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Lee DA, Lee HJ, Park KM. Structural connectivity as a predictive factor for perampanel response in patients with epilepsy. Seizure 2024; 118:125-131. [PMID: 38701705 DOI: 10.1016/j.seizure.2024.04.026] [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: 01/09/2024] [Revised: 04/05/2024] [Accepted: 04/28/2024] [Indexed: 05/05/2024] Open
Abstract
OBJECTIVES This study aimed to identify clinical characteristics that could predict the response to perampanel (PER) and determine whether structural connectivity is a predictive factor. METHODS We enrolled patients with epilepsy who received PER and were followed-up for a minimum of 12 months. Good PER responders, who were seizure-free or presented with more than 50 % seizure reduction, were classified separately from poor PER responders who had seizure reduction of less than 50 % or non-responders. A graph theoretical analysis was conducted based on diffusion tensor imaging to calculate network measures of structural connectivity among patients with epilepsy. RESULTS 106 patients with epilepsy were enrolled, including 26 good PER responders and 80 poor PER responders. Good PER responders used fewer anti-seizure medications before PER administration compared to those by poor PER responders (3 vs. 4; p = 0.042). Early PER treatment was more common in good PER responders than poor PER responders (46.2 vs. 21.3 %, p = 0.014). Regarding cortical structural connectivity, the global efficiency was higher and characteristic path length was lower in good PER responders than in poor PER responders (0.647 vs. 0.635, p = 0.006; 1.726 vs. 1,759, p = 0.008, respectively). For subcortical structural connectivity, the mean clustering coefficient and small-worldness index were higher in good PER responders than in poor PER responders (0.821 vs. 0.791, p = 0.009; 0.597 vs. 0.560, p = 0.009, respectively). CONCLUSION This study demonstrated that early PER administration can predict a good PER response in patients with epilepsy, and structural connectivity could potentially offer clinical utility in predicting PER response.
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Affiliation(s)
- Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Ho-Joon Lee
- Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea.
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2
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Stroh A, Schweiger S, Ramirez JM, Tüscher O. The selfish network: how the brain preserves behavioral function through shifts in neuronal network state. Trends Neurosci 2024; 47:246-258. [PMID: 38485625 DOI: 10.1016/j.tins.2024.02.005] [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: 08/19/2023] [Revised: 01/31/2024] [Accepted: 02/19/2024] [Indexed: 04/12/2024]
Abstract
Neuronal networks possess the ability to regulate their activity states in response to disruptions. How and when neuronal networks turn from physiological into pathological states, leading to the manifestation of neuropsychiatric disorders, remains largely unknown. Here, we propose that neuronal networks intrinsically maintain network stability even at the cost of neuronal loss. Despite the new stable state being potentially maladaptive, neural networks may not reverse back to states associated with better long-term outcomes. These maladaptive states are often associated with hyperactive neurons, marking the starting point for activity-dependent neurodegeneration. Transitions between network states may occur rapidly, and in discrete steps rather than continuously, particularly in neurodegenerative disorders. The self-stabilizing, metastable, and noncontinuous characteristics of these network states can be mathematically described as attractors. Maladaptive attractors may represent a distinct pathophysiological entity that could serve as a target for new therapies and for fostering resilience.
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Affiliation(s)
- Albrecht Stroh
- Leibniz Institute for Resilience Research, Mainz, Germany; Institute of Pathophysiology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
| | - Susann Schweiger
- Leibniz Institute for Resilience Research, Mainz, Germany; Institute of Human Genetics, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany; Institute of Molecular Biology (IMB), Mainz, Germany
| | - Jan-Marino Ramirez
- Center for Integrative Brain Research at the Seattle Children's Research Institute, University of Washington, Seattle, USA
| | - Oliver Tüscher
- Leibniz Institute for Resilience Research, Mainz, Germany; Institute of Molecular Biology (IMB), Mainz, Germany; Department of Psychiatry and Psychotherapy, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
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3
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Li Y, Ran Y, Yao M, Chen Q. Altered static and dynamic functional connectivity of the default mode network across epilepsy subtypes in children: A resting-state fMRI study. Neurobiol Dis 2024; 192:106425. [PMID: 38296113 DOI: 10.1016/j.nbd.2024.106425] [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: 10/28/2023] [Revised: 01/08/2024] [Accepted: 01/27/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Epilepsy is a chronic neurologic disorder characterized by abnormal functioning of brain networks, making it a complex research topic. Recent advancements in neuroimaging technology offer an effective approach to unraveling the intricacies of the human brain. Within different types of epilepsy, there is growing recognition regarding ongoing changes in the default mode network (DMN). However, little is known about the shared and distinct alterations of static functional connectivity (sFC) and dynamic functional connectivity (dFC) in DMN among epileptic subtypes, especially in children with epilepsy. METHODS Here, 110 children with epilepsy at a single center, including idiopathic generalized epilepsy (IGE), frontal lobe epilepsy (FLE), temporal lobe epilepsy (TLE), and parietal lobe epilepsy (PLE), as well as 84 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (fMRI) scan. We investigated both sFC and dFC between groups of the DMN. RESULTS Decreased static and dynamic connectivity within the DMN subsystem were shared by all subtypes. In each epilepsy subtype, children with epilepsy displayed significant and distinct patterns of DMN connectivity compared to the control group: the IGE group showed reduced interhemispheric connectivity, the FLE group consistently demonstrated disturbances in frontal region connectivity, the TLE group exhibited significant disruptions in hippocampal connectivity, and the PLE group displayed a notable decrease in parietal-temporal connectivity within the DMN. Some state-specific FC disruptions (decreased dFC) were observed in each epilepsy subtype that cannot detect by sFC. To determine their uniqueness within specific subtypes, bootstrapping methods were employed and found the significant results (IGE: between PCC and bilateral precuneus, FLE: between right middle frontal gyrus and bilateral middle temporal gyrus, TLE: between left Hippocampus and right fusiform, PLE: between left angular and cingulate cortex). Furthermore, only children with IGE exhibited dynamic features associated with clinical variables. CONCLUSIONS Our findings highlight both shared and distinct FC alterations within the DMN in children with different types of epilepsy. Furthermore, our work provides a novel perspective on the functional alterations in the DMN of pediatric patients, suggesting that combined sFC and dFC analysis can provide valuable insights for deepening our understanding of the neuronal mechanism underlying epilepsy in children.
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Affiliation(s)
- Yongxin Li
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China.
| | - Yun Ran
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Maohua Yao
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
| | - Qian Chen
- Department of Pediatric Neurosurgery, Shenzhen Children's Hospital, Shenzhen, China
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4
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Yang C, Biswal B, Cui Q, Jing X, Ao Y, Wang Y. Frequency-dependent alterations of global signal topography in patients with major depressive disorder. Psychol Med 2024:1-10. [PMID: 38362834 DOI: 10.1017/s0033291724000254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) is associated not only with disorders in multiple brain networks but also with frequency-specific brain activities. The abnormality of spatiotemporal networks in patients with MDD remains largely unclear. METHODS We investigated the alterations of the global spatiotemporal network in MDD patients using a large-sample multicenter resting-state functional magnetic resonance imaging dataset. The spatiotemporal characteristics were measured by the variability of global signal (GS) and its correlation with local signals (GSCORR) at multiple frequency bands. The association between these indicators and clinical scores was further assessed. RESULTS The GS fluctuations were reduced in patients with MDD across the full frequency range (0-0.1852 Hz). The GSCORR was also reduced in the MDD group, especially in the relatively higher frequency range (0.0728-0.1852 Hz). Interestingly, these indicators showed positive correlations with depressive scores in the MDD group and relative negative correlations in the control group. CONCLUSION The GS and its spatiotemporal effects on local signals were weakened in patients with MDD, which may impair inter-regional synchronization and related functions. Patients with severe depression may use the compensatory mechanism to make up for the functional impairments.
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Affiliation(s)
- Chengxiao Yang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Xiujuan Jing
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
| | - Yujia Ao
- Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Yifeng Wang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu 610066, China
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5
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Li A, Liu H, Lei X, He Y, Wu Q, Yan Y, Zhou X, Tian X, Peng Y, Huang S, Li K, Wang M, Sun Y, Yan H, Zhang C, He S, Han R, Wang X, Liu B. Hierarchical fluctuation shapes a dynamic flow linked to states of consciousness. Nat Commun 2023; 14:3238. [PMID: 37277338 DOI: 10.1038/s41467-023-38972-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 05/23/2023] [Indexed: 06/07/2023] Open
Abstract
Consciousness arises from the spatiotemporal neural dynamics, however, its relationship with neural flexibility and regional specialization remains elusive. We identified a consciousness-related signature marked by shifting spontaneous fluctuations along a unimodal-transmodal cortical axis. This simple signature is sensitive to altered states of consciousness in single individuals, exhibiting abnormal elevation under psychedelics and in psychosis. The hierarchical dynamic reflects brain state changes in global integration and connectome diversity under task-free conditions. Quasi-periodic pattern detection revealed that hierarchical heterogeneity as spatiotemporally propagating waves linking to arousal. A similar pattern can be observed in macaque electrocorticography. Furthermore, the spatial distribution of principal cortical gradient preferentially recapitulated the genetic transcription levels of the histaminergic system and that of the functional connectome mapping of the tuberomammillary nucleus, which promotes wakefulness. Combining behavioral, neuroimaging, electrophysiological, and transcriptomic evidence, we propose that global consciousness is supported by efficient hierarchical processing constrained along a low-dimensional macroscale gradient.
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Affiliation(s)
- Ang Li
- State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Haiyang Liu
- Department of Anesthesiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100101, China
- Department of Anesthesiology, Qinghai Provincial Traffic Hospital, Xining, 810001, China
| | - Xu Lei
- Sleep and Neuroimaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, China
| | - Yini He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Qian Wu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Yan Yan
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China
| | - Xin Zhou
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Xiaohan Tian
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Yingjie Peng
- State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Shangzheng Huang
- State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Kaixin Li
- State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Meng Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Yuqing Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Hao Yan
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
| | - Cheng Zhang
- The Department of Respiratory and Critical Care Medicine, Peking University First Hospital, Beijing, 100034, China
| | - Sheng He
- State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Ruquan Han
- Department of Anesthesiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100101, China.
| | - Xiaoqun Wang
- State Key Lab of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
- New Cornerstone Science Laboratory, Beijing Normal University, Beijing, 100875, China.
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
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6
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Alalayah KM, Senan EM, Atlam HF, Ahmed IA, Shatnawi HSA. Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means. Diagnostics (Basel) 2023; 13:diagnostics13111957. [PMID: 37296809 DOI: 10.3390/diagnostics13111957] [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: 04/01/2023] [Revised: 05/22/2023] [Accepted: 06/02/2023] [Indexed: 06/12/2023] Open
Abstract
Epilepsy is a neurological disorder in the activity of brain cells that leads to seizures. An electroencephalogram (EEG) can detect seizures as it contains physiological information of the neural activity of the brain. However, visual examination of EEG by experts is time consuming, and their diagnoses may even contradict each other. Thus, an automated computer-aided diagnosis for EEG diagnostics is necessary. Therefore, this paper proposes an effective approach for the early detection of epilepsy. The proposed approach involves the extraction of important features and classification. First, signal components are decomposed to extract the features via the discrete wavelet transform (DWT) method. Principal component analysis (PCA) and the t-distributed stochastic neighbor embedding (t-SNE) algorithm were applied to reduce the dimensions and focus on the most important features. Subsequently, K-means clustering + PCA and K-means clustering + t-SNE were used to divide the dataset into subgroups to reduce the dimensions and focus on the most important representative features of epilepsy. The features extracted from these steps were fed to extreme gradient boosting, K-nearest neighbors (K-NN), decision tree (DT), random forest (RF) and multilayer perceptron (MLP) classifiers. The experimental results demonstrated that the proposed approach provides superior results to those of existing studies. During the testing phase, the RF classifier with DWT and PCA achieved an accuracy of 97.96%, precision of 99.1%, recall of 94.41% and F1 score of 97.41%. Moreover, the RF classifier with DWT and t-SNE attained an accuracy of 98.09%, precision of 99.1%, recall of 93.9% and F1 score of 96.21%. In comparison, the MLP classifier with PCA + K-means reached an accuracy of 98.98%, precision of 99.16%, recall of 95.69% and F1 score of 97.4%.
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Affiliation(s)
- Khaled M Alalayah
- Department of Computer Science, College of Science and Arts, Najran University, Sharurah 68341, Saudi Arabia
| | - Ebrahim Mohammed Senan
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a P.O. Box 1152, Yemen
| | - Hany F Atlam
- Cyber Security Centre, WMG, University of Warwick, Coventry CV4 7AL, UK
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7
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Kim JH, De Asis-Cruz J, Cook KM, Limperopoulos C. Gestational age-related changes in the fetal functional connectome: in utero evidence for the global signal. Cereb Cortex 2023; 33:2302-2314. [PMID: 35641159 PMCID: PMC9977380 DOI: 10.1093/cercor/bhac209] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 05/06/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
The human brain begins to develop in the third gestational week and rapidly grows and matures over the course of pregnancy. Compared to fetal structural neurodevelopment, less is known about emerging functional connectivity in utero. Here, we investigated gestational age (GA)-associated in vivo changes in functional brain connectivity during the second and third trimesters in a large dataset of 110 resting-state functional magnetic resonance imaging scans from a cohort of 95 healthy fetuses. Using representational similarity analysis, a multivariate analytical technique that reveals pair-wise similarity in high-order space, we showed that intersubject similarity of fetal functional connectome patterns was strongly related to between-subject GA differences (r = 0.28, P < 0.01) and that GA sensitivity of functional connectome was lateralized, especially at the frontal area. Our analysis also revealed a subnetwork of connections that were critical for predicting age (mean absolute error = 2.72 weeks); functional connectome patterns of individual fetuses reliably predicted their GA (r = 0.51, P < 0.001). Lastly, we identified the primary principal brain network that tracked fetal brain maturity. The main network showed a global synchronization pattern resembling global signal in the adult brain.
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Affiliation(s)
- Jung-Hoon Kim
- Developing Brain Institue, Children’s National Hospital, 111 Michigan Avenue, N.W., Washington, DC, 20010, USA
| | - Josepheen De Asis-Cruz
- Developing Brain Institue, Children’s National Hospital, 111 Michigan Avenue, N.W., Washington, DC, 20010, USA
| | - Kevin M Cook
- Developing Brain Institue, Children’s National Hospital, 111 Michigan Avenue, N.W., Washington, DC, 20010, USA
| | - Catherine Limperopoulos
- Corresponding author: Developing Brain Institute, Children’s National, 111 Michigan Ave. N.W., Washington D.C. 20010.
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8
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Xie JJ, Li XY, Dong Y, Chen C, Qu BY, Wang S, Xu H, Roe AW, Lai HY, Wu ZY. Local and Global Abnormalities in Pre-symptomatic Huntington's Disease Revealed by 7T Resting-state Functional MRI. Neurosci Bull 2023; 39:94-98. [PMID: 36036300 PMCID: PMC9849632 DOI: 10.1007/s12264-022-00943-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 06/09/2022] [Indexed: 01/22/2023] Open
Affiliation(s)
- Juan-Juan Xie
- Department of Neurology and Department of Medical Genetics in the Second Affiliated Hospital, and Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Xiao-Yan Li
- Department of Neurology and Department of Medical Genetics in the Second Affiliated Hospital, and Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Yi Dong
- Department of Neurology and Department of Medical Genetics in the Second Affiliated Hospital, and Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Cong Chen
- Department of Neurology and Department of Medical Genetics in the Second Affiliated Hospital, and Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Bo-Yi Qu
- Department of Neurology and Department of Medical Genetics in the Second Affiliated Hospital, and Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310009, China
- Interdisciplinary Institute of Neuroscience and Technology, and College of Biomedical Engineering and Instrument Science, Key Laboratory for Biomedical Engineering of the Ministry of Education, Zhejiang University, Hangzhou, 310029, China
| | - Shuang Wang
- Department of Neurology and Department of Medical Genetics in the Second Affiliated Hospital, and Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Han Xu
- Department of Neurology and Department of Medical Genetics in the Second Affiliated Hospital, and Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310009, China
- Department of Neurobiology, Zhejiang University School of Medicine, Hangzhou, 310058, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Anna Wang Roe
- Interdisciplinary Institute of Neuroscience and Technology, and College of Biomedical Engineering and Instrument Science, Key Laboratory for Biomedical Engineering of the Ministry of Education, Zhejiang University, Hangzhou, 310029, China.
| | - Hsin-Yi Lai
- Department of Neurology and Department of Medical Genetics in the Second Affiliated Hospital, and Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310009, China.
- Interdisciplinary Institute of Neuroscience and Technology, and College of Biomedical Engineering and Instrument Science, Key Laboratory for Biomedical Engineering of the Ministry of Education, Zhejiang University, Hangzhou, 310029, China.
| | - Zhi-Ying Wu
- Department of Neurology and Department of Medical Genetics in the Second Affiliated Hospital, and Key Laboratory of Medical Neurobiology of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310009, China.
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, 310058, China.
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9
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Huang J, Wang M, Ju H, Shi Z, Ding W, Zhang D. SD-CNN: A static-dynamic convolutional neural network for functional brain networks. Med Image Anal 2023; 83:102679. [PMID: 36423466 DOI: 10.1016/j.media.2022.102679] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 09/14/2022] [Accepted: 10/29/2022] [Indexed: 11/13/2022]
Abstract
Static functional connections (sFCs) and dynamic functional connections (dFCs) have been widely used in the resting-state functional MRI (rs-fMRI) analysis. sFCs, calculated based on entire rs-fMRI scans, can accurately describe the static topology of the brain network. dFCs, estimated by dividing rs-fMRI scans into a series of short sliding windows, are used to reveal time-varying changes in FC patterns. Currently, how to jointly use sFCs and dFCs to identify brain diseases under the framework of deep learning is still a hot issue. To this end, we propose a static-dynamic convolutional neural network for functional brain networks, which involves a static pathway and a dynamic pathway for taking full advantages of sFCs and dFCs. Specifically, the static pathway, using high-resolution convolution filters (i.e., convolution filters with a high number of channels) at a single adjacency matrix of sFCs, is performed to capture static FC patterns. The dynamic pathway, using low-resolution convolution filters at each adjacency matrix of dFCs, is performed to capture time-varying FC patterns. Two types of diffusion connections are used in this model for encouraging the transfer of information between the static pathway and the dynamic pathway, which can make the learned features more discriminative. Furthermore, a static and dynamic combination classifier is introduced to combine features from two pathways for identifying brain diseases. Experiments on two real datasets demonstrate the effectiveness and advantages of our proposed method.
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Affiliation(s)
- Jiashuang Huang
- School of Information Science and Technology, Nantong University, Nantong, 226019, China; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Mingliang Wang
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Hengrong Ju
- School of Information Science and Technology, Nantong University, Nantong, 226019, China
| | - Zhenquan Shi
- School of Information Science and Technology, Nantong University, Nantong, 226019, China
| | - Weiping Ding
- School of Information Science and Technology, Nantong University, Nantong, 226019, China.
| | - Daoqiang Zhang
- Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
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10
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Zhang J, Northoff G. Beyond noise to function: reframing the global brain activity and its dynamic topography. Commun Biol 2022; 5:1350. [PMID: 36481785 PMCID: PMC9732046 DOI: 10.1038/s42003-022-04297-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 11/24/2022] [Indexed: 12/13/2022] Open
Abstract
How global and local activity interact with each other is a common question in complex systems like climate and economy. Analogously, the brain too displays 'global' activity that interacts with local-regional activity and modulates behavior. The brain's global activity, investigated as global signal in fMRI, so far, has mainly been conceived as non-neuronal noise. We here review the findings from healthy and clinical populations to demonstrate the neural basis and functions of global signal to brain and behavior. We show that global signal (i) is closely coupled with physiological signals and modulates the arousal level; and (ii) organizes an elaborated dynamic topography and coordinates the different forms of cognition. We also postulate a Dual-Layer Model including both background and surface layers. Together, the latest evidence strongly suggests the need to go beyond the view of global signal as noise by embracing a dual-layer model with background and surface layer.
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Affiliation(s)
- Jianfeng Zhang
- grid.263488.30000 0001 0472 9649Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China ,grid.263488.30000 0001 0472 9649School of Psychology, Shenzhen University, Shenzhen, China
| | - Georg Northoff
- grid.13402.340000 0004 1759 700XMental Health Center, Zhejiang University School of Medicine, Hangzhou, China ,grid.28046.380000 0001 2182 2255Institute of Mental Health Research, University of Ottawa, Ottawa, Canada ,grid.410595.c0000 0001 2230 9154Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China
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11
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Wang Y, Li Y, Yang L, Huang W. Altered topological organization of resting-state functional networks in children with infantile spasms. Front Neurosci 2022; 16:952940. [PMID: 36248635 PMCID: PMC9562010 DOI: 10.3389/fnins.2022.952940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 09/14/2022] [Indexed: 11/15/2022] Open
Abstract
Covering neuroimaging evidence has demonstrated that epileptic symptoms are associated with the disrupted topological architecture of the brain network. Infantile spasms (IS) as an age-specific epileptic encephalopathy also showed abnormal structural or functional connectivity in specific brain regions or specific networks. However, little is known about the topological alterations of whole-brain functional networks in patients with IS. To fill this gap, we used the graph theoretical analysis to investigate the topological properties (whole-brain small-world property and modular interaction) in 17 patients with IS and 34 age- and gender-matched healthy controls. The functional networks in both groups showed efficient small-world architecture over the sparsity range from 0.05 to 0.4. While patients with IS showed abnormal global properties characterized by significantly decreased normalized clustering coefficient, normalized path length, small-worldness, local efficiency, and significantly increased global efficiency, implying a shift toward a randomized network. Modular analysis revealed decreased intra-modular connectivity within the default mode network (DMN) and fronto-parietal network but increased inter-modular connectivity between the cingulo-opercular network and occipital network. Moreover, the decreased intra-modular connectivity in DMN was significantly negatively correlated with seizure frequency. The inter-modular connectivity between the cingulo-opercular and occipital network also showed a significant correlation with epilepsy frequency. Together, the current study revealed the disrupted topological organization of the whole-brain functional network, which greatly advances our understanding of neuronal architecture in IS and may contribute to predict the prognosis of IS as disease biomarkers.
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Affiliation(s)
- Ya Wang
- School of Basic Medical Sciences, Engineering Research Center for Translation of Medical 3D Printing Application, Guangdong Provincial Key Laboratory of Digital Medicine and Biomechanics, National Key Discipline of Human Anatomy, Southern Medical University, Guangzhou, China
| | - Yongxin Li
- Formula-Pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China
- *Correspondence: Yongxin Li,
| | - Lin Yang
- Department of Anesthesiology, The Fifth Affiliated Hospital of Southern Medical University, Guangzhou, China
| | - Wenhua Huang
- School of Basic Medical Sciences, Engineering Research Center for Translation of Medical 3D Printing Application, Guangdong Provincial Key Laboratory of Digital Medicine and Biomechanics, National Key Discipline of Human Anatomy, Southern Medical University, Guangzhou, China
- Wenhua Huang,
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Li Y, Qin B, Chen Q, Chen J. Altered dynamic functional network connectivity within default mode network of epileptic children with generalized tonic-clonic seizures. Epilepsy Res 2022; 184:106969. [PMID: 35738202 DOI: 10.1016/j.eplepsyres.2022.106969] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 05/13/2022] [Accepted: 06/14/2022] [Indexed: 11/24/2022]
Abstract
OBJECTIVE Generalized tonic-clonic seizures (GTCS) is a group of epileptic disorders characterized by widespread generalized spike-and-waves discharges along with unresponsiveness and convulsions. Abnormal connectivity in the DMN is the common findings in children with generalized epilepsy. However, the neural mechanisms underlying the altered brain connectivity of DMN in children with GTCS remain unclear. The aim of the current study was to explore the temporal properties of functional connectivity states by dynamic functional connectivity (dFC) within the DMN of GTCS children. METHODS We collected resting-state functional MRI data from 22 GTCS children and 29 age-matched healthy controls. Sliding window approach and k-mean clustering analysis were applied to analyze the dFC and identify transient states of the DMN. Furthermore, the relationship between the dynamic properties and clinical features was assessed. RESULTS The dFC analyses identified two reoccurring states: a more frequent and weak connected state (State 1) and a less frequent and strong connected state (State 2). Relative to the normal control, GTCS children spent more time in State 1 showing weak connections and spent less time in State 2 showing strong connections. Dynamic functional network connectivity strength within the DMN showed both increase and decrease in patient group. In addition, the changes of dynamic metric were found to be correlated with epilepsy duration. SIGNIFICANT Our findings imply abnormal interactions and the state dynamics in DMN of the children with GTCS. These disruptions of temporal dynamic in DMN may provide significance for understanding the neural mechanism underlying the GTCS in children and suggest that dFC method can be considered as a valuable tool in children with epilepsy.
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Affiliation(s)
- Yongxin Li
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China.
| | - Bing Qin
- Epilepsy Center and Department of Neurosurgery, The First Affiliated Hospital, Jinan University, Guangzhou, China
| | - Qian Chen
- Department of Pediatric Neurosurgery, Shenzhen Children's Hospital, Shenzhen, China
| | - Jiaxu Chen
- Guangzhou Key Laboratory of Formula-Pattern of Traditional Chinese Medicine, Formula-pattern Research Center, School of Traditional Chinese Medicine, Jinan University, Guangzhou, China.
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Pazarlar BA, Aripaka SS, Petukhov V, Pinborg L, Khodosevich K, Mikkelsen JD. Expression profile of synaptic vesicle glycoprotein 2A, B, and C paralogues in temporal neocortex tissue from patients with temporal lobe epilepsy (TLE). Mol Brain 2022; 15:45. [PMID: 35578248 PMCID: PMC9109314 DOI: 10.1186/s13041-022-00931-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 05/05/2022] [Indexed: 11/10/2022] Open
Abstract
AbstractSynaptic vesicle glycoprotein-2 (SV2) is a family of proteins consisting of SV2A, SV2B, and SV2C. This protein family has attracted attention in recent years after SV2A was shown to be an epileptic drug target and a perhaps a biomarker of synaptic density. So far, the anatomical localization of these proteins in the rodent and human brain have been reported, but co-expression of SV2 genes on a cellular level, their expressions in the human brain, comparison to radioligand binding, any possible regulation in epilepsy are not known. We have here analyzed the expression of SV2 genes in neuronal subtypes in the temporal neocortex in selected specimens by using single nucleus-RNA sequencing, and performed quantitative PCR in populations of temporal lobe epilepsy (TLE) patients and healthy controls. [3H]-UCB-J autoradiography was performed to analyze the correlation between the mRNA transcript and binding capacity to SV2A. Our data showed that the SV2A transcript is expressed in all glutamatergic and GABAergic cortical subtypes, while SV2B expression is restricted to only the glutamatergic neurons and SV2C has very limited expression in a small subgroup of GABAergic interneurons. The level of [3H]-UCB-J binding and the concentration of SV2A mRNA is strongly correlated in each patient, and the expression is lower in the TLE patients. There is no relationship between SV2A expression and age, sex, seizure frequency, duration of epilepsy, or whether patients were recently treated with levetiracetam or not. Collectively, these findings point out a neuronal subtype-specific distribution of the expression of the three SV2 genes, and the lower levels of both radioligand binding and expression further emphasize the significance of these proteins in this disease.
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Duan R, Jing L, Li Y, Gong Z, Yao Y, Wang W, Zhang Y, Cheng J, Peng Y, Li L, Jia Y. Altered Global Signal Topography in Alcohol Use Disorders. Front Aging Neurosci 2022; 14:803780. [PMID: 35250540 PMCID: PMC8888878 DOI: 10.3389/fnagi.2022.803780] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 01/24/2022] [Indexed: 12/15/2022] Open
Abstract
The most common symptom of patients with alcohol use disorders (AUD) is cognitive impairment that negatively affects abstinence. Presently, there is a lack of indicators for early diagnosis of alcohol-related cognitive impairment (ARCI). We aimed to assess the cognitive deficits in AUD patients with the help of a specific imaging marker for ARCI. Data-driven dynamic and static global signal topography (GST) methods were applied to explore the cross-talks between local and global neuronal activities in the AUD brain. Twenty-six ARCI, 54 AUD without cognitive impairment (AUD-NCI), and gender/age-matched 40 healthy control (HC) subjects were recruited for this study. We found that there was no significant difference with respect to voxel-based morphometry (VBM) and static GST between AUD-NCI and ARCI groups. And in dynamic GST measurements, the AUD-NCI patients had the highest coefficient of variation (CV) at the right insula, followed by ARCI and the HC subjects. In precuneus, the order was reversed. There was no significant correlation between the dynamic GST and behavioral scores or alcohol consumption. These results suggested that dynamic GST might have potential implications in understanding AUD pathogenesis and disease management.
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Affiliation(s)
- Ranran Duan
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lijun Jing
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yanfei Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhe Gong
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yaobing Yao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weijian Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ying Peng
- Department of Neurology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Li Li
- Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
- *Correspondence: Li Li Yanjie Jia
| | - Yanjie Jia
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Li Li Yanjie Jia
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Romero-Garcia R, Hart MG, Bethlehem RAI, Mandal A, Assem M, Crespo-Facorro B, Gorriz JM, Burke GAA, Price SJ, Santarius T, Erez Y, Suckling J. BOLD Coupling between Lesioned and Healthy Brain Is Associated with Glioma Patients' Recovery. Cancers (Basel) 2021; 13:5008. [PMID: 34638493 PMCID: PMC8508466 DOI: 10.3390/cancers13195008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/30/2021] [Accepted: 10/01/2021] [Indexed: 11/16/2022] Open
Abstract
Predicting functional outcomes after surgery and early adjuvant treatment is difficult due to the complex, extended, interlocking brain networks that underpin cognition. The aim of this study was to test glioma functional interactions with the rest of the brain, thereby identifying the risk factors of cognitive recovery or deterioration. Seventeen patients with diffuse non-enhancing glioma (aged 22-56 years) were longitudinally MRI scanned and cognitively assessed before and after surgery and during a 12-month recovery period (55 MRI scans in total after exclusions). We initially found, and then replicated in an independent dataset, that the spatial correlation pattern between regional and global BOLD signals (also known as global signal topography) was associated with tumour occurrence. We then estimated the coupling between the BOLD signal from within the tumour and the signal extracted from different brain tissues. We observed that the normative global signal topography is reorganised in glioma patients during the recovery period. Moreover, we found that the BOLD signal within the tumour and lesioned brain was coupled with the global signal and that this coupling was associated with cognitive recovery. Nevertheless, patients did not show any apparent disruption of functional connectivity within canonical functional networks. Understanding how tumour infiltration and coupling are related to patients' recovery represents a major step forward in prognostic development.
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Affiliation(s)
- Rafael Romero-Garcia
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
- Department of Medical Physiology and Biophysics, Instituto de Biomedicina de Sevilla (IBiS), HUVR/CSIC/Universidad de Sevilla, 41013 Sevilla, Spain
| | - Michael G Hart
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | | | - Ayan Mandal
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Moataz Assem
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
| | - Benedicto Crespo-Facorro
- Department of Psychiatry, Instituto de Investigación Sanitaria de Sevilla, IBiS, Hospital Universitario Virgen del Rocio, CIBERSAM, 41013 Sevilla, Spain
| | - Juan Manuel Gorriz
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
- Department of Signal Theory, Networking and Communications, Universidad de Granada, 18071 Granada, Spain
| | - G A Amos Burke
- Department of Paediatric Haematology, Oncology and Palliative Care, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Stephen J Price
- Academic Neurosurgery Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Thomas Santarius
- Academic Neurosurgery Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Yaara Erez
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, UK
- Faculty of Engineering, Bar-Ilan University, Ramat Gan 5290002, Israel
| | - John Suckling
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge CB2 0SZ, UK
- Cambridge and Peterborough NHS Foundation Trust, Cambridge CB21 5EF, UK
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Ao Y, Ouyang Y, Yang C, Wang Y. Global Signal Topography of the Human Brain: A Novel Framework of Functional Connectivity for Psychological and Pathological Investigations. Front Hum Neurosci 2021; 15:644892. [PMID: 33841119 PMCID: PMC8026854 DOI: 10.3389/fnhum.2021.644892] [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: 12/22/2020] [Accepted: 03/01/2021] [Indexed: 11/15/2022] Open
Abstract
The global signal (GS), which was once regarded as a nuisance of functional magnetic resonance imaging, has been proven to convey valuable neural information. This raised the following question: what is a GS represented in local brain regions? In order to answer this question, the GS topography was developed to measure the correlation between global and local signals. It was observed that the GS topography has an intrinsic structure characterized by higher GS correlation in sensory cortices and lower GS correlation in higher-order cortices. The GS topography could be modulated by individual factors, attention-demanding tasks, and conscious states. Furthermore, abnormal GS topography has been uncovered in patients with schizophrenia, major depressive disorder, bipolar disorder, and epilepsy. These findings provide a novel insight into understanding how the GS and local brain signals coactivate to organize information in the human brain under various brain states. Future directions were further discussed, including the local-global confusion embedded in the GS correlation, the integration of spatial information conveyed by the GS, and temporal information recruited by the connection analysis. Overall, a unified psychopathological framework is needed for understanding the GS topography.
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Affiliation(s)
- Yujia Ao
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Yujie Ouyang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Chengxiao Yang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Yifeng Wang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
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