities. To address the issue, we presented participants with sequences of short Chinese sentences through auditory, visual, or combined audio-visual modalities while electroencephalographic responses were recorded. With a frequency tagging approach, we analyzed the neural representations of basic linguistic units (i.e. characters/monosyllabic words) and higher-level linguistic structures (i.e. phrases and sentences) across the 3 modalities separately. We found that audio-visual integration occurs in all linguistic units, and the brain areas involved in the integration varied across different linguistic levels. In particular, the integration of sentences activated the local left prefrontal area. Therefore, we used continuous theta-burst stimulation to verify that the left prefrontal cortex plays a vital role in the audio-visual integration of sentence information. Our findings suggest the advantage of bimodal language comprehension at hierarchical stages in language-related information processing and provide evidence for the causal role of the left prefrontal regions in processing information of audio-visual sentences.
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Affiliation(s)
- Changfu Pei
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.,School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Yuan Qiu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.,School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.,School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.,Research Unit of Neuroscience, Chinese Academy of Medical Science, 2019RU035, Chengdu, China
| | - Xunan Huang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.,School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.,School of Foreign Languages, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China
| | - Yajing Si
- School of Psychology, Xinxiang Medical University, Xinxiang, 453003, China
| | - Yuqin Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.,School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Xiabing Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.,School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Chunli Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.,School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Qiang Liu
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, Sichuan, 610066, China
| | - Zehong Cao
- STEM, Mawson Lakes Campus, University of South Australia, Adelaide, SA 5095, Australia
| | - Nai Ding
- College of Biomedical Engineering and Instrument Sciences, Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310007, China
| | - Shan Gao
- School of Foreign Languages, University of Electronic Science and Technology of China, Chengdu, Sichuan, 611731, China
| | - Kimmo Alho
- Department of Psychology and Logopedics, University of Helsinki, Helsinki, FI 00014, Finland
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.,School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.,Research Unit of Neuroscience, Chinese Academy of Medical Science, 2019RU035, Chengdu, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 611731, China.,School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.,Research Unit of Neuroscience, Chinese Academy of Medical Science, 2019RU035, Chengdu, China.,Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, 610041, China
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15
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Liang T, Hong L, Xiao J, Wei L, Liu X, Wang H, Dong B, Liu X. Directed network analysis reveals changes in cortical and muscular connectivity caused by different standing balance tasks. J Neural Eng 2022; 19. [PMID: 35767971 DOI: 10.1088/1741-2552/ac7d0c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 06/29/2022] [Indexed: 11/12/2022]
Abstract
Objective.Standing balance forms the basis of daily activities that require the integration of multi-sensory information and coordination of multi-muscle activation. Previous studies have confirmed that the cortex is directly involved in balance control, but little is known about the neural mechanisms of cortical integration and muscle coordination in maintaining standing balance.Approach.We used a direct directed transfer function (dDTF) to analyze the changes in the cortex and muscle connections of healthy subjects (15 subjects: 13 male and 2 female) corresponding to different standing balance tasks.Main results.The results show that the topology of the EEG brain network and muscle network changes significantly as the difficulty of the balancing tasks increases. For muscle networks, the connection analysis shows that the connection of antagonistic muscle pairs plays a major role in the task. For EEG brain networks, graph theory-based analysis shows that the clustering coefficient increases significantly, and the characteristic path length decreases significantly with increasing task difficulty. We also found that cortex-to-muscle connections increased with the difficulty of the task and were significantly stronger than the muscle-to-cortex connections.Significance.These results show that changes in the difficulty of balancing tasks alter EEG brain networks and muscle networks, and an analysis based on the directed network can provide rich information for exploring the neural mechanisms of balance control.
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Affiliation(s)
- Tie Liang
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China.,Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, People's Republic of China
| | - Lei Hong
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China
| | - Jinzhuang Xiao
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China
| | - Lixin Wei
- Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, People's Republic of China
| | - Xiaoguang Liu
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China
| | - Hongrui Wang
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China.,Institute of Electric Engineering, Yanshan University, Qinhuangdao, Hebei 066004, People's Republic of China
| | - Bin Dong
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China.,Development Planning Office, Affiliated Hospital of Hebei University, Baoding 071002, People's Republic of China
| | - Xiuling Liu
- Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Baoding 071002, People's Republic of China
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16
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Yi C, Qiu Y, Chen W, Chen C, Wang Y, Li P, Yang L, Zhang X, Jiang L, Yao D, Li F, Xu P. Constructing Time-varying Directed EEG network by Multivariate Nonparametric Dynamical Granger Causality. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1412-1421. [PMID: 35576427 DOI: 10.1109/tnsre.2022.3175483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Time-varying directed electroencephalography (EEG) network is the potential tool for studying the dynamical causality among brain areas at a millisecond level; which conduces to understanding how our brain effectively adapts to information processing, giving inspiration to causality- and brain-inspired machine learning. Currently, its construction still mainly relies on the parametric approach such as multivariate adaptive autoregressive (MVAAR), represented by the most widely used adaptive directed transfer function (ADTF). Restricted by the model assumption, the corresponding performance largely depends on the MVAAR modeling which usually encounters difficulty in fitting complex spectral features. In this study, we proposed to construct EEG directed network with multivariate nonparametric dynamical Granger causality (mndGC) method that infers the causality of a network, instead, in a data-driven way directly and therefore avoids the trap in the model-dependent parametric approach. Comparisons between mndGC and ADTF were conducted both with simulation and real data application. Simulation study demonstrated the superiority of mndGC both in noise resistance and capturing the instantaneous directed network changes. When applying to the real motor imagery (MI) data set, distinguishable network characters between left- and right-hand MI during different MI stages were better revealed by mndGC. Our study extends the nonparametric causality exploration and provides practical suggestions for the time-varying directed EEG network analysis.
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17
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Jiang L, Li F, Chen B, Yi C, Peng Y, Zhang T, Yao D, Xu P. The task-dependent modular covariance networks unveiled by multiple-way fusion-based analysis. Int J Neural Syst 2022; 32:2250035. [PMID: 35719086 DOI: 10.1142/s0129065722500356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China
| | - Baodan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Chanlin Yi
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Yueheng Peng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
| | - Tao Zhang
- School of Science, Xihua University, Chengdu 610039, P. R. China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China
- School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, P. R. China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
- School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, P. R. China
- Radiation Oncology Key Laboratory of Sichuan Province, Chengdu 610041, P. R. China
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18
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Wang Z, Zhang F, Yue L, Hu L, Li X, Xu B, Liang Z. Cortical Complexity and Connectivity during Isoflurane-induced General Anesthesia: A Rat Study. J Neural Eng 2022; 19. [PMID: 35472693 DOI: 10.1088/1741-2552/ac6a7b] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 04/25/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE The investigation of neurophysiologic mechanisms of anesthetic drug-induced loss of consciousness (LOC) by using the entropy, complexity, and information integration theories at the mesoscopic level has been a hot topic in recent years. However, systematic research is still lacking. APPROACH We analyzed electrocorticography (ECoG) data recorded from nine rats during isoflurane-induced unconsciousness. To characterize the complexity and connectivity changes, we investigated ECoG power, symbolic dynamic-based entropy (i.e., permutation entropy (PE)), complexity (i.e., permutation Lempel-Ziv complexity (PLZC)), information integration (i.e., permutation cross mutual information (PCMI)), and PCMI-based cortical brain networks in the frontal, parietal, and occipital cortical regions. MAIN RESULTS Firstly, LOC was accompanied by a raised power in the ECoG beta (12-30 Hz) but a decreased power in the high gamma (55-95 Hz) frequency band in all three brain regions. Secondly, PE and PLZC showed similar change trends in the lower frequency band (0.1-45 Hz), declining after LOC (p<0.05) and increasing after recovery of consciousness (p<0.001). Thirdly, intra-frontal and inter-frontal-parietal PCMI declined after LOC, in both lower (0.1-45Hz) and higher frequency bands (55-95Hz) (p<0.001). Finally, the local network parameters of the nodal clustering coefficient and nodal efficiency in the frontal region decreased after LOC, in both the lower and higher frequency bands (p<0.05). Moreover, global network parameters of the normalized average clustering coefficient and small world index increased slightly after LOC in the lower frequency band. However, this increase was not statistically significant. SIGNIFICANCE The PE, PLZC, PCMI and PCMI-based brain networks are effective metrics for qualifying the effects of isoflurane.
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Affiliation(s)
- Zhijie Wang
- Yanshan University, Yanshan University, Qinhuangdao 066004, China., Qinhuangdao, 066004, CHINA
| | - Fengrui Zhang
- Department of Psychology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing 100049, China., Beijing, 100049, CHINA
| | - Lupeng Yue
- Department of Psychology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing 100049, China., Beijing, 100049, CHINA
| | - Li Hu
- Department of Psychology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing 100049, China, Beijing, 100049, CHINA
| | - Xiaoli Li
- Department of Psychology, Beijing Normal University, Beijing Normal University, Beijing 100875, China., Beijing, Beijing, 100875, CHINA
| | - Bo Xu
- PLA General Hospital of Southern Theatre Command, Guangzhou 510010, China., Guangzhou, Guangdong, 510010, CHINA
| | - Zhenhu Liang
- Institute of Electrical Engineering, Yanshan University, Yanshan University, Qinhuangdao 066004, China., Qinhuangdao, 066004, CHINA
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19
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Jiang L, Wang J, Dai J, Li F, Chen B, He R, Liao Y, Yao D, Dong W, Xu P. Altered temporal variability in brain functional connectivity identified by fuzzy entropy underlines schizophrenia deficits. J Psychiatr Res 2022; 148:315-324. [PMID: 35193035 DOI: 10.1016/j.jpsychires.2022.02.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 01/13/2022] [Accepted: 02/14/2022] [Indexed: 11/18/2022]
Abstract
Investigation of the temporal variability of resting-state brain networks informs our understanding of how neural connectivity aggregates and disassociates over time, further shedding light on the aberrant neural interactions that underlie symptomatology and psychosis development. In the current work, an electroencephalogram-based sliding window analysis was utilized for the first time to measure the nonlinear complexity of dynamic resting-state brain networks of schizophrenia (SZ) patients by applying fuzzy entropy. The results of this study demonstrated the attenuated temporal variability among multiple electrodes that were distributed in the frontal and right parietal lobes for SZ patients when compared with healthy controls (HCs). Meanwhile, a concomitant strengthening of the posterior and peripheral flexible connections that may be attributed to the excessive alertness or sensitivity of SZ patients to the external environment was also revealed. These temporal fluctuation distortions combined reflect an abnormality in the coordination of functional network switching in SZ, which is further the source of worse task performance (i.e., P300 amplitude) and the negative relationship between individual complexity metrics and P300 amplitude. Notably, when using the network metrics as features, multiple linear regressions of P300 amplitudes were also exactly achieved for both the SZ and HC groups. These findings shed light on the pathophysiological mechanisms of SZ from a temporal variability perspective and provide potential biomarkers for quantifying SZ's progressive neurophysiological deterioration.
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Affiliation(s)
- Lin Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Jiuju Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Jing Dai
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; Chengdu Mental Health Center, Chengdu, 610036, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China.
| | - Baodan Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Runyang He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Yuanyuan Liao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China; School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Wentian Dong
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, 611731, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China.
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