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Zhang L, Zhuang B, Wang M, Zhu J, Chen T, Yang Y, Shi H, Zhu X, Ma L. Delineating abnormal individual structural covariance brain network organization in pediatric epilepsy with unilateral resection of visual cortex. Epilepsy Behav Rep 2024; 27:100676. [PMID: 38826153 PMCID: PMC11137379 DOI: 10.1016/j.ebr.2024.100676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 05/13/2024] [Accepted: 05/14/2024] [Indexed: 06/04/2024] Open
Abstract
Although several previous studies have used resting-state functional magnetic resonance imaging and diffusion tensor imaging to report topological changes in the brain in epilepsy, it remains unclear whether the individual structural covariance network (SCN) changes in epilepsy, especially in pediatric epilepsy with visual cortex resection but with normal functions. Herein, individual SCNs were mapped and analyzed for seven pediatric patients with epilepsy after surgery and 15 age-matched healthy controls. A whole-brain individual SCN was constructed based on an automated anatomical labeling template, and global and nodal network metrics were calculated for statistical analyses. Small-world properties were exhibited by pediatric patients after brain surgery and by healthy controls. After brain surgery, pediatric patients with epilepsy exhibited a higher shortest path length, lower global efficiency, and higher nodal efficiency in the cuneus than those in healthy controls. These results revealed that pediatric epilepsy after brain surgery, even with normal functions, showed altered topological organization of the individual SCNs, which revealed residual network topological abnormalities and may provide initial evidence for the underlying functional impairments in the brain of pediatric patients with epilepsy after surgery that can occur in the future.
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Affiliation(s)
- Liang Zhang
- Department of Neurosurgery, Wuxi Clinical College of Anhui Medical University (The 904th Hospital of PLA), Wuxi, Jiangsu Province 214044, China
| | - Bei Zhuang
- Department of Anesthesiology, Wuxi Clinical College of Anhui Medical University (The 904th Hospital of PLA), Wuxi, Jiangsu Province 214044, China
| | - Mengyuan Wang
- Department of Nursing, Wuxi Clinical College of Anhui Medical University (The 904th Hospital of PLA), Wuxi, Jiangsu Province 214044, China
| | - Jie Zhu
- Department of Neurosurgery, Wuxi Clinical College of Anhui Medical University (The 904th Hospital of PLA), Wuxi, Jiangsu Province 214044, China
| | - Tao Chen
- Department of Neurosurgery, Wuxi Clinical College of Anhui Medical University (The 904th Hospital of PLA), Wuxi, Jiangsu Province 214044, China
| | - Yang Yang
- Department of Neurosurgery, Wuxi Clinical College of Anhui Medical University (The 904th Hospital of PLA), Wuxi, Jiangsu Province 214044, China
| | - Haoting Shi
- Department of Neurosurgery, Wuxi Clinical College of Anhui Medical University (The 904th Hospital of PLA), Wuxi, Jiangsu Province 214044, China
| | - Xiaoming Zhu
- Department of Neurosurgery, Wuxi Clinical College of Anhui Medical University (The 904th Hospital of PLA), Wuxi, Jiangsu Province 214044, China
| | - Li Ma
- Department of Neurosurgery, Wuxi Clinical College of Anhui Medical University (The 904th Hospital of PLA), Wuxi, Jiangsu Province 214044, China
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Kang J, Mao W, Wu J, Huang X, Casanova MF, Sokhadze EM, Li X, Geng X. Development of EEG connectivity from preschool to school-age children. Front Neurosci 2024; 17:1277786. [PMID: 38274502 PMCID: PMC10808652 DOI: 10.3389/fnins.2023.1277786] [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: 08/15/2023] [Accepted: 12/28/2023] [Indexed: 01/27/2024] Open
Abstract
Introduction Many studies have collected normative developmental EEG data to better understand brain function in early life and associated changes during both aging and pathology. Higher cognitive functions of the brain do not normally stem from the workings of a single brain region that works but, rather, on the interaction between different brain regions. In this regard studying the connectivity between brain regions is of great importance towards understanding higher cognitive functions and its underlying mechanisms. Methods In this study, EEG data of children (N = 253; 3-10 years old; 113 females, 140 males) from pre-school to schoolage was collected, and the weighted phase delay index and directed transfer function method was used to find the electrophysiological indicators of both functional connectivity and effective connectivity. A general linear model was built between the indicators and age, and the change trend of electrophysiological indicators analyzed for age. Results The results showed an age trend for the functional and effective connectivity of the brain of children. Discussion The results are of importance in understanding normative brain development and in defining those conditions that deviate from typical growth trajectories.
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Affiliation(s)
- Jiannan Kang
- Child Rehabilitation Division, Ningbo Rehabilitation Hospital, Ningbo, China
| | - Wenqin Mao
- Child Rehabilitation Division, Ningbo Rehabilitation Hospital, Ningbo, China
| | - Juanmei Wu
- Child Rehabilitation Division, Ningbo Rehabilitation Hospital, Ningbo, China
| | - Xinping Huang
- Child Rehabilitation Division, Ningbo Rehabilitation Hospital, Ningbo, China
| | - Manuel F. Casanova
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville Campus, Prisma Health System, Greenville, SC, United States
| | - Estate M. Sokhadze
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville Campus, Prisma Health System, Greenville, SC, United States
- Duke University, Durham, NC, United States
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xinling Geng
- School of Biomedical Engineering, Capital Medical University, Beijing, China
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3
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Wang Q, Wang Y, Xu W, Chen X, Li X, Li Q, Li H. Corresponding anatomical of the macaque superior parietal lobule areas 5 (PE) subdivision reveal similar connectivity patterns with humans. Front Neurosci 2022; 16:964310. [PMID: 36267237 PMCID: PMC9577089 DOI: 10.3389/fnins.2022.964310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 08/26/2022] [Indexed: 11/25/2022] Open
Abstract
Using the animal brain as a cross-species tool for human brain research based on imaging features can provide more potential to reveal comprehensive human brain analysis. Previous studies have shown that human Brodmann area 5 (BA5) and macaque PE are homologous regions. They are both involved in processes depth and direction information during the touch process in the arm movement. However, recent studies show that both BA5 and PE are not homogeneous. According to the cytoarchitecture, BA5 is subdivided into three different subregions, and PE can be subdivided into PEl, PEla, and PEm. The species homologous relationship among the subregions is not clear between BA5 and PE. At the same time, the subdivision of PE based on the anatomical connection of white matter fiber bundles needs more verification. This research subdivided the PE of macaques based on the anatomical connection of white matter fiber bundles. Two PE subregions are defined based on probabilistic fiber tracking, one on the anterior side and the other on the dorsal side. Finally, the research draws connectivity fingerprints with predefined homologous target areas for the BA5 and PE subregions to reveal the characteristics of structure and functions and gives the homologous correspondence identified.
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Wang J, Wang Z, Zhang H, Feng S, Lu Y, Wang S, Wang H, Sun YE, Chen Y. White Matter Structural and Network Topological Changes Underlying the Behavioral Phenotype of MECP2 Mutant Monkeys. Cereb Cortex 2021; 31:5396-5410. [PMID: 34117744 DOI: 10.1093/cercor/bhab166] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 05/12/2021] [Accepted: 05/21/2021] [Indexed: 11/13/2022] Open
Abstract
To explore the brain structural basis underlying the behavioral abnormalities associated with Rett syndrome (RTT), we carried out detailed longitudinal noninvasive magnetic resonance imaging analyses of RTT monkey models created by gene-editing, from weaning, through adolescence, till sexual maturation. Here, we report abnormal developmental dynamics of brain white matter (WM) microstructures and network topological organizations via diffusion tensor imaging. Specifically, disrupted WM microstructural integrity was observed at 9 months, but recovered thereafter, whereas WM network topological properties showed persistent abnormal dynamics from 9 to 37 months. Changes in the WM microstructure and WM network topology were correlated well with RTT-associated behavioral abnormalities including sleep latency, environmental exploration, and conflict encounters. Deleterious and protracted early WM myelination process likely lead to abnormal synaptic pruning, resulting in poor functional segregations. Together, this study provides initial evidence for changes in WM microstructure and network topological organization, which may underlie the neuro-patho-etilogy of RTT.
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Affiliation(s)
- Jiaojian Wang
- Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhengbo Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
| | - Hongjiang Zhang
- Department of MRI, The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology, Kunming 650032, China
| | - Shufei Feng
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
| | - Yi Lu
- The Department of Medical Imaging, The First Affiliated Hospital of Kunming Medical University, Kunming 650500, China
| | - Shuang Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
| | - Hong Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
| | - Yi Eve Sun
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China
| | - Yongchang Chen
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
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Shen D, Li Q, Liu J, Liao Y, Li Y, Gong Q, Huang X, Li T, Li J, Qiu C, Hu J. The Deficits of Individual Morphological Covariance Network Architecture in Schizophrenia Patients With and Without Violence. Front Psychiatry 2021; 12:777447. [PMID: 34867559 PMCID: PMC8634443 DOI: 10.3389/fpsyt.2021.777447] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 10/18/2021] [Indexed: 02/05/2023] Open
Abstract
Background: Schizophrenia is associated with a significant increase in the risk of violence, which constitutes a public health concern and contributes to stigma associated with mental illness. Although previous studies revealed structural and functional abnormalities in individuals with violent schizophrenia (VSZ), the neural basis of psychotic violence remains controversial. Methods: In this study, high-resolution structural magnetic resonance imaging (MRI) data were acquired from 18 individuals with VSZ, 23 individuals with non-VSZ (NSZ), and 22 age- and sex-matched healthy controls (HCs). Whole-brain voxel-based morphology and individual morphological covariance networks were analysed to reveal differences in gray matter volume (GMV) and individual morphological covariance network topology. Relationships among abnormal GMV, network topology, and clinical assessments were examined using correlation analyses. Results: GMV in the hypothalamus gradually decreased from HCs and NSZ to VSZ and showed significant differences between all pairs of groups. Graph theory analyses revealed that morphological covariance networks of HCs, NSZ, and VSZ exhibited small worldness. Significant differences in network topology measures, including global efficiency, shortest path length, and nodal degree, were found. Furthermore, changes in GMV and network topology were closely related to clinical performance in the NSZ and VSZ groups. Conclusions: These findings revealed the important role of local structural abnormalities of the hypothalamus and global network topological impairments in the neuropathology of NSZ and VSZ, providing new insight into the neural basis of and markers for VSZ and NSZ to facilitate future accurate clinical diagnosis and targeted treatment.
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Affiliation(s)
- Danlin Shen
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
| | - Qing Li
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
| | | | - Yi Liao
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Yuanyuan Li
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
| | - Xiaoqi Huang
- Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
| | - Tao Li
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China.,Affiliated Mental Health Center, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jing Li
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
| | - Changjian Qiu
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
| | - Junmei Hu
- School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, China
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6
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He B, Cao L, Xia X, Zhang B, Zhang D, You B, Fan L, Jiang T. Fine-Grained Topography and Modularity of the Macaque Frontal Pole Cortex Revealed by Anatomical Connectivity Profiles. Neurosci Bull 2020; 36:1454-1473. [PMID: 33108588 PMCID: PMC7719154 DOI: 10.1007/s12264-020-00589-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 07/30/2020] [Indexed: 11/25/2022] Open
Abstract
The frontal pole cortex (FPC) plays key roles in various higher-order functions and is highly developed in non-human primates. An essential missing piece of information is the detailed anatomical connections for finer parcellation of the macaque FPC than provided by the previous tracer results. This is important for understanding the functional architecture of the cerebral cortex. Here, combining cross-validation and principal component analysis, we formed a tractography-based parcellation scheme that applied a machine learning algorithm to divide the macaque FPC (2 males and 6 females) into eight subareas using high-resolution diffusion magnetic resonance imaging with the 9.4T Bruker system, and then revealed their subregional connections. Furthermore, we applied improved hierarchical clustering to the obtained parcels to probe the modular structure of the subregions, and found that the dorsolateral FPC, which contains an extension to the medial FPC, was mainly connected to regions of the default-mode network. The ventral FPC was mainly involved in the social-interaction network and the dorsal FPC in the metacognitive network. These results enhance our understanding of the anatomy and circuitry of the macaque brain, and contribute to FPC-related clinical research.
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Affiliation(s)
- Bin He
- School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China
| | - Long Cao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Xiaoluan Xia
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030600, China
| | - Baogui Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China
| | - Dan Zhang
- Core Facility, Center of Biomedical Analysis, Tsinghua University, Beijing, 100084, China
| | - Bo You
- School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China.
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China. .,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, CAS, Beijing, 100190, China. .,University of CAS, Beijing, 100049, China.
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China. .,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, CAS, Beijing, 100190, China. .,Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China. .,The Queensland Brain Institute, University of Queensland, Brisbane, QLD, 4072, Australia. .,University of CAS, Beijing, 100049, China. .,Chinese Institute for Brain Research, Beijing, 102206, China.
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7
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Wang J, Yang Y, Zhao X, Zuo Z, Tan LH. Evolutional and developmental anatomical architecture of the left inferior frontal gyrus. Neuroimage 2020; 222:117268. [PMID: 32818615 DOI: 10.1016/j.neuroimage.2020.117268] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 07/17/2020] [Accepted: 08/12/2020] [Indexed: 12/21/2022] Open
Abstract
The left inferior frontal gyrus (IFG) including Broca's area is involved in the processing of many language subdomains, and thus, research on the evolutional and human developmental characteristics of the left IFG will shed light on how language emerges and maturates. In this study, we used diffusion magnetic resonance imaging (dMRI) and resting-state functional MRI (fMRI) to investigate the evolutional and developmental patterns of the left IFG in humans (age 6-8, age 11-13, and age 16-18 years) and macaques. Tractography-based parcellation was used to define the subcomponents of left IFG and consistently identified four subregions in both humans and macaques. This parcellation scheme for left IFG in human was supported by specific coactivation patterns and functional characterization for each subregion. During evolution and development, we found increased functional balance, amplitude of low frequency fluctuations, functional integration, and functional couplings. We also observed higher fractional anisotropy values, i.e. better myelination of dorsal and ventral white matter language pathways during evolution and development. We assume that the resting-state functional connectivity and task-related coactivation mapping are associated with hierarchical language processing. Our findings have shown the evolutional and human developmental patterns of left IFG, and will contribute to the understanding of how the human language evolves and how atypical language developmental disorders may occur.
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Affiliation(s)
- Jiaojiang Wang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 625014, China; Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen 518057, China.
| | - Yang Yang
- CAS Key Laboratory of Behavioral Science, Center for Brain Science and Learning Difficulties, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Xudong Zhao
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhentao Zuo
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
| | - Li-Hai Tan
- Guangdong-Hongkong-Macau Institute of CNS Regeneration and Ministry of Education CNS Regeneration Collaborative Joint Laboratory, Jinan University, Guangzhou 510632, China; Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen 518057, China.
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8
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He B, Yang Z, Fan L, Gao B, Li H, Ye C, You B, Jiang T. MonkeyCBP: A Toolbox for Connectivity-Based Parcellation of Monkey Brain. Front Neuroinform 2020; 14:14. [PMID: 32410977 PMCID: PMC7198896 DOI: 10.3389/fninf.2020.00014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 03/10/2020] [Indexed: 01/24/2023] Open
Abstract
Non-human primate models are widely used in studying the brain mechanism underlying brain development, cognitive functions, and psychiatric disorders. Neuroimaging techniques, such as magnetic resonance imaging, play an important role in the examinations of brain structure and functions. As an indispensable tool for brain imaging data analysis, brain atlases have been extensively investigated, and a variety of versions constructed. These atlases diverge in the criteria based on which they are plotted. The criteria range from cytoarchitectonic features, neurotransmitter receptor distributions, myelination fingerprints, and transcriptomic patterns to structural and functional connectomic profiles. Among them, brainnetome atlas is tightly related to brain connectome information and built by parcellating the brain on the basis of the anatomical connectivity profiles derived from structural neuroimaging data. The pipeline for building the brainnetome atlas has been published as a toolbox named ATPP (A Pipeline for Automatic Tractography-Based Brain Parcellation). In this paper, we present a variation of ATPP, which is dedicated to monkey brain parcellation, to address the significant differences in the process between the two species. The new toolbox, MonkeyCBP, has major alterations in three aspects: brain extraction, image registration, and validity indices. By parcellating two different brain regions (posterior cingulate cortex) and (frontal pole) of the rhesus monkey, we demonstrate the efficacy of these alterations. The toolbox has been made public (https://github.com/bheAI/MonkeyCBP_CLI, https://github.com/bheAI/MonkeyCBP_GUI). It is expected that the toolbox can benefit the non-human primate neuroimaging community with high-throughput computation and low labor involvement.
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Affiliation(s)
- Bin He
- School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, China
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Zhengyi Yang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Bin Gao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Hai Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Chuyang Ye
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Bo You
- School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
- Chinese Institute for Brain Research, Beijing, China
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9
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Wei Q, Bai T, Brown EC, Xie W, Chen Y, Ji G, Ramasubbu R, Tian Y, Wang K. Thalamocortical connectivity in electroconvulsive therapy for major depressive disorder. J Affect Disord 2020; 264:163-171. [PMID: 32056746 DOI: 10.1016/j.jad.2019.11.120] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Revised: 09/28/2019] [Accepted: 11/28/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND Electroconvulsive therapy (ECT) can lead to rapid and effective responses in major depressive disorder (MDD). However, the precise neural mechanisms of ECT for MDD are still unclear. Previous work has confirmed that thalamocortical circuits play an important role in emotion and cognition. However, the relationship between mechanisms of ECT for MDD and thalamocortical connectivity has not yet been investigated. METHOD Thalamocortical functional connectivity analysis was performed on resting-state functional magnetic resonance imaging (fMRI) data collected from 28 MDD patients both pre- and post-ECT treatment, as well as 20 healthy controls. The cortex was parceled into six regions of interest (ROIs), which were used as seeds to assess the functional connectivity between the cortex and each voxel in the thalamus. Then, functional connectivity between the identified thalamic subregions and the rest of the brain was quantified to better localize thalamocortical connectivity related to ECT. Structural connectivity among the functionally abnormal regions was also determined using probabilistic tractography from diffusion tensor imaging (DTI) data. RESULTS There was decreased parietal cortex-left pulvinar and left pulvinar-bilateral precuneus functional connectivity in post-ECT MDD patients, compared to pre-ECT MDD patients. Furthermore, functional connectivity strength of parietal cortex-left pulvinar and left pulvinar-bilateral precuneus was negative correlation with verbal fluency test scores in post-ECT MDD patients. No significant change was found in structural connectivity analysis. LIMITATIONS The sample size of our study was not large. CONCLUSION Our findings implicate that the specific abnormalities in thalamocortical circuit may be associated with cognitive impairment induced by ECT.
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Affiliation(s)
- Qiang Wei
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Tongjian Bai
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Elliot C Brown
- Charité-Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Neuroscience Research Center, Berlin Institute of Health, Berlin, Germany; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada; Department of Psychiatry, University of Calgary, Calgary, AB, Canada; Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Wen Xie
- Anhui Mental Health Center, Hefei, China
| | - Yang Chen
- Anhui Mental Health Center, Hefei, China
| | - Gongjun Ji
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Rajamannar Ramasubbu
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada; Department of Psychiatry, University of Calgary, Calgary, AB, Canada; Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Yanghua Tian
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China.
| | - Kai Wang
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China.
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10
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Xu J, Lyu H, Li T, Xu Z, Fu X, Jia F, Wang J, Hu Q. Delineating functional segregations of the human middle temporal gyrus with resting-state functional connectivity and coactivation patterns. Hum Brain Mapp 2019; 40:5159-5171. [PMID: 31423713 PMCID: PMC6865466 DOI: 10.1002/hbm.24763] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Revised: 07/25/2019] [Accepted: 07/31/2019] [Indexed: 12/25/2022] Open
Abstract
Although the middle temporal gyrus (MTG) has been parcellated into subregions with distinguished anatomical connectivity patterns, whether the structural topography of MTG can inform functional segregations of this area remains largely unknown. Accumulating evidence suggests that the brain's underlying organization and function can be directly and effectively delineated with resting-state functional connectivity (RSFC) by identifying putative functional boundaries between cortical areas. Here, RSFC profiles were used to explore functional segregations of the MTG and defined four subregions from anterior to posterior in two independent datasets, which showed a similar pattern with MTG parcellation scheme obtained using anatomical connectivity. The functional segregations of MTG were further supported by whole brain RSFC, coactivation, and specific RFSC, and coactivation mapping. Furthermore, the fingerprint with predefined 10 networks and functional characterizations of each subregion using meta-analysis also identified functional distinction between subregions. The specific connectivity analysis and functional characterization indicated that the bilateral most anterior subregions mainly participated in social cognition and semantic processing; the ventral middle subregions were involved in social cognition in left hemisphere and auditory processing in right hemisphere; the bilateral ventro-posterior subregions participated in action observation, whereas the left subregion was also involved in semantic processing; both of the dorsal subregions in superior temporal sulcus were involved in language, social cognition, and auditory processing. Taken together, our findings demonstrated MTG sharing similar structural and functional topographies and provide more detailed information about the functional organization of the MTG, which may facilitate future clinical and cognitive research on this area.
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Affiliation(s)
- Jinping Xu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
- University of Chinese Academy of SciencesBeijingChina
| | - Hanqing Lyu
- Radiology DepartmentShenzhen Traditional Chinese Medicine HospitalShenzhenChina
| | - Tian Li
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Ziyun Xu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Xianjun Fu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Fucang Jia
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
| | - Jiaojian Wang
- Key Laboratory for NeuroInformation of the Ministry of EducationSchool of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Qingmao Hu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
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11
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Corresponding anatomical and coactivation architecture of the human precuneus showing similar connectivity patterns with macaques. Neuroimage 2019; 200:562-574. [DOI: 10.1016/j.neuroimage.2019.07.001] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Revised: 06/03/2019] [Accepted: 07/01/2019] [Indexed: 12/22/2022] Open
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12
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Xia X, Fan L, Hou B, Zhang B, Zhang D, Cheng C, Deng H, Dong Y, Zhao X, Li H, Jiang T. Fine-Grained Parcellation of the Macaque Nucleus Accumbens by High-Resolution Diffusion Tensor Tractography. Front Neurosci 2019; 13:709. [PMID: 31354418 PMCID: PMC6635473 DOI: 10.3389/fnins.2019.00709] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 06/24/2019] [Indexed: 11/13/2022] Open
Abstract
Limited in part by the spatial resolution of typical in vivo magnetic resonance imaging (MRI) data, recent neuroimaging studies have only identified a connectivity-based shell-core-like partitioning of the nucleus accumbens (Acb) in humans. This has hindered the process of making a more refined description of the Acb using non-invasive neuroimaging technologies and approaches. In this study, high-resolution ex vivo macaque brain diffusion MRI data were acquired to investigate the tractography-based parcellation of the Acb. Our results identified a shell-core-like partitioning in macaques that is similar to that in humans as well as an alternative solution that subdivided the Acb into four parcels, the medial shell, the lateral shell, the ventral core, and the dorsal core. Furthermore, we characterized the specific anatomical and functional connectivity profiles of these Acb subregions and generalized their specialized functions to establish a fine-grained macaque Acb brainnetome atlas. This atlas should be helpful in neuroimaging, stereotactic surgery, and comparative neuroimaging studies to reveal the neurophysiological substrates of various diseases and cognitive functions associated with the Acb.
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Affiliation(s)
- Xiaoluan Xia
- College of Information and Computer, Taiyuan University of Technology, Jinzhong, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Bing Hou
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Baogui Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Dan Zhang
- Core Facility, Center of Biomedical Analysis, Tsinghua University, Beijing, China
| | - Chen Cheng
- College of Information and Computer, Taiyuan University of Technology, Jinzhong, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Hongxia Deng
- College of Information and Computer, Taiyuan University of Technology, Jinzhong, China
| | - Yunyun Dong
- College of Information and Computer, Taiyuan University of Technology, Jinzhong, China
| | - Xudong Zhao
- State Key Laboratory of Brain and Cognitive Sciences, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Haifang Li
- College of Information and Computer, Taiyuan University of Technology, Jinzhong, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
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13
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Wang L, Xu J, Wang C, Wang J. Whole Brain Functional Connectivity Pattern Homogeneity Mapping. Front Hum Neurosci 2018; 12:164. [PMID: 29740305 PMCID: PMC5928195 DOI: 10.3389/fnhum.2018.00164] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 04/09/2018] [Indexed: 01/13/2023] Open
Abstract
Mounting studies have demonstrated that brain functions are determined by its external functional connectivity patterns. However, how to characterize the voxel-wise similarity of whole brain functional connectivity pattern is still largely unknown. In this study, we introduced a new method called functional connectivity homogeneity (FcHo) to delineate the voxel-wise similarity of whole brain functional connectivity patterns. FcHo was defined by measuring the whole brain functional connectivity patterns similarity of a given voxel with its nearest 26 neighbors using Kendall's coefficient concordance (KCC). The robustness of this method was tested in four independent datasets selected from a large repository of MRI. Furthermore, FcHo mapping results were further validated using the nearest 18 and six neighbors and intra-subject reproducibility with each subject scanned two times. We also compared FcHo distribution patterns with local regional homogeneity (ReHo) to identify the similarity and differences of the two methods. Finally, FcHo method was used to identify the differences of whole brain functional connectivity patterns between professional Chinese chess players and novices to test its application. FcHo mapping consistently revealed that the high FcHo was mainly distributed in association cortex including parietal lobe, frontal lobe, occipital lobe and default mode network (DMN) related areas, whereas the low FcHo was mainly found in unimodal cortex including primary visual cortex, sensorimotor cortex, paracentral lobule and supplementary motor area. These results were further supported by analyses of the nearest 18 and six neighbors and intra-subject similarity. Moreover, FcHo showed both similar and different whole brain distribution patterns compared to ReHo. Finally, we demonstrated that FcHo can effectively identify the whole brain functional connectivity pattern differences between professional Chinese chess players and novices. Our findings indicated that FcHo is a reliable method to delineate the whole brain functional connectivity pattern similarity and may provide a new way to study the functional organization and to reveal neuropathological basis for brain disorders.
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Affiliation(s)
- Lijie Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jinping Xu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Chao Wang
- College of Psychology and Sociology, Shenzhen University, Shenzhen, China
| | - Jiaojian Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, China
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