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Shi LJ, Li CC, Zhang XT, Lin YC, Wang YP, Zhang JC. Application of HFO and scaling analysis of neuronal oscillations in the presurgical evaluation of focal epilepsy. Brain Res Bull 2024; 215:111018. [PMID: 38908759 DOI: 10.1016/j.brainresbull.2024.111018] [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: 12/18/2023] [Revised: 03/07/2024] [Accepted: 06/19/2024] [Indexed: 06/24/2024]
Abstract
PURPOSE To explore the utility of high frequency oscillations (HFO) and long-range temporal correlations (LRTCs) in preoperative assessment of epilepsy. METHODS MEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results. RESULTS Compared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p<0.01). Moreover, these two LRCT indices across five distinct frequency bands correlated with EZ. CONCLUSION HFO and source-level LRTCs are correlated with EZ. Integrating HFO and LRTCs may be an effective approach for presurgical evaluation of epilepsy.
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Affiliation(s)
- Li-Juan Shi
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Can-Cheng Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
| | - Xia-Ting Zhang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing 100053, China
| | - Yi-Cong Lin
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing 100053, China
| | - Yu-Ping Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing 100053, China.
| | - Ji-Cong Zhang
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China; Hefei Innovation Research Institute, Beihang University, Hefei, Anhui, China.
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2
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Deng D, Sun H, Wang Y, Guo X, Yuan Y, Wang J, Qiu L. Structural and functional abnormalities in first-episode drug-naïve pediatric idiopathic generalized epilepsy. Cereb Cortex 2024; 34:bhae021. [PMID: 38314605 DOI: 10.1093/cercor/bhae021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 01/09/2024] [Accepted: 01/10/2024] [Indexed: 02/06/2024] Open
Abstract
The aim of this study was to investigate brain structure and corresponding static and dynamic functional connectivity (sFC & dFC) abnormalities in untreated, first-episode pediatric idiopathic generalized epilepsy (IGE), with the goal of better understanding the underlying pathological mechanisms of IGE. Thirty-one children with IGE and 31 age-matched healthy controls (HC) were recruited. Structural magnetic resonance imaging (sMRI) data were acquired, and voxel-based morphometry (VBM) analysis were performed to reveal abnormal gray matter volume (GMV). Moreover, sFC and dFC analyses were conducted using the brain areas exhibiting abnormal GMV as seed regions to explore abnormal functional couplings. Compared to HC, the IGE group exhibited increased GMV in left middle cingulate cortex (MCC) and right parahippocampus (ParaHipp). In addition, the analyses of dFC and sFC with MCC and ParaHipp as seeds revealed more extensive functional connectivity (FC) changes in dFC. Notably, the structurally and functionally abnormal brain areas were primarily localized in the default mode network (DMN). However, our study did not find any significant associations between these altered neuroimaging measurements and clinical outcomes. This study uncovered microstructural changes as well as corresponding sFC and dFC changes in patients with new-onset, untreated pediatric IGE. The affected brain regions were primarily located within the DMN, highlighting the DMN's crucial role in the development of pediatric IGE.
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Affiliation(s)
- Dingmei Deng
- West China School of Public Health and West China Fourth Hospital, Sichuan University, No. 18, South Section 3, First Ring Road, Wuhou District, Chengdu 610041, China
- Medical Imaging Center, The Second People's Hospital of Yibin, 96# Beida Street, Cuiping District, Yibin 644000, China
- Clinical Research and Translational Center, Second People's Hospital of Yibin City-West China Yibin Hospital, Sichuan University, 96# Beida Street, Cuiping District, Yibin 644000, China
| | - Hui Sun
- College of Electrical Engineering, Sichuan University, No. 24, South Section 1, First Ring Road, Wuhou District, Chengdu 610065, China
| | - Yuting Wang
- Medical Imaging Center, The Second People's Hospital of Yibin, 96# Beida Street, Cuiping District, Yibin 644000, China
- Clinical Research and Translational Center, Second People's Hospital of Yibin City-West China Yibin Hospital, Sichuan University, 96# Beida Street, Cuiping District, Yibin 644000, China
| | - Xin Guo
- Medical Imaging Center, The Second People's Hospital of Yibin, 96# Beida Street, Cuiping District, Yibin 644000, China
- Clinical Research and Translational Center, Second People's Hospital of Yibin City-West China Yibin Hospital, Sichuan University, 96# Beida Street, Cuiping District, Yibin 644000, China
| | - Yizhi Yuan
- Medical Imaging Center, The Second People's Hospital of Yibin, 96# Beida Street, Cuiping District, Yibin 644000, China
- Clinical Research and Translational Center, Second People's Hospital of Yibin City-West China Yibin Hospital, Sichuan University, 96# Beida Street, Cuiping District, Yibin 644000, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, No.7, Zhiyuan Road, Chenggong District, Kunming 650500, China
- Yunnan Key Laboratory of Primate Biomedical Research, No.7, Zhiyuan Road, Chenggong District, Kunming 650500, China
| | - Lihua Qiu
- Medical Imaging Center, The Second People's Hospital of Yibin, 96# Beida Street, Cuiping District, Yibin 644000, China
- Clinical Research and Translational Center, Second People's Hospital of Yibin City-West China Yibin Hospital, Sichuan University, 96# Beida Street, Cuiping District, Yibin 644000, China
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, No. 24, South Section 1, First Ring Road, Wuhou District, Chengdu City, Sichuan Province, Chengdu 610065, China
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3
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Pan H, Mao Y, Liu P, Li Y, Wei G, Qiao X, Ren Y, Zhao F. Extracting transition features among brain states based on coarse-grained similarity measurement for autism spectrum disorder analysis. Med Phys 2023; 50:6269-6282. [PMID: 36995984 DOI: 10.1002/mp.16406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/13/2023] [Accepted: 03/23/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND The abnormal brain functional connectivity (FC) of patients with mental diseases is closely linked to the transition features among brain states. However, the current research on state transition will produce certain division deviations in the measurement method of state division, and also ignore the transition features among multiple states that contain more abundant information for analyzing brain diseases. PURPOSE To investigate the potential of the proposed method based on coarse-grained similarity measurement to solve the problem of state division, and consider the transition features among multiple states to analyze the FC abnormalities of autism spectrum disorder (ASD) patients. METHODS We used resting-state functional magnetic resonance imaging to examine 45 ASD and 47 healthy controls (HC). The FC between brain regions was calculated by the sliding window and correlation algorithm, and a novel coarse-grained similarity measure method was used to cluster the FC networks into five states, and then extract the features both of the state itself and the transition features among multiple states for analysis and diagnosis. RESULTS (1) The state as divided by the coarse-grained measurement method improves the diagnostic performance of individuals with ASD compared with previous methods. (2) The transition features among multiple states can provide complementary information to the features of the state itself in the ASD analysis and diagnosis. (3) ASD individuals have different brain state transitions than HC. Specifically, the abnormalities in intra- and inter-network connectivity of ASD patients mainly occur in the default mode network, the visual network, and the cerebellum. CONCLUSIONS Such results demonstrate that our approach with new measurements and new features is effective and promising in brain state analysis and ASD diagnosis.
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Affiliation(s)
- Hongxin Pan
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Yanyan Mao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Peiqiang Liu
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Yuan Li
- School of Management Science and Engineering, Shandong Technology and Business University, Yantai, China
| | - Guanglan Wei
- Information Network Center, Shandong Second Provincial General Hospital, Jinan, China
| | - Xiaoyan Qiao
- School of Mathematics and Information Science, Shandong Technology and Business University, Yantai, China
| | - Yande Ren
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
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4
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Li Y, Qian L, Li G, Zhang Z. Frequency specificity of aberrant triple networks in major depressive disorder: a resting-state effective connectivity study. Front Neurosci 2023; 17:1200029. [PMID: 37457005 PMCID: PMC10347531 DOI: 10.3389/fnins.2023.1200029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/13/2023] [Indexed: 07/18/2023] Open
Abstract
Major depressive disorder (MDD) has been associated with aberrant effective connectivity (EC) among the default mode network (DMN), salience network (SN), and central executive network (CEN)-collectively referred to as triple networks. However, prior research has predominantly concentrated on broad frequency bands (0.01-0.08 Hz or 0.01-0.15 Hz), ignoring the influence of distinct rhythms on triple network causal dynamics. In the present study, we aim to investigate EC alterations within the triple networks across various frequency bands in patients with MDD. Utilizing a data-driven frequency decomposition approach and a multivariate Granger causality analysis, we characterized frequency-specific EC patterns of triple networks in 49 MDD patients and 54 healthy controls. A support vector machine classifier was subsequently employed to assess the discriminative capacity of the frequency-specific EC features. Our findings revealed that, compared to controls, patients exhibited not only enhanced mean EC within the CEN in the conventional frequency band (0.01-0.08 Hz), but also decreased mean EC from the SN to the DMN in a higher frequency band (0.12-0.18 Hz), and increased mean EC from the CEN to the SN in a sub-frequency band (0.04-0.08 Hz); the latter was significantly correlated with disease severity. Moreover, optimal classification performance for distinguishing patients from controls was attained by combining EC features across all three frequency bands, with the area under the curve (AUC) value of 0.8831 and the corresponding accuracy, sensitivity, and specificity of 89.97%, 92.63%, and 87.32%, respectively. These insights into EC changes within the triple networks across multiple frequency bands offer valuable perspectives on the neurobiological basis of MDD and could aid in developing frequency-specific EC features as potential biomarkers for disease diagnosis.
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Affiliation(s)
- Ying Li
- Department of Electronics and Information Engineering, Lanzhou Institute of Technology, Lanzhou, China
| | - Linze Qian
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Gang Li
- Key Laboratory of Urban Rail Transit Intelligent Operation and Maintenance Technology & Equipment of Zhejiang Provincial, Zhejiang Normal University, Jinhua, China
- College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
| | - Zhe Zhang
- School of Physics, Hangzhou Normal University, Hangzhou, China
- Institute of Brain Science, Hangzhou Normal University School of Basic Medical Sciences, Hangzhou, China
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5
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Qin Y, Li S, Yao D, Luo C. Causality Analysis to the Abnormal Subcortical–Cortical Connections in Idiopathic-Generalized Epilepsy. Front Neurosci 2022; 16:925968. [PMID: 35844218 PMCID: PMC9280354 DOI: 10.3389/fnins.2022.925968] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 06/06/2022] [Indexed: 11/13/2022] Open
Abstract
Idiopathic generalized epilepsy (IGE) was characterized by 3–6 Hz generalized spike-wave discharges (GSWDs), and extensive altered interactions in subcortical-cortical circuit. However, the dynamics and the causal relationship among these interactions were less studied. Using resting-state functional magnetic resonance imaging (fMRI) data, the abnormal connections in the subcortical-cortical pathway in IGE were examined. Then, we proposed a novel method of granger causal analysis based on the dynamic functional connectivity, and the predictive effects among these abnormal connections were calculated. The results showed that the thalamus, and precuneus were key regions representing abnormal functional network connectivity (FNC) in the subcortical-cortical circuit. Moreover, the connectivity between precuneus and adjacent regions had a causal effect on the widespread dysfunction of the thalamocortical circuit. In addition, the connection between the striatum and thalamus indicated the modulation role on the cortical connection in epilepsy. These results described the causality of the widespread abnormality of the subcortical-cortical circuit in IGE in terms of the dynamics of functional connections, which provided additional evidence for understanding the potential modulation pattern of the abnormal epileptic pathway.
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Affiliation(s)
- Yun Qin
- Sichuan Provincial People’s Hospital, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China
| | - Sipei Li
- Glasgow College, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- Sichuan Provincial People’s Hospital, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China
| | - Cheng Luo
- Sichuan Provincial People’s Hospital, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- *Correspondence: Cheng Luo,
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6
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Liu G, Zheng W, Liu H, Guo M, Ma L, Hu W, Ke M, Sun Y, Zhang J, Zhang Z. Aberrant dynamic structure-function relationship of rich-club organization in treatment-naïve newly diagnosed juvenile myoclonic epilepsy. Hum Brain Mapp 2022; 43:3633-3645. [PMID: 35417064 PMCID: PMC9294302 DOI: 10.1002/hbm.25873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 03/28/2022] [Accepted: 04/03/2022] [Indexed: 11/25/2022] Open
Abstract
Neuroimaging studies have shown that juvenile myoclonic epilepsy (JME) is characterized by impaired brain networks. However, few studies have investigated the potential disruptions in rich‐club organization—a core feature of the brain networks. Moreover, it is unclear how structure–function relationships dynamically change over time in JME. Here, we quantify the anatomical rich‐club organization and dynamic structural and functional connectivity (SC–FC) coupling in 47 treatment‐naïve newly diagnosed patients with JME and 40 matched healthy controls. Dynamic functional network efficiency and its association with SC–FC coupling were also calculated to examine the supporting of structure–function relationship to brain information transfer. The results showed that the anatomical rich‐club organization was disrupted in the patient group, along with decreased connectivity strength among rich‐club hub nodes. Furthermore, reduced SC–FC coupling in rich‐club organization of the patients was found in two functionally independent dynamic states, that is the functional segregation state (State 1) and the strong somatomotor‐cognitive control interaction state (State 5); and the latter was significantly associated with disease severity. In addition, the relationships between SC–FC coupling of hub nodes connections and functional network efficiency in State 1 were found to be absent in patients. The aberrant dynamic SC–FC coupling of rich‐club organization suggests a selective influence of densely interconnected network core in patients with JME at the early phase of the disease, offering new insights and potential biomarkers into the underlying neurodevelopmental basis of behavioral and cognitive impairments observed in JME.
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Affiliation(s)
- Guangyao Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China.,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Hong Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China.,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Man Guo
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Laiyang Ma
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China.,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Wanjun Hu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China.,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Ming Ke
- College of Computer and Communication, Lanzhou University of Technology, Lanzhou, China
| | - Yu Sun
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.,Zhejiang Lab, Hangzhou, China
| | - Jing Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China.,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou, China
| | - Zhe Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.,School of Physics, Hangzhou Normal University, Hangzhou, China
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7
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Sendi MS, Zendehrouh E, Sui J, Fu Z, Zhi D, Lv L, Ma X, Ke Q, Li X, Wang C, Abbott CC, Turner JA, Miller RL, Calhoun VD. Abnormal Dynamic Functional Network Connectivity Estimated from Default Mode Network Predicts Symptom Severity in Major Depressive Disorder. Brain Connect 2021; 11:838-849. [PMID: 33514278 PMCID: PMC8713570 DOI: 10.1089/brain.2020.0748] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Background: Major depressive disorder (MDD) is a severe mental illness marked by a continuous sense of sadness and a loss of interest. The default mode network (DMN) is a group of brain areas that are more active during rest and deactivate when engaged in task-oriented activities. The DMN of MDD has been found to have aberrant static functional network connectivity (FNC) in recent studies. In this work, we extend previous findings by evaluating dynamic functional network connectivity (dFNC) within the DMN subnodes in MDD. Methods: We analyzed resting-state functional magnetic resonance imaging data of 262 patients with MDD and 277 healthy controls (HCs). We estimated dFNCs for seven subnodes of the DMN, including the anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and precuneus (PCu), using a sliding window approach, and then clustered the dFNCs into five brain states. Classification of MDD and HC subjects based on state-specific FC was performed using a logistic regression classifier. Transition probabilities between dFNC states were used to identify relationships between symptom severity and dFNC data in MDD patients. Results: By comparing state-specific FNC between HC and MDD, a disrupted connectivity pattern was observed within the DMN. In more detail, we found that the connectivity of ACC is stronger, and the connectivity between PCu and PCC is weaker in individuals with MDD than in those of HC subjects. In addition, MDD showed a higher probability of transitioning from a state with weaker ACC connectivity to a state with stronger ACC connectivity, and this abnormality is associated with symptom severity. This is the first research to look at the dFC of the DMN in MDD with a large sample size. It provides novel evidence of abnormal time-varying DMN configuration in MDD and offers links to symptom severity in MDD subjects. Impact Statement This study is the first attempt that explored the temporal change on default mode network (DMN) connectivity in a relatively large cohort of patients with major depressive disorder (MDD). We also introduced a new hypothesis that explains the inconsistency in DMN functional network connectivity (FNC) comparison between MDD and healthy control based on static FNC in the previous literature. Additionally, our findings suggest that within anterior cingulate cortex connectivity and the connectivity between the precuneus and posterior cingulate cortex are the potential biomarkers for the future intervention of MDD.
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Affiliation(s)
- Mohammad S.E. Sendi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Elaheh Zendehrouh
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Jing Sui
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Future Technologies, University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Dongmei Zhi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Future Technologies, University of Chinese Academy of Sciences, Beijing, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
- Henan Key Laboratory of Biological Psychiatry, Xinxiang Medical University, Xinxiang, China
| | - Xiaohong Ma
- Psychiatric Laboratory and Mental Health Center, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
- Huaxi Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Qing Ke
- Department of Neurology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xianbin Li
- Beijing Key Lab of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Chuanyue Wang
- Beijing Key Lab of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
| | | | - Jessica A. Turner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Psychology, Georgia State University, Atlanta, Georgia, USA
- Neuroscience Institute, Georgia State University, Atlanta, Georgia, USA
| | - Robyn L. Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
| | - Vince D. Calhoun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
- Department of Computer Science, Georgia State University, Atlanta, Georgia, USA
- Department of Psychology, Georgia State University, Atlanta, Georgia, USA
- Neuroscience Institute, Georgia State University, Atlanta, Georgia, USA
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8
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A systematic review of resting-state and task-based fmri in juvenile myoclonic epilepsy. Brain Imaging Behav 2021; 16:1465-1494. [PMID: 34786666 DOI: 10.1007/s11682-021-00595-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/20/2021] [Indexed: 10/19/2022]
Abstract
Functional neuroimaging modalities have enhanced our understanding of juvenile myoclonic epilepsy (JME) underlying neural mechanisms. Due to its non-invasive, sensitive and analytical nature, functional magnetic resonance imaging (fMRI) provides valuable insights into relevant functional brain networks and their segregation and integration properties. We systematically reviewed the contribution of resting-state and task-based fMRI to the current understanding of the pathophysiology and the patterns of seizure propagation in JME Altogether, despite some discrepancies, functional findings suggest that corticothalamo-striato-cerebellar network along with default-mode network and salience network are the most affected networks in patients with JME. However, further studies are required to investigate the association between JME's main deficiencies, e.g., motor and cognitive deficiencies and fMRI findings. Moreover, simultaneous electroencephalography-fMRI (EEG-fMRI) studies indicate that alterations of these networks play a role in seizure modulation but fall short of identifying a causal relationship between altered functional properties and seizure propagation. This review highlights the complex pathophysiology of JME, which necessitates the design of more personalized diagnostic and therapeutic strategies in this group.
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9
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Sendi MSE, Zendehrouh E, Ellis CA, Liang Z, Fu Z, Mathalon DH, Ford JM, Preda A, van Erp TGM, Miller RL, Pearlson GD, Turner JA, Calhoun VD. Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity. Front Neural Circuits 2021; 15:649417. [PMID: 33815070 PMCID: PMC8013735 DOI: 10.3389/fncir.2021.649417] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 02/24/2021] [Indexed: 01/10/2023] Open
Abstract
Background: Schizophrenia affects around 1% of the global population. Functional connectivity extracted from resting-state functional magnetic resonance imaging (rs-fMRI) has previously been used to study schizophrenia and has great potential to provide novel insights into the disorder. Some studies have shown abnormal functional connectivity in the default mode network (DMN) of individuals with schizophrenia, and more recent studies have shown abnormal dynamic functional connectivity (dFC) in individuals with schizophrenia. However, DMN dFC and the link between abnormal DMN dFC and symptom severity have not been well-characterized. Method: Resting-state fMRI data from subjects with schizophrenia (SZ) and healthy controls (HC) across two datasets were analyzed independently. We captured seven maximally independent subnodes in the DMN by applying group independent component analysis and estimated dFC between subnode time courses using a sliding window approach. A clustering method separated the dFCs into five reoccurring brain states. A feature selection method modeled the difference between SZs and HCs using the state-specific FC features. Finally, we used the transition probability of a hidden Markov model to characterize the link between symptom severity and dFC in SZ subjects. Results: We found decreases in the connectivity of the anterior cingulate cortex (ACC) and increases in the connectivity between the precuneus (PCu) and the posterior cingulate cortex (PCC) (i.e., PCu/PCC) of SZ subjects. In SZ, the transition probability from a state with weaker PCu/PCC and stronger ACC connectivity to a state with stronger PCu/PCC and weaker ACC connectivity increased with symptom severity. Conclusions: To our knowledge, this was the first study to investigate DMN dFC and its link to schizophrenia symptom severity. We identified reproducible neural states in a data-driven manner and demonstrated that the strength of connectivity within those states differed between SZs and HCs. Additionally, we identified a relationship between SZ symptom severity and the dynamics of DMN functional connectivity. We validated our results across two datasets. These results support the potential of dFC for use as a biomarker of schizophrenia and shed new light upon the relationship between schizophrenia and DMN dynamics.
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Affiliation(s)
- Mohammad S. E. Sendi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Elaheh Zendehrouh
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Charles A. Ellis
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Zhijia Liang
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Daniel H. Mathalon
- Department of Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, CA, United States
- Mental Health Service, Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States
| | - Judith M. Ford
- Department of Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, CA, United States
- Mental Health Service, Veterans Affairs San Francisco Healthcare System, San Francisco, CA, United States
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Theo G. M. van Erp
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Robyn L. Miller
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Godfrey D. Pearlson
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, United States
| | - Jessica A. Turner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Psychology, Neuroscience Institute, Georgia State University, Atlanta, GA, United States
| | - Vince D. Calhoun
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, United States
- Department of Psychology, Neuroscience Institute, Georgia State University, Atlanta, GA, United States
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10
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Zhang Z, Liu G, Zheng W, Shi J, Liu H, Sun Y. Altered dynamic effective connectivity of the default mode network in newly diagnosed drug-naïve juvenile myoclonic epilepsy. Neuroimage Clin 2020; 28:102431. [PMID: 32950903 PMCID: PMC7509229 DOI: 10.1016/j.nicl.2020.102431] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Revised: 08/08/2020] [Accepted: 09/08/2020] [Indexed: 01/21/2023]
Abstract
Juvenile myoclonic epilepsy (JME) has been repeatedly revealed to be associated with brain dysconnectivity in the default mode network (DMN). However, the implicit assumption of stationary and nondirectional functional connectivity (FC) in most previous resting-state fMRI studies raises an open question of JME-related aberrations in dynamic causal properties of FC. Here, we introduces an empirical method incorporating sliding-window approach and a multivariate Granger causality analysis to investigate, for the first time, the reorganization of dynamic effective connectivity (DEC) in DMN for patients with JME. DEC was obtained from resting-state fMRI of 34 patients with newly diagnosed and drug-naïve JME and 34 matched controls. Through clustering analysis, we found two distinct states that characterize the DEC patterns (i.e., a less frequent, strongly connected state (State 1) and a more frequent, weakly connected state (State 2)). Patients showed altered ECs within DMN subnetworks in the State 2, whereas abnormal ECs between DMN subnetworks were found in the State 1. Furthermore, we observed that the causal influence flows of the medial prefrontal cortex and angular gyrus were altered in a manner of state specificity, and associated with disease severity of patients. Overall, our findings extend the dysconnectivity hypothesis in JME from static to dynamic causal FC and demonstrate that aberrant DEC may underlie abnormal brain function in JME at early phase of illness.
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Affiliation(s)
- Zhe Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Zhejiang, China
| | - Guangyao Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
| | - Weihao Zheng
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Zhejiang, China
| | - Jie Shi
- School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Hong Liu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, China
| | - Yu Sun
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Zhejiang, China; Department of Radiology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Zhejiang, China.
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11
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Wein S, Tomé AM, Goldhacker M, Greenlee MW, Lang EW. A Constrained ICA-EMD Model for Group Level fMRI Analysis. Front Neurosci 2020; 14:221. [PMID: 32351349 PMCID: PMC7175031 DOI: 10.3389/fnins.2020.00221] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Accepted: 02/28/2020] [Indexed: 11/13/2022] Open
Abstract
Independent component analysis (ICA), being a data-driven method, has been shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is that it is not, in general, compatible with the analysis of group data. Various techniques have been proposed to overcome this limitation of ICA. In this paper, a novel ICA-based workflow for extracting resting-state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used, in a data-driven manner, to generate reference signals that can be incorporated into a constrained version of ICA (cICA), thereby eliminating the inherent ambiguities of ICA. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach for fMRI analysis. In this study, we demonstrate that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA. This approach yields typical resting-state patterns that are consistent over subjects. By introducing these reference signals into the ICA, our processing pipeline yields comparable activity patterns across subjects in a mathematically transparent manner. Our approach provides a user-friendly tool to adjust the trade-off between a high similarity across subjects and preserving individual subject features of the independent components.
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Affiliation(s)
- Simon Wein
- CIML, Biophysics, University of Regensburg, Regensburg, Germany.,Experimental Psychology, University of Regensburg, Regensburg, Germany
| | - Ana M Tomé
- IEETA/DETI, Universidade de Aveiro, Aveiro, Portugal
| | - Markus Goldhacker
- CIML, Biophysics, University of Regensburg, Regensburg, Germany.,Experimental Psychology, University of Regensburg, Regensburg, Germany
| | - Mark W Greenlee
- Experimental Psychology, University of Regensburg, Regensburg, Germany
| | - Elmar W Lang
- CIML, Biophysics, University of Regensburg, Regensburg, Germany
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12
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Altered dynamic functional connectivity in weakly-connected state in major depressive disorder. Clin Neurophysiol 2019; 130:2096-2104. [PMID: 31541987 DOI: 10.1016/j.clinph.2019.08.009] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Revised: 05/27/2019] [Accepted: 08/14/2019] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Major depressive disorder (MDD) is accompanied by abnormal changes in dynamic functional connectivity (FC) among brain regions. The aim of this study is to investigate whether the abnormalities of dynamic FC in MDD are state-dependent (related to a specific connectivity state). METHODS We performed time-varying connectivity analysis on resting-state functional magnetic resonance imaging (rs-fMRI) of 49 MDD patients and 54 matched healthy controls (HCs). FC differences between groups in each connectivity state were analyzed and associations between disease severity and dynamics of aberrant FC were explored. RESULTS Two distinct connectivity states (i.e., weakly-connected and strongly-connected state) were identified. Compared to HCs, MDD patients were associated with increased mean dwell time and decreased FC between and within subnetworks in the weakly-connected state. Dynamics of reduced FC between cognitive control network and default mode network as well as within cognitive control network predicted individual differences in depression symptom severity. CONCLUSIONS Our findings suggested that the MDD-caused FC alterations mostly appeared in the weakly-connected state, which might contribute to clinical diagnosis of MDD. SIGNIFICANCE These findings provide new perspectives for understanding the state-dependent neurophysiological mechanisms in MDD.
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13
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Lee H, Park KM. Structural and functional connectivity in newly diagnosed juvenile myoclonic epilepsy. Acta Neurol Scand 2019; 139:469-475. [PMID: 30758836 DOI: 10.1111/ane.13079] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Accepted: 02/10/2019] [Indexed: 01/02/2023]
Abstract
OBJECTIVES We aimed to evaluate structural and functional connectivity of patients with newly diagnosed juvenile myoclonic epilepsy (JME) compared to healthy subjects. METHODS We enrolled 36 patients with a diagnosis of JME, who were newly diagnosed and drug-naïve. They underwent T1-weighted imaging, and structural volumes were calculated using FreeSurfer software. In addition, EEG data were obtained from all of them. Structural and functional connectivity matrices were estimated by calculating the structural volumes and EEG amplitude correlation, respectively. Then, the connectivity measures were calculated using the BRAPH program. We also enrolled healthy subjects to compare its structural and functional connectivity with the patients with JME. RESULTS We observed that patients with JME exhibited significantly different functional and structural connectivity compared to healthy control subjects. In the global structural connectivity, global efficiency, and local efficiency, and small-worldness index were decreased, whereas characteristic path length was increased in patients with JME. Betweenness centrality of cingulate, precentral, superior parietal, and superior frontal cortex was increased in patients with JME whereas that of hippocampus was decreased. In the functional connectivity, the betweenness centrality of the fronto-central electrodes was significantly increased. CONCLUSIONS This study reports that the structural connectivity and functional connectivity in patients with JME are significantly different from those in healthy control subjects, even those with newly diagnosed drug-naive state. The patients with JME exhibited disrupted topological disorganization of the global brain network and hub reorganization in structural and functional connectivity. These alterations are implicated in the pathogenesis of JME and suggestive of network disease.
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Affiliation(s)
- Ho‐Joon Lee
- Department of Radiology Haeundae Paik Hospital Busan Korea
| | - Kang Min Park
- Department of Neurology Haeundae Paik Hospital Busan Korea
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