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Wang Y, Shu Y, Cai G, Guo Y, Gao J, Chen Y, Lv L, Zeng X. Altered static and dynamic functional network connectivity in primary angle-closure glaucoma patients. Sci Rep 2024; 14:11682. [PMID: 38778225 PMCID: PMC11111766 DOI: 10.1038/s41598-024-62635-6] [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: 11/23/2023] [Accepted: 05/20/2024] [Indexed: 05/25/2024] Open
Abstract
To explore altered patterns of static and dynamic functional brain network connectivity (sFNC and dFNC) in Primary angle-closure glaucoma (PACG) patients. Clinically confirmed 34 PACG patients and 33 age- and gender-matched healthy controls (HCs) underwent evaluation using T1 anatomical and functional MRI on a 3 T scanner. Independent component analysis, sliding window, and the K-means clustering method were employed to investigate the functional network connectivity (FNC) and temporal metrics based on eight resting-state networks. Differences in FNC and temporal metrics were identified and subsequently correlated with clinical variables. For sFNC, compared with HCs, PACG patients showed three decreased interactions, including SMN-AN, SMN-VN and VN-AN pairs. For dFNC, we derived four highly structured states of FC that occurred repeatedly between individual scans and subjects, and the results are highly congruent with sFNC. In addition, PACG patients had a decreased fraction of time in state 3 and negatively correlated with IOP (p < 0.05). PACG patients exhibit abnormalities in both sFNC and dFNC. The high degree of overlap between static and dynamic results suggests the stability of functional connectivity networks in PACG patients, which provide a new perspective to understand the neuropathological mechanisms of optic nerve damage in PACG patients.
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Affiliation(s)
- Yuanyuan Wang
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yongqiang Shu
- Positron Emission Tomography (PET) Center, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Guoqian Cai
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yu Guo
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Junwei Gao
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Ye Chen
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Lianjiang Lv
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xianjun Zeng
- Department of Radiology, The First Affiliated Hospital of Nanchang University, Nanchang, China.
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2
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Yang Y, Luo S, Wang W, Gao X, Yao X, Wu T. From bench to bedside: Overview of magnetoencephalography in basic principle, signal processing, source localization and clinical applications. Neuroimage Clin 2024; 42:103608. [PMID: 38653131 PMCID: PMC11059345 DOI: 10.1016/j.nicl.2024.103608] [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: 11/22/2023] [Revised: 04/14/2024] [Accepted: 04/16/2024] [Indexed: 04/25/2024]
Abstract
Magnetoencephalography (MEG) is a non-invasive technique that can precisely capture the dynamic spatiotemporal patterns of the brain by measuring the magnetic fields arising from neuronal activity along the order of milliseconds. Observations of brain dynamics have been used in cognitive neuroscience, the diagnosis of neurological diseases, and the brain-computer interface (BCI). In this study, we outline the basic principle, signal processing, and source localization of MEG, and describe its clinical applications for cognitive assessment, the diagnoses of neurological diseases and mental disorders, preoperative evaluation, and the BCI. This review not only provides an overall perspective of MEG, ranging from practical techniques to clinical applications, but also enhances the prevalent understanding of neural mechanisms. The use of MEG is expected to lead to significant breakthroughs in neuroscience.
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Affiliation(s)
- Yanling Yang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China; College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Shichang Luo
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China; College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Wenjie Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China; College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Xiumin Gao
- School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Xufeng Yao
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, China.
| | - Tao Wu
- College of Medical Imaging, Jiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, China
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3
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Chen Z, Cai Y, Xiao L, Wei XE, Liu Y, Lin C, Liu D, Liu H, Rong L. Increased functional connectivity between default mode network and visual network potentially correlates with duration of residual dizziness in patients with benign paroxysmal positional vertigo. Front Neurol 2024; 15:1363869. [PMID: 38500812 PMCID: PMC10944895 DOI: 10.3389/fneur.2024.1363869] [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: 12/31/2023] [Accepted: 02/23/2024] [Indexed: 03/20/2024] Open
Abstract
Objective To assess changes in static and dynamic functional network connectivity (sFNC and dFNC) and explore their correlations with clinical features in benign paroxysmal positional vertigo (BPPV) patients with residual dizziness (RD) after successful canalith repositioning maneuvers (CRM) using resting-state fMRI. Methods We studied resting-state fMRI data from 39 BPPV patients with RD compared to 38 BPPV patients without RD after successful CRM. Independent component analysis and methods of sliding window and k-means clustering were adopted to investigate the changes in dFNC and sFNC between the two groups. Additionally, temporal features and meta-states were compared between the two groups. Furthermore, the associations between fMRI results and clinical characteristics were analyzed using Pearson's partial correlation analysis. Results Compared with BPPV patients without RD, patients with RD had longer duration of BPPV and higher scores of dizziness handicap inventory (DHI) before successful CRM. BPPV patients with RD displayed no obvious abnormal sFNC compared to patients without RD. In the dFNC analysis, patients with RD showed increased FNC between default mode network (DMN) and visual network (VN) in state 4, the FNC between DMN and VN was positively correlated with the duration of RD. Furthermore, we found increased mean dwell time (MDT) and fractional windows (FW) in state 1 but decreased MDT and FW in state 3 in BPPV patients with RD. The FW of state 1 was positively correlated with DHI score before CRM, the MDT and FW of state 3 were negatively correlated with the duration of BPPV before CRM in patients with RD. Additionally, compared with patients without RD, patients with RD showed decreased number of states and state span. Conclusion The occurrence of RD might be associated with increased FNC between DMN and VN, and the increased FNC between DMN and VN might potentially correlate with the duration of RD symptoms. In addition, we found BPPV patients with RD showed altered global meta-states and temporal features. These findings are helpful for us to better understand the underlying neural mechanisms of RD and potentially contribute to intervention development for BPPV patients with RD.
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Affiliation(s)
- Zhengwei Chen
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yaxian Cai
- Department of Neurology, General Hospital of the Yangtze River Shipping, Wuhan, Hubei, China
| | - Lijie Xiao
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Xiu-E Wei
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Yueji Liu
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Cunxin Lin
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- Graduate School of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Dan Liu
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
- Graduate School of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Haiyan Liu
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
| | - Liangqun Rong
- Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China
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Hu Z, Zhou C, He L. Abnormal dynamic functional network connectivity in patients with early-onset bipolar disorder. Front Psychiatry 2023; 14:1169488. [PMID: 37448493 PMCID: PMC10338119 DOI: 10.3389/fpsyt.2023.1169488] [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: 02/19/2023] [Accepted: 06/12/2023] [Indexed: 07/15/2023] Open
Abstract
Objective To explore the changes in dynamic functional brain network connectivity (dFNC) in patients with early-onset bipolar disorder (BD). Methods Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 39 patients with early-onset BD and 22 healthy controls (HCs). Four repeated and stable dFNC states were characterised by independent component analysis (ICA), sliding time windows and k-means clustering, and three dFNC temporal metrics (fraction of time, mean dwell time and number of transitions) were obtained. The dFNC temporal metrics and the differences in dFNC between the two groups in different states were evaluated, and the correlations between the differential dFNC metrics and neuropsychological scores were analysed. Results The dFNC analysis showed four connected patterns in all subjects. Compared with the HCs, the dFNC patterns of early-onset BD were significantly altered in all four states, mainly involving impaired cognitive and perceptual networks. In addition, early-onset BD patients had a decreased fraction of time and mean dwell time in state 2 and an increased mean dwell time in state 3 (p < 0.05). The mean dwell time in state 3 of BD showed a positive correlation trend with the HAMA score (r = 0.4049, p = 0.0237 × 3 > 0.05 after Bonferroni correction). Conclusion Patients with early-onset BD had abnormal dynamic properties of brain functional network connectivity, suggesting that their dFNC was unstable, mainly manifesting as impaired coordination between cognitive and perceptual networks. This study provided a new imaging basis for the neuropathological study of emotional and cognitive deficits in early-onset BD.
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Affiliation(s)
- Ziyi Hu
- Department of Radiology, First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Chun Zhou
- Department of Radiology, First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Laichang He
- Department of Radiology, First Affiliated Hospital of Nanchang University, Nanchang, China
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Candelaria-Cook FT, Schendel ME, Flynn L, Cerros C, Hill DE, Stephen JM. Disrupted dynamic functional network connectivity in fetal alcohol spectrum disorders. ALCOHOL, CLINICAL & EXPERIMENTAL RESEARCH 2023; 47:687-703. [PMID: 36880528 PMCID: PMC10281251 DOI: 10.1111/acer.15046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/30/2023] [Accepted: 02/23/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND Prenatal alcohol exposure (PAE) can result in harmful and long-lasting neurodevelopmental changes. Children with PAE or a fetal alcohol spectrum disorder (FASD) have decreased white matter volume and resting-state spectral power compared to typically developing controls (TDC) and impaired resting-state static functional connectivity. The impact of PAE on resting-state dynamic functional network connectivity (dFNC) is unknown. METHODS Using eyes-closed and eyes-open magnetoencephalography (MEG) resting-state data, global dFNC statistics and meta-states were examined in 89 children aged 6-16 years (51 TDC, 38 with FASD). Source analyzed MEG data were used as input to group spatial independent component analysis to derive functional networks from which the dFNC was calculated. RESULTS During eyes-closed, relative to TDC, participants with FASD spent a significantly longer time in state 2, typified by anticorrelation (i.e., decreased connectivity) within and between default mode network (DMN) and visual network (VN), and state 4, typified by stronger internetwork correlation. The FASD group exhibited greater dynamic fluidity and dynamic range (i.e., entered more states, changed from one meta-state to another more often, and traveled greater distances) than TDC. During eyes-open, TDC spent significantly more time in state 1, typified by positive intra- and interdomain connectivity with modest correlation within the frontal network (FN), while participants with FASD spent a larger fraction of time in state 2, typified by anticorrelation within and between DMN and VN and strong correlation within and between FN, attention network, and sensorimotor network. CONCLUSIONS There are important resting-state dFNC differences between children with FASD and TDC. Participants with FASD exhibited greater dynamic fluidity and dynamic range and spent more time in states typified by anticorrelation within and between DMN and VN, and more time in a state typified by high internetwork connectivity. Taken together, these network aberrations indicate that prenatal alcohol exposure has a global effect on resting-state connectivity.
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Affiliation(s)
| | - Megan E. Schendel
- The Mind Research Network and Lovelace Biomedical Research Institute, Albuquerque, New Mexico, USA
| | - Lucinda Flynn
- The Mind Research Network and Lovelace Biomedical Research Institute, Albuquerque, New Mexico, USA
| | - Cassandra Cerros
- Department of Pediatrics, University of New Mexico Health Sciences Center, Albuquerque, NM
| | - Dina E. Hill
- Department of Psychiatry and Behavioral Sciences, University of New Mexico Health Sciences Center, Albuquerque, NM
| | - Julia M. Stephen
- The Mind Research Network and Lovelace Biomedical Research Institute, Albuquerque, New Mexico, USA
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Tagawa M, Takei Y, Kato Y, Suto T, Hironaga N, Ohki T, Takahashi Y, Fujihara K, Sakurai N, Ujita K, Tsushima Y, Fukuda M. Disrupted local beta band networks in schizophrenia revealed through graph analysis: A magnetoencephalography study. Psychiatry Clin Neurosci 2022; 76:309-320. [PMID: 35397141 DOI: 10.1111/pcn.13362] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 03/14/2022] [Accepted: 03/25/2022] [Indexed: 11/30/2022]
Abstract
AIMS Schizophrenia (SZ) is characterized by psychotic symptoms and cognitive impairment, and is hypothesized to be a 'dysconnection' syndrome due to abnormal neural network formation. Although numerous studies have helped elucidate the pathophysiology of SZ, many aspects of the mechanism underlying psychotic symptoms remain unknown. This study used graph theory analysis to evaluate the characteristics of the resting-state network (RSN) in terms of microscale and macroscale indices, and to identify candidates as potential biomarkers of SZ. Specifically, we discriminated topological characteristics in the frequency domain and investigated them in the context of psychotic symptoms in patients with SZ. METHODS We performed graph theory analysis of electrophysiological RSN data using magnetoencephalography to compare topological characteristics represented by microscale (degree centrality and clustering coefficient) and macroscale (global efficiency, local efficiency, and small-worldness) indices in 29 patients with SZ and 38 healthy controls. In addition, we investigated the aberrant topological characteristics of the RSN in patients with SZ and their relationship with SZ symptoms. RESULTS SZ was associated with a decreased clustering coefficient, local efficiency, and small-worldness, especially in the high beta band. In addition, macroscale changes in the low beta band are closely associated with negative symptoms. CONCLUSIONS The local networks of patients with SZ may disintegrate at both the microscale and macroscale levels, mainly in the beta band. Adopting an electrophysiological perspective of SZ as a failure to form local networks in the beta band will provide deeper insights into the pathophysiology of SZ as a 'dysconnection' syndrome.
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Affiliation(s)
- Minami Tagawa
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan.,Gunma Prefectural Psychiatric Medical Center, Gunma, Japan
| | - Yuichi Takei
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan
| | - Yutaka Kato
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan.,Tsutsuji Mental Hospital, Gunma, Japan
| | - Tomohiro Suto
- Gunma Prefectural Psychiatric Medical Center, Gunma, Japan
| | - Naruhito Hironaga
- Brain Center, Faculty of Medicine, Kyushu University, Fukuoka, Japan
| | - Takefumi Ohki
- International Research Center for Neurointelligence (IRCN), The University of Tokyo, Tokyo, Japan
| | - Yumiko Takahashi
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan
| | - Kazuyuki Fujihara
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan.,Department of Genetic and Behavioral Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan
| | - Noriko Sakurai
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan
| | - Koichi Ujita
- Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Gunma, Japan
| | - Yoshito Tsushima
- Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Gunma, Japan
| | - Masato Fukuda
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan
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Liu L, Ren J, Li Z, Yang C. A review of MEG dynamic brain network research. Proc Inst Mech Eng H 2022; 236:763-774. [PMID: 35465768 DOI: 10.1177/09544119221092503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The dynamic description of neural networks has attracted the attention of researchers for dynamic networks may carry more information compared with resting-state networks. As a non-invasive electrophysiological data with high temporal and spatial resolution, magnetoencephalogram (MEG) can provide rich information for the analysis of dynamic functional brain networks. In this review, the development of MEG brain network was summarized. Several analysis methods such as sliding window, Hidden Markov model, and time-frequency based methods used in MEG dynamic brain network studies were discussed. Finally, the current research about multi-modal brain network analysis and their applications with MEG neurophysiology, which are prospected to be one of the research directions in the future, were concluded.
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Affiliation(s)
- Lu Liu
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Jiechuan Ren
- Department of Internal Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhimei Li
- Department of Internal Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chunlan Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
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Bai D, Yao W, Wang S, Wang J. Multiscale Weighted Permutation Entropy Analysis of Schizophrenia Magnetoencephalograms. ENTROPY 2022; 24:e24030314. [PMID: 35327825 PMCID: PMC8946927 DOI: 10.3390/e24030314] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 02/19/2022] [Accepted: 02/20/2022] [Indexed: 12/27/2022]
Abstract
Schizophrenia is a neuropsychiatric disease that affects the nonlinear dynamics of brain activity. The primary objective of this study was to explore the complexity of magnetoencephalograms (MEG) in patients with schizophrenia. We combined a multiscale method and weighted permutation entropy to characterize MEG signals from 19 schizophrenia patients and 16 healthy controls. When the scale was larger than 42, the MEG signals of schizophrenia patients were significantly more complex than those of healthy controls (p<0.004). The difference in complexity between patients with schizophrenia and the controls was strongest in the frontal and occipital areas (p<0.001), and there was almost no difference in the central area. In addition, the results showed that the dynamic range of MEG complexity is wider in healthy individuals than in people with schizophrenia. Overall, the multiscale weighted permutation entropy method reliably quantified the complexity of MEG from schizophrenia patients, contributing to the development of potential magnetoencephalographic biomarkers for schizophrenia.
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Affiliation(s)
- Dengxuan Bai
- School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
| | - Wenpo Yao
- Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
- Correspondence: (W.Y.); (J.W.)
| | - Shuwang Wang
- School of Electronic Information, Nanjing Vocational College of Information Technolog, Nanjing 210023, China;
| | - Jun Wang
- Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
- Correspondence: (W.Y.); (J.W.)
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9
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Bai D, Yao W, Lv Z, Yan W, Wang J. Multiscale multidimensional recurrence quantitative analysis for analysing MEG signals in patients with schizophrenia. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102586] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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10
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Kim JA, Davis KD. Magnetoencephalography: physics, techniques, and applications in the basic and clinical neurosciences. J Neurophysiol 2021; 125:938-956. [PMID: 33567968 DOI: 10.1152/jn.00530.2020] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Magnetoencephalography (MEG) is a technique used to measure the magnetic fields generated from neuronal activity in the brain. MEG has a high temporal resolution on the order of milliseconds and provides a more direct measure of brain activity when compared with hemodynamic-based neuroimaging methods such as magnetic resonance imaging and positron emission tomography. The current review focuses on basic features of MEG such as the instrumentation and the physics that are integral to the signals that can be measured, and the principles of source localization techniques, particularly the physics of beamforming and the techniques that are used to localize the signal of interest. In addition, we review several metrics that can be used to assess functional coupling in MEG and describe the advantages and disadvantages of each approach. Lastly, we discuss the current and future applications of MEG.
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Affiliation(s)
- Junseok A Kim
- Division of Brain, Imaging and Behaviour, Krembil Brain Institute, Krembil Research Institute, University Health Network, Toronto, Ontario, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Karen D Davis
- Division of Brain, Imaging and Behaviour, Krembil Brain Institute, Krembil Research Institute, University Health Network, Toronto, Ontario, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,Department of Surgery, University of Toronto, Toronto, Ontario, Canada
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11
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Nguchu BA, Zhao J, Wang Y, Li Y, Wei Y, Uwisengeyimana JDD, Wang X, Qiu B, Li H. Atypical Resting-State Functional Connectivity Dynamics Correlate With Early Cognitive Dysfunction in HIV Infection. Front Neurol 2021; 11:606592. [PMID: 33519683 PMCID: PMC7841016 DOI: 10.3389/fneur.2020.606592] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 12/01/2020] [Indexed: 01/20/2023] Open
Abstract
Purpose: Previous studies have shown that HIV affects striato-cortical regions, leading to persisting cognitive impairment in 30-70% of the infected individuals despite combination antiretroviral therapy. This study aimed to investigate brain functional dynamics whose deficits might link to early cognitive decline or immunologic deterioration. Methods: We applied sliding windows and K-means clustering to fMRI data (HIV patients with asymptomatic neurocognitive impairment and controls) to construct dynamic resting-state functional connectivity (RSFC) maps and identify states of their reoccurrences. The average and variability of dynamic RSFC, and the dwelling time and state transitioning of each state were evaluated. Results: HIV patients demonstrated greater variability in RSFC between the left pallidum and regions of right pre-central and post-central gyri, and between the right supramarginal gyrus and regions of the right putamen and left pallidum. Greater variability was also found in the frontal RSFC of pars orbitalis of the left inferior frontal gyrus and right superior frontal gyrus (medial). While deficits in learning and memory recall of HIV patients related to greater striato-sensorimotor variability, deficits in attention and working memory were associated with greater frontal variability. Greater striato-parietal variability presented a strong link with immunologic function (CD4+/CD8+ ratio). Furthermore, HIV-infected patients exhibited longer time and reduced transitioning in states typified by weaker connectivity in specific networks. CD4+T-cell counts of the HIV-patients were related to reduced state transitioning. Conclusion: Our findings suggest that HIV alters brain functional connectivity dynamics, which may underlie early cognitive impairment. These findings provide novel insights into our understanding of HIV pathology, complementing the existing knowledge.
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Affiliation(s)
- Benedictor Alexander Nguchu
- Hefei National Laboratory for Physical Sciences at the Microscale, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Jing Zhao
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Yanming Wang
- Hefei National Laboratory for Physical Sciences at the Microscale, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Yu Li
- Hefei National Laboratory for Physical Sciences at the Microscale, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Yarui Wei
- Hefei National Laboratory for Physical Sciences at the Microscale, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Jean de Dieu Uwisengeyimana
- Hefei National Laboratory for Physical Sciences at the Microscale, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Xiaoxiao Wang
- Hefei National Laboratory for Physical Sciences at the Microscale, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Bensheng Qiu
- Hefei National Laboratory for Physical Sciences at the Microscale, Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, China
| | - Hongjun Li
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, China
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12
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Candelaria-Cook FT, Stephen JM. Test-Retest Reliability of Magnetoencephalography Resting-State Functional Connectivity in Schizophrenia. Front Psychiatry 2020; 11:551952. [PMID: 33391043 PMCID: PMC7772354 DOI: 10.3389/fpsyt.2020.551952] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 11/23/2020] [Indexed: 12/17/2022] Open
Abstract
The reliability of magnetoencephalography (MEG) resting-state functional connectivity in schizophrenia (SZ) is unknown as previous research has focused on healthy controls (HC). Here, we examined reliability in 26 participants (13-SZ, 13-HC). Eyes opened and eyes closed resting-state data were collected on 4 separate occasions during 2 visits, 1 week apart. For source modeling, we used minimum norm software to apply dynamic statistical parametric mapping. Source analyses compared the following functional connectivity metrics from each data run: coherence (coh), imaginary coherence (imcoh), pairwise phase consistency (ppc), phase-locking value (plv), phase lag index (pli), weighted phase lag index (wpli), and weighted phase lag index debiased (wpli2). Intraclass correlation coefficients (ICCs) were calculated for whole brain, network, and network pair averages. For reliability, ICCs above 0.75 = excellent, above 0.60 = good, above 0.40 = fair, and below 0.40 = poor reliability. We found the reliability of these metrics varied greatly depending on frequency band, network, network pair, and participant group examined. Broadband (1-58 Hz) whole brain averages in both HC and SZ showed excellent reliability for wpli2, and good to fair reliability for ppc, plv, and coh. Broadband network averages showed excellent to good reliability across 1 hour and 1 week for coh, imcoh, ppc, plv, wpli within default mode, cognitive control, and visual networks in HC, while the same metrics had excellent to fair reliability in SZ. Regional network pair averages showed good to fair reliability for coh, ppc, plv within default mode, cognitive control and visual network pairs in HC and SZ. In general, HC had higher reliability compared to SZ, and the default mode, cognitive control, and visual networks had higher reliability compared to somatosensory and auditory networks. Similar reliability levels occurred for both eyes opened and eyes closed resting-states for most metrics. The functional connectivity metrics of coh, ppc, and plv performed best across 1 hour and 1 week in HC and SZ. We also found that SZ had reduced coh, plv, and ppc in the dmn average and pair values indicating dysconnectivity in SZ. These findings encourage collecting both eyes opened and eyes closed resting-state MEG, while demonstrating that clinical populations may differ in reliability.
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Weber S, Johnsen E, Kroken RA, Løberg EM, Kandilarova S, Stoyanov D, Kompus K, Hugdahl K. Dynamic Functional Connectivity Patterns in Schizophrenia and the Relationship With Hallucinations. Front Psychiatry 2020; 11:227. [PMID: 32300313 PMCID: PMC7145395 DOI: 10.3389/fpsyt.2020.00227] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 03/10/2020] [Indexed: 11/13/2022] Open
Abstract
There is a wealth of evidence showing aberrant functional connectivity (FC) in schizophrenia but with considerable variability in findings across studies. Dynamic FC is an extension of traditional static FC, in that such analyses allow for explorations of temporal changes in connectivity. Thereby they also provide more detailed information on connectivity abnormalities in psychiatric disorders such as schizophrenia. The current study investigated dynamic FC in a sample of 80 schizophrenia patients and 80 matched healthy control subjects, replicating previous findings of aberrant dwell times in specific FC states, and further supporting a role for default mode network (DMN) dysfunction. Furthermore, relationships with hallucinations, a core symptom of schizophrenia, were explored. Two measures of hallucinations were used, one measure of current hallucination severity assessed on the day of scanning, and one trait-measure where hallucinations were assessed repeatedly over the course of 1 year. Current hallucination severity did not show a significant relationship with dynamic FC. However, the trait-measure of hallucination proneness over 1 year showed a significant relationship with dynamic FC. Patients with high hallucination proneness spent less time in connectivity states characterized by strong anti-correlation between the DMN and task-positive networks. The findings support theoretical models of hallucinations which have proposed an instability of the DMN and impaired cognitive control in patients with hallucinations. Furthermore, the results point to hallucination proneness as a potential marker for identifying distinct subgroups of schizophrenia patients.
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Affiliation(s)
- Sarah Weber
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.,Division of Psychiatry and NORMENT Centre of Excellence, Haukeland University Hospital, Bergen, Norway
| | - Erik Johnsen
- Division of Psychiatry and NORMENT Centre of Excellence, Haukeland University Hospital, Bergen, Norway.,Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Rune A Kroken
- Division of Psychiatry and NORMENT Centre of Excellence, Haukeland University Hospital, Bergen, Norway.,Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Else-Marie Løberg
- Division of Psychiatry and NORMENT Centre of Excellence, Haukeland University Hospital, Bergen, Norway.,Department of Addiction Medicine, Haukeland University Hospital, Bergen, Norway.,Department of Clinical Psychology, University of Bergen, Bergen, Norway
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology, Medical University of Plovdiv, Plovdiv, Bulgaria.,Research Institute, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology, Medical University of Plovdiv, Plovdiv, Bulgaria.,Research Institute, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Kristiina Kompus
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.,Division of Psychiatry and NORMENT Centre of Excellence, Haukeland University Hospital, Bergen, Norway.,Institute of Psychology, University of Tartu, Tartu, Estonia
| | - Kenneth Hugdahl
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.,Division of Psychiatry and NORMENT Centre of Excellence, Haukeland University Hospital, Bergen, Norway.,Department of Radiology, Haukeland University Hospital, Bergen, Norway
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Magnetic Source Imaging and Infant MEG: Current Trends and Technical Advances. Brain Sci 2019; 9:brainsci9080181. [PMID: 31357668 PMCID: PMC6721320 DOI: 10.3390/brainsci9080181] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2019] [Revised: 07/23/2019] [Accepted: 07/26/2019] [Indexed: 12/25/2022] Open
Abstract
Magnetoencephalography (MEG) is known for its temporal precision and good spatial resolution in cognitive brain research. Nonetheless, it is still rarely used in developmental research, and its role in developmental cognitive neuroscience is not adequately addressed. The current review focuses on the source analysis of MEG measurement and its potential to answer critical questions on neural activation origins and patterns underlying infants’ early cognitive experience. The advantages of MEG source localization are discussed in comparison with functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS), two leading imaging tools for studying cognition across age. Challenges of the current MEG experimental protocols are highlighted, including measurement and data processing, which could potentially be resolved by developing and improving both software and hardware. A selection of infant MEG research in auditory, speech, vision, motor, sleep, cross-modality, and clinical application is then summarized and discussed with a focus on the source localization analyses. Based on the literature review and the advancements of the infant MEG systems and source analysis software, typical practices of infant MEG data collection and analysis are summarized as the basis for future developmental cognitive research.
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