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Qi X, Zhang X, Shen H, Su J, Gao X, Li Y, Yang H, Gao C, Ni W, Lei Y, Gu Y, Mao Y, Yu Y. Switching of brain networks across different cerebral perfusion states: insights from EEG dynamic microstate analyses. Cereb Cortex 2024; 34:bhae035. [PMID: 38342687 DOI: 10.1093/cercor/bhae035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/16/2024] [Accepted: 01/17/2024] [Indexed: 02/13/2024] Open
Abstract
The alteration of neural interactions across different cerebral perfusion states remains unclear. This study aimed to fulfill this gap by examining the longitudinal brain dynamic information interactions before and after cerebral reperfusion. Electroencephalogram in eyes-closed state at baseline and postoperative 7-d and 3-month follow-ups (moyamoya disease: 20, health controls: 23) were recorded. Dynamic network analyses were focused on the features and networks of electroencephalogram microstates across different microstates and perfusion states. Considering the microstate features, the parameters were disturbed of microstate B, C, and D but preserved of microstate A. The transition probabilities of microstates A-B and B-D were increased to play a complementary role across different perfusion states. Moreover, the microstate variability was decreased, but was significantly improved after cerebral reperfusion. Regarding microstate networks, the functional connectivity strengths were declined, mainly within frontal, parietal, and occipital lobes and between parietal and occipital lobes in different perfusion states, but were ameliorated after cerebral reperfusion. This study elucidates how dynamic interaction patterns of brain neurons change after cerebral reperfusion, which allows for the observation of brain network transitions across various perfusion states in a live clinical setting through direct intervention.
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Affiliation(s)
- Xiaoying Qi
- Department of Physiology, School of Medicine, Yan'an University, Yan'an 716000, China
- School of Life Science and Human Phenome Institute, Research Institute of Intelligent Complex Systems and Institute of Science and Technology for Brain-Inspired Intelligence Fudan University, Shanghai 200433, China
| | - Xin Zhang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Hao Shen
- School of Life Science and Human Phenome Institute, Research Institute of Intelligent Complex Systems and Institute of Science and Technology for Brain-Inspired Intelligence Fudan University, Shanghai 200433, China
| | - Jiabin Su
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Xinjie Gao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Yanjiang Li
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Heng Yang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Chao Gao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Wei Ni
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Yu Lei
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Yuxiang Gu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai 200040, China
- National Center for Neurological Disorders, Fudan University, Shanghai 200433, China
| | - Yuguo Yu
- School of Life Science and Human Phenome Institute, Research Institute of Intelligent Complex Systems and Institute of Science and Technology for Brain-Inspired Intelligence Fudan University, Shanghai 200433, China
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Gil Ávila C, Bott FS, Tiemann L, Hohn VD, May ES, Nickel MM, Zebhauser PT, Gross J, Ploner M. DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience. Sci Data 2023; 10:613. [PMID: 37696851 PMCID: PMC10495446 DOI: 10.1038/s41597-023-02525-0] [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: 05/19/2023] [Accepted: 08/31/2023] [Indexed: 09/13/2023] Open
Abstract
Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing, and analysis of EEG data is time-consuming and researcher-dependent. Therefore, we developed DISCOVER-EEG, an open and fully automated pipeline that enables easy and fast preprocessing, analysis, and visualization of resting state EEG data. Data in the Brain Imaging Data Structure (BIDS) standard are automatically preprocessed, and physiologically meaningful features of brain function (including oscillatory power, connectivity, and network characteristics) are extracted and visualized using two open-source and widely used Matlab toolboxes (EEGLAB and FieldTrip). We tested the pipeline in two large, openly available datasets containing EEG recordings of healthy participants and patients with a psychiatric condition. Additionally, we performed an exploratory analysis that could inspire the development of biomarkers for healthy aging. Thus, the DISCOVER-EEG pipeline facilitates the aggregation, reuse, and analysis of large EEG datasets, promoting open and reproducible research on brain function.
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Affiliation(s)
- Cristina Gil Ávila
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität München, München, Germany
| | - Felix S Bott
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Laura Tiemann
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Vanessa D Hohn
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Elisabeth S May
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Moritz M Nickel
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Paul Theo Zebhauser
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany
- Center for Interdisciplinary Pain Medicine, TUM School of Medicine, Technical University of Munich, München, Germany
| | - Joachim Gross
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Markus Ploner
- Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany.
- TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany.
- Center for Interdisciplinary Pain Medicine, TUM School of Medicine, Technical University of Munich, München, Germany.
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Hao Z, Zhai X, Cheng D, Pan Y, Dou W. EEG Microstate-Specific Functional Connectivity and Stroke-Related Alterations in Brain Dynamics. Front Neurosci 2022; 16:848737. [PMID: 35645720 PMCID: PMC9131012 DOI: 10.3389/fnins.2022.848737] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 04/08/2022] [Indexed: 11/13/2022] Open
Abstract
The brain, as a complex dynamically distributed information processing system, involves the coordination of large-scale brain networks such as neural synchronization and fast brain state transitions, even at rest. However, the neural mechanisms underlying brain states and the impact of dysfunction following brain injury on brain dynamics remain poorly understood. To this end, we proposed a microstate-based method to explore the functional connectivity pattern associated with each microstate class. We capitalized on microstate features from eyes-closed resting-state EEG data to investigate whether microstate dynamics differ between subacute stroke patients (N = 31) and healthy populations (N = 23) and further examined the correlations between microstate features and behaviors. An important finding in this study was that each microstate class was associated with a distinct functional connectivity pattern, and it was highly consistent across different groups (including an independent dataset). Although the connectivity patterns were diminished in stroke patients, the skeleton of the patterns was retained to some extent. Nevertheless, stroke patients showed significant differences in most parameters of microstates A, B, and C compared to healthy controls. Notably, microstate C exhibited an opposite pattern of differences to microstates A and B. On the other hand, there were no significant differences in all microstate parameters for patients with left-sided vs. right-sided stroke, as well as patients before vs. after lower limb training. Moreover, support vector machine (SVM) models were developed using only microstate features and achieved moderate discrimination between patients and controls. Furthermore, significant negative correlations were observed between the microstate-wise functional connectivity and lower limb motor scores. Overall, these results suggest that the changes in microstate dynamics for stroke patients appear to be state-selective, compensatory, and related to brain dysfunction after stroke and subsequent functional reconfiguration. These findings offer new insights into understanding the neural mechanisms of microstates, uncovering stroke-related alterations in brain dynamics, and exploring new treatments for stroke patients.
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Affiliation(s)
- Zexuan Hao
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China
| | - Xiaoxue Zhai
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China
| | - Dandan Cheng
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China
| | - Yu Pan
- Department of Rehabilitation Medicine, School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China
| | - Weibei Dou
- Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University, Beijing, China
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Qin Y, Liu X, Guo X, Liu M, Li H, Xu S. Low-Frequency Repetitive Transcranial Magnetic Stimulation Restores Dynamic Functional Connectivity in Subcortical Stroke. Front Neurol 2021; 12:771034. [PMID: 34950102 PMCID: PMC8689061 DOI: 10.3389/fneur.2021.771034] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 10/27/2021] [Indexed: 01/09/2023] Open
Abstract
Background and Purpose: Strokes consistently result in brain network dysfunction. Previous studies have focused on the resting-state characteristics over the study period, while dynamic recombination remains largely unknown. Thus, we explored differences in dynamics between brain networks in patients who experienced subcortical stroke and the effects of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) on dynamic functional connectivity (dFC). Methods: A total of 41 patients with subcortical stroke were randomly divided into the LF-rTMS (n = 23) and the sham stimulation groups (n = 18). Resting-state functional MRI data were collected before (1 month after stroke) and after (3 months after stroke) treatment; a total of 20 age- and sex-matched healthy controls were also included. An independent component analysis, sliding window approach, and k-means clustering were used to identify different functional networks, estimate dFC matrices, and analyze dFC states before treatment. We further assessed the effect of LF-rTMS on dFCs in patients with subcortical stroke. Results: Compared to healthy controls, patients with stroke spent significantly more time in state I [p = 0.043, effect size (ES) = 0.64] and exhibited shortened stay in state II (p = 0.015, ES = 0.78); the dwell time gradually returned to normal after LF-rTMS treatment (p = 0.015, ES = 0.55). Changes in dwell time before and after LF-rTMS treatment were positively correlated with changes in the Fugl-Meyer Assessment for Upper Extremity (pr = 0.48, p = 0.028). Moreover, patients with stroke had decreased dFCs between the sensorimotor and cognitive control domains, yet connectivity within the cognitive control network increased. These abnormalities were partially improved after LF-rTMS treatment. Conclusion: Abnormal changes were noted in temporal and spatial characteristics of sensorimotor domains and cognitive control domains of patients who experience subcortical stroke; LF-rTMS can promote the partial recovery of dFC. These findings offer new insight into the dynamic neural mechanisms underlying effect of functional recombination and rTMS in subcortical stroke. Registration: http://www.chictr.org.cn/index.aspx, Unique.identifier: ChiCTR1800019452.
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Affiliation(s)
- Yin Qin
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China
- Department of Rehabilitation Medicine, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, China
| | - Xiaoying Liu
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China
- Department of Rehabilitation Medicine, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, China
| | - Xiaoping Guo
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China
- Department of Rehabilitation Medicine, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, China
| | - Minhua Liu
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China
- Department of Rehabilitation Medicine, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, China
| | - Hui Li
- Department of Radiology, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, China
| | - Shangwen Xu
- Department of Radiology, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, China
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Rao B, Xu D, Zhao C, Wang S, Li X, Sun W, Gang Y, Fang J, Xu H. Development of functional connectivity within and among the resting-state networks in anesthetized rhesus monkeys. Neuroimage 2021; 242:118473. [PMID: 34390876 DOI: 10.1016/j.neuroimage.2021.118473] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 08/08/2021] [Accepted: 08/11/2021] [Indexed: 01/22/2023] Open
Abstract
OBJECTIVE The age-related changes in the resting-state networks (RSNs) exhibited temporally specific patterns in humans, and humans and rhesus monkeys have similar RSNs. We hypothesized that the RSNs in rhesus monkeys experienced similar developmental patterns as humans. METHODS We acquired resting-state fMRI data from 62 rhesus monkeys, which were divided into childhood, adolescence, and early adulthood groups. Group independent component analysis (ICA) was used to identify monkey RSNs. We detected the between-group differences in the RSNs and static, dynamic, and effective functional network connections (FNCs) using one-way variance analysis (ANOVA) and post-hoc analysis. RESULTS Eight rhesus RSNs were identified, including cerebellum (CN), left and right lateral visual (LVN and RVN), posterior default mode (pDMN), visuospatial (VSN), frontal (FN), salience (SN), and sensorimotor networks (SMN). In internal connections, the CN, SN, FN, and SMN mainly matured in early adulthood. The static FNCs associated with FN, SN, pDMN primarily experienced fast descending slow ascending type (U-shaped) developmental patterns for maturation, and the dynamic FNCs related to pDMN (RVN, CN, and SMN) and SMN (CN) were mature in early adulthood. The effective FNC results showed that the pDMN and VSN (stimulated), SN (inhibited), and FN (first inhibited then stimulated) chiefly matured in early adulthood. CONCLUSION We identified eight monkey RSNs, which exhibited similar development patterns as humans. All the RSNs and FNCs in monkeys were not widely changed but fine-tuned. Our study clarified that the progressive synchronization, exploration, and regulation of cognitive RSNs within the pDMN, FN, SN, and VSN denoted potential maturation of the RSNs throughout development. We confirmed the development patterns of RSNs and FNCs would support the use of monkeys as a best animal model for human brain function.
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Affiliation(s)
- Bo Rao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuchang District, Wuhan, Hubei 430071, China.
| | - Dan Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuchang District, Wuhan, Hubei 430071, China.
| | - Chaoyang Zhao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuchang District, Wuhan, Hubei 430071, China.
| | - Shouchao Wang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuchang District, Wuhan, Hubei 430071, China
| | - Xuan Li
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuchang District, Wuhan, Hubei 430071, China
| | - Wenbo Sun
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuchang District, Wuhan, Hubei 430071, China
| | - Yadong Gang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuchang District, Wuhan, Hubei 430071, China
| | - Jian Fang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuchang District, Wuhan, Hubei 430071, China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuchang District, Wuhan, Hubei 430071, China.
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