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Hu X, Cheng B, Tang Y, Long T, Huang Y, Li P, Song Y, Song X, Li K, Yin Y, Chen X. Gray matter volume and corresponding covariance connectivity are biomarkers for major depressive disorder. Brain Res 2024; 1837:148986. [PMID: 38714227 DOI: 10.1016/j.brainres.2024.148986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 04/06/2024] [Accepted: 05/04/2024] [Indexed: 05/09/2024]
Abstract
The major depressive disorder (MDD) is a common and severe mental disorder. To identify a reliable biomarker for MDD is important for early diagnosis and prevention. Given easy access and high reproducibility, the structural magnetic resonance imaging (sMRI) is an ideal method to identify the biomarker for depression. In this study, sMRI data of first episode, treatment-naïve 66 MDD patients and 54 sex-, age-, and education-matched healthy controls (HC) were used to identify the differences in gray matter volume (GMV), group-level, individual-level covariance connections. Finally, the abnormal GMV and individual covariance connections were applied to classify MDD from HC. MDD patients showed higher GMV in middle occipital gyrus (MOG) and precuneus (PCun), and higher structural covariance connections between MOG and PCun. In addition, the Allen Human Brain Atlas (AHBA) was applied and revealed the genetic basis for the changes of gray matter volume. Importantly, we reported that GMV in MOG, PCun and structural covariance connectivity between MOG and PCun are able to discriminate MDD from HC. Our results revealed structural underpinnings for MDD, which may contribute towards early discriminating for depression.
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Affiliation(s)
- Xiao Hu
- Department of Rehabilitation Medicine, West China Second University Hospital, Sichuan University, Chengdu 610041, China; Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu 610041, China
| | - Bochao Cheng
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, China; Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Yuying Tang
- Department of Rehabilitation Medicine, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Tong Long
- Department of Rehabilitation Medicine, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Yan Huang
- Department of Rehabilitation Medicine, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Pei Li
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Yu Song
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Xiyang Song
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, China
| | - Kun Li
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu 610041, China
| | - Yijie Yin
- School of Sociality and Psychology, Southwest Minzu University, Chengdu 610041, China
| | - Xijian Chen
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu 610041, China.
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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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3
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Koc E, Kalkan H, Bilgen S. Autism Spectrum Disorder Detection by Hybrid Convolutional Recurrent Neural Networks from Structural and Resting State Functional MRI Images. AUTISM RESEARCH AND TREATMENT 2023; 2023:4136087. [PMID: 38152612 PMCID: PMC10752691 DOI: 10.1155/2023/4136087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 10/19/2023] [Accepted: 11/22/2023] [Indexed: 12/29/2023]
Abstract
This study aims to increase the accuracy of autism spectrum disorder (ASD) diagnosis based on cognitive and behavioral phenotypes through multiple neuroimaging modalities. We apply machine learning (ML) algorithms to classify ASD patients and healthy control (HC) participants using structural magnetic resonance imaging (s-MRI) together with resting state functional MRI (rs-f-MRI and f-MRI) data from the large multisite data repository ABIDE (autism brain imaging data exchange) and identify important brain connectivity features. The 2D f-MRI images were converted into 3D s-MRI images, and datasets were preprocessed using the Montreal Neurological Institute (MNI) atlas. The data were then denoised to remove any confounding factors. We show, by using three fusion strategies such as early fusion, late fusion, and cross fusion, that, in this implementation, hybrid convolutional recurrent neural networks achieve better performance in comparison to either convolutional neural networks (CNNs) or recurrent neural networks (RNNs). The proposed model classifies subjects as autistic or not according to how functional and anatomical connectivity metrics provide an overall diagnosis based on the autism diagnostic observation schedule (ADOS) standard. Our hybrid network achieved an accuracy of 96% by fusing s-MRI and f-MRI together, which outperforms the methods used in previous studies.
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Affiliation(s)
- Emel Koc
- Istanbul Okan University, Istanbul, Türkiye
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Xiong Y, Hong H, Liu C, Zhang YQ. Social isolation and the brain: effects and mechanisms. Mol Psychiatry 2023; 28:191-201. [PMID: 36434053 PMCID: PMC9702717 DOI: 10.1038/s41380-022-01835-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 10/05/2022] [Accepted: 10/10/2022] [Indexed: 11/26/2022]
Abstract
An obvious consequence of the coronavirus disease (COVID-19) pandemic is the worldwide reduction in social interaction, which is associated with many adverse effects on health in humans from babies to adults. Although social development under normal or isolated environments has been studied since the 1940s, the mechanism underlying social isolation (SI)-induced brain dysfunction remains poorly understood, possibly due to the complexity of SI in humans and translational gaps in findings from animal models. Herein, we present a systematic review that focused on brain changes at the molecular, cellular, structural and functional levels induced by SI at different ages and in different animal models. SI studies in humans and animal models revealed common socioemotional and cognitive deficits caused by SI in early life and an increased occurrence of depression and anxiety induced by SI during later stages of life. Altered neurotransmission and neural circuitry as well as abnormal development and function of glial cells in specific brain regions may contribute to the abnormal emotions and behaviors induced by SI. We highlight distinct alterations in oligodendrocyte progenitor cell differentiation and oligodendrocyte maturation caused by SI in early life and later stages of life, respectively, which may affect neural circuit formation and function and result in diverse brain dysfunctions. To further bridge animal and human SI studies, we propose alternative animal models with brain structures and complex social behaviors similar to those of humans.
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Affiliation(s)
- Ying Xiong
- grid.9227.e0000000119573309State Key Laboratory of Molecular and Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101 China
| | - Huilin Hong
- grid.9227.e0000000119573309State Key Laboratory of Molecular and Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101 China
| | - Cirong Liu
- grid.9227.e0000000119573309Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031 China ,grid.511008.dShanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, 201210 China
| | - Yong Q. Zhang
- grid.9227.e0000000119573309State Key Laboratory of Molecular and Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, 100101 China ,grid.410726.60000 0004 1797 8419University of the Chinese Academy of Sciences, Beijing, 100101 China
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Lean RE, Smyser CD, Brady RG, Triplett RL, Kaplan S, Kenley JK, Shimony JS, Smyser TA, Miller JP, Barch DM, Luby JL, Warner BB, Rogers CE. Prenatal exposure to maternal social disadvantage and psychosocial stress and neonatal white matter connectivity at birth. Proc Natl Acad Sci U S A 2022; 119:e2204135119. [PMID: 36219693 PMCID: PMC9586270 DOI: 10.1073/pnas.2204135119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 09/14/2022] [Indexed: 11/18/2022] Open
Abstract
Early life adversity (social disadvantage and psychosocial stressors) is associated with altered microstructure in fronto-limbic pathways important for socioemotional development. Understanding when these associations begin to emerge may inform the timing and design of preventative interventions. In this longitudinal study, 399 mothers were oversampled for low income and completed social background measures during pregnancy. Measures were analyzed with structural equation analysis resulting in two latent factors: social disadvantage (education, insurance status, income-to-needs ratio [INR], neighborhood deprivation, and nutrition) and psychosocial stress (depression, stress, life events, and racial discrimination). At birth, 289 healthy term-born neonates underwent a diffusion MRI (dMRI) scan. Mean diffusivity (MD) and fractional anisotropy (FA) were measured for the dorsal and inferior cingulum bundle (CB), uncinate, and fornix using probabilistic tractography in FSL. Social disadvantage and psychosocial stress were fitted to dMRI parameters using regression models adjusted for infant postmenstrual age at scan and sex. Social disadvantage, but not psychosocial stress, was independently associated with lower MD in the bilateral inferior CB and left uncinate, right fornix, and lower MD and higher FA in the right dorsal CB. Results persisted after accounting for maternal medical morbidities and prenatal drug exposure. In moderation analysis, psychosocial stress was associated with lower MD in the left inferior CB among the lower-to-higher socioeconomic status (SES) (INR ≥ 200%) group, but not the extremely low SES (INR < 200%) group. Increasing access to social welfare programs that reduce the burden of social disadvantage and related psychosocial stressors may be an important target to protect fetal brain development in fronto-limbic pathways.
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Affiliation(s)
- Rachel E. Lean
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO 63110
| | - Christopher D. Smyser
- Department of Pediatrics, Washington University School of Medicine in St. Louis, St. Louis, MO 63110
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110
| | - Rebecca G. Brady
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110
| | - Regina L. Triplett
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110
| | - Sydney Kaplan
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110
| | - Jeanette K. Kenley
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110
| | - Joshua S. Shimony
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110
| | - Tara A. Smyser
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO 63110
| | - J. Phillip Miller
- Department of Biostatistics, Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, MO 63110
| | - Deanna M. Barch
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO 63110
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO 63110
- Department of Psychological and Brain Sciences, Washington University School of Medicine in St. Louis, St. Louis, MO 63130
| | - Joan L. Luby
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO 63110
| | - Barbara B. Warner
- Department of Pediatrics, Washington University School of Medicine in St. Louis, St. Louis, MO 63110
- Department of Newborn Medicine, Washington University School of Medicine in St. Louis, St. Louis, MO 63110
| | - Cynthia E. Rogers
- Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO 63110
- Department of Pediatrics, Washington University School of Medicine in St. Louis, St. Louis, MO 63110
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Zhang J, Zhao T, Zhang J, Zhang Z, Li H, Cheng B, Pang Y, Wu H, Wang J. Prediction of childhood maltreatment and subtypes with personalized functional connectome of large-scale brain networks. Hum Brain Mapp 2022; 43:4710-4721. [PMID: 35735128 PMCID: PMC9491288 DOI: 10.1002/hbm.25985] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/12/2022] [Accepted: 05/24/2022] [Indexed: 12/20/2022] Open
Abstract
Childhood maltreatment (CM) has a long impact on physical and mental health of children. However, the neural underpinnings of CM are still unclear. In this study, we aimed to establish the associations between functional connectome of large‐scale brain networks and influences of CM evaluated through Childhood Trauma Questionnaire (CTQ) at the individual level based on resting‐state functional magnetic resonance imaging data of 215 adults. A novel individual functional mapping approach was employed to identify subject‐specific functional networks and functional network connectivities (FNCs). A connectome‐based predictive modeling (CPM) was used to estimate CM total and subscale scores using individual FNCs. The CPM established with FNCs can well predict CM total scores and subscale scores including emotion abuse, emotion neglect, physical abuse, physical neglect, and sexual abuse. These FNCs primarily involve default mode network, fronto‐parietal network, visual network, limbic network, motor network, dorsal and ventral attention networks, and different networks have distinct contributions to predicting CM and subtypes. Moreover, we found that CM showed age and sex effects on individual functional connections. Taken together, the present findings revealed that different types of CM are associated with different atypical neural networks which provide new clues to understand the neurobiological consequences of childhood adversity.
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Affiliation(s)
- Jiang Zhang
- College of Electrical EngineeringSichuan UniversityChengduChina
- Med‐X Center for InformaticsSichuan UniversityChengduChina
| | - Tianyu Zhao
- College of Electrical EngineeringSichuan UniversityChengduChina
| | - Jingyue Zhang
- College of Electrical EngineeringSichuan UniversityChengduChina
| | - Zhiwei Zhang
- College of Electrical EngineeringSichuan UniversityChengduChina
| | - Hongming Li
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Bochao Cheng
- Department of RadiologyWest China Second University Hospital of Sichuan UniversityChengduChina
| | - Yajing Pang
- School of Electrical EngineeringZhengzhou UniversityZhengzhouChina
| | - Huawang Wu
- The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital)GuangzhouChina
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational MedicineKunming University of Science and TechnologyKunmingChina
- Yunnan Key Laboratory of Primate Biomedical ResearchKunmingYunnanChina
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Repeated Maternal Separation Prolongs the Critical Period of Ocular Dominance Plasticity in Mouse Visual Cortex. Neurosci Lett 2022; 776:136577. [DOI: 10.1016/j.neulet.2022.136577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/07/2022] [Accepted: 03/08/2022] [Indexed: 11/18/2022]
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Gao J, Chen M, Li Y, Gao Y, Li Y, Cai S, Wang J. Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks. Front Neurosci 2021; 14:629630. [PMID: 33584183 PMCID: PMC7877487 DOI: 10.3389/fnins.2020.629630] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 12/29/2020] [Indexed: 12/18/2022] Open
Abstract
Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders with behavioral and cognitive impairment and brings huge burdens to the patients' families and the society. To accurately identify patients with ASD from typical controls is important for early detection and early intervention. However, almost all the current existing classification methods for ASD based on structural MRI (sMRI) mainly utilize the independent local morphological features and do not consider the covariance patterns of these features between regions. In this study, by combining the convolutional neural network (CNN) and individual structural covariance network, we proposed a new framework to classify ASD patients with sMRI data from the ABIDE consortium. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to characterize the weight of features contributing to the classification. The experimental results showed that our proposed method outperforms the currently used methods for classifying ASD patients with the ABIDE data and achieves a high classification accuracy of 71.8% across different sites. Furthermore, the discriminative features were found to be mainly located in the prefrontal cortex and cerebellum, which may be the early biomarkers for the diagnosis of ASD. Our study demonstrated that CNN is an effective tool to build the framework for the diagnosis of ASD with individual structural covariance brain network.
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Affiliation(s)
- Jingjing Gao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Mingren Chen
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuanyuan Li
- 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
| | - Yachun Gao
- School of Physics, University of Electronic Science and Technology of China, Chengdu, China
| | - Yanling Li
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, China
| | - Shimin Cai
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Jiaojian Wang
- 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
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
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9
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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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10
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Altered cortical gray matter volume and functional connectivity after transcutaneous spinal cord direct current stimulation in idiopathic restless legs syndrome. Sleep Med 2020; 74:254-261. [DOI: 10.1016/j.sleep.2020.07.026] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 07/18/2020] [Accepted: 07/20/2020] [Indexed: 01/18/2023]
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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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