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      Connectomics-based resting-state functional network alterations predict suicidality in major depressive disorder

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      1 , 1 , 1 , 2 , 1 , 1 , 2 , 3 , 1 , 2 , 3 , , on behalf of REST-meta-MDD Consortium
      Translational Psychiatry
      Nature Publishing Group UK
      Depression, Diagnostic markers

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          Abstract

          Suicidal behavior is a major concern for patients who suffer from major depressive disorder (MDD). However, dynamic alterations and dysfunction of resting-state networks (RSNs) in MDD patients with suicidality have remained unclear. Thus, we investigated whether subjects with different severity of suicidal ideation and suicidal behavior may have different disturbances in brain RSNs and whether these changes could be used as the diagnostic biomarkers to discriminate MDD with or without suicidal ideation and suicidal behavior. Then a multicenter, cross-sectional study of 528 MDD patients with or without suicidality and 998 healthy controls was performed. We defined the probability of dying by the suicide of the suicidality components as a ‘suicidality gradient’. We constructed ten RSNs, including default mode (DMN), subcortical (SUB), ventral attention (VAN), and visual network (VIS). The network connections of RSNs were analyzed among MDD patients with different suicidality gradients and healthy controls using ANCOVA, chi-squared tests, and network-based statistical analysis. And support vector machine (SVM) model was designed to distinguish patients with mild-to-severe suicidal ideation, and suicidal behavior. We found the following abnormalities with increasing suicidality gradient in MDD patients: within-network connectivity values initially increased and then decreased, and one-versus-other network values decreased first and then increased. Besides, within- and between-network connectivity values of the various suicidality gradients are mainly negatively correlated with HAMD anxiety and positively correlated with weight. We found that VIS and DMN-VIS values were affected by age ( p < 0.05), cingulo-opercular network, and SUB-VAN values were statistically influenced by sex ( p < 0.05). Furthermore, the SVM model could distinguish MDD patients with different suicidality gradients (AUC range, 0.73–0.99). In conclusion, we have identified that disrupted brain connections were present in MDD patients with different suicidality gradient. These findings provided useful information about the pathophysiological mechanisms of MDD patients with suicidality.

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          LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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              Real-world complex systems may be mathematically modeled as graphs, revealing properties of the system. Here we study graphs of functional brain organization in healthy adults using resting state functional connectivity MRI. We propose two novel brain-wide graphs, one of 264 putative functional areas, the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships. These graphs contain many subgraphs in good agreement with known functional brain systems. Other subgraphs lack established functional identities; we suggest possible functional characteristics for these subgraphs. Further, graph measures of the areal network indicate that the default mode subgraph shares network properties with sensory and motor subgraphs: it is internally integrated but isolated from other subgraphs, much like a "processing" system. The modified voxelwise graph also reveals spatial motifs in the patterning of systems across the cortex. Copyright © 2011 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                chmxie@163.com
                Journal
                Transl Psychiatry
                Transl Psychiatry
                Translational Psychiatry
                Nature Publishing Group UK (London )
                2158-3188
                27 November 2023
                27 November 2023
                2023
                : 13
                : 365
                Affiliations
                [1 ]Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, ( https://ror.org/04ct4d772) Nanjing, Jiangsu 210009 China
                [2 ]GRID grid.263826.b, ISNI 0000 0004 1761 0489, Institute of Neuropsychiatry, Affiliated ZhongDa Hospital, , Southeast University, ; Nanjing, Jiangsu 210009 China
                [3 ]The Key Laboratory of Developmental Genes and Human Disease, Southeast University, ( https://ror.org/04ct4d772) Nanjing, Jiangsu 210009 China
                [4 ]Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, ( https://ror.org/034t30j35) Beijing, 100101 China
                [5 ]Department of Psychology, University of Chinese Academy of Sciences, ( https://ror.org/05qbk4x57) Beijing, 100049 China
                [6 ]GRID grid.137628.9, ISNI 0000 0004 1936 8753, Department of Child and Adolescent Psychiatry, , New York University School of Medicine, ; New York, NY10016 US
                [7 ]Nathan Kline Institute for Psychiatric Research, ( https://ror.org/01s434164) Orangeburg, NY 10962 USA
                [8 ]Anhui Medical University, ( https://ror.org/03xb04968) Anhui, 230022 China
                [9 ]GRID grid.24696.3f, ISNI 0000 0004 0369 153X, Beijing Anding Hospital, , Capital Medical University, ; Beijing, 100054 China
                [10 ]The First Affiliated Hospital of Jinan University, ( https://ror.org/05d5vvz89) Guangzhou, Guangdong 510630 China
                [11 ]Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, ( https://ror.org/00ka6rp58) Hangzhou, Zhejiang 310016 China
                [12 ]Department of Psychiatry, The Second Xiangya Hospital of Central South University, ( https://ror.org/053v2gh09) Changsha, Hunan 410011 China
                [13 ]First Affiliated Hospital of Kunming Medical University, ( https://ror.org/02g01ht84) Kunming, Yunnan 650032 China
                [14 ]GRID grid.412449.e, ISNI 0000 0000 9678 1884, Department of Psychiatry, First Affiliated Hospital, , China Medical University, ; Shenyang, Liaoning 110001 China
                [15 ]Department of Psychiatry, Shanghai Jiao Tong University School of Medicine, ( https://ror.org/0220qvk04) Shanghai, 200240 China
                [16 ]Department of Radiology, Huaxi MR Research Center, West China Hospital of Sichuan University, ( https://ror.org/007mrxy13) Chengdu, Sichuan 610041 China
                [17 ]Psychoradiology Research Unit of Chinese Academy of Medical Sciences, West China Hospital of Sichuan University, ( https://ror.org/007mrxy13) Chengdu, Sichuan 610041 China
                [18 ]Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University, ( https://ror.org/04ct4d772) Nanjing, Jiangsu 210096 China
                [19 ]Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, ( https://ror.org/033vnzz93) Chongqing, 400016 China
                [20 ]Mental Health Center, West China Hospital, Sichuan University, ( https://ror.org/011ashp19) Chengdu, Sichuan 610041 China
                [21 ]Department of Clinical Psychology, Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, ( https://ror.org/05t8y2r12) Suzhou, Jiangsu 215137 China
                [22 ]Faculty of Psychology, Southwest University, ( https://ror.org/01kj4z117) Chongqing, 400716 China
                [23 ]Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, ( https://ror.org/00a2xv884) Hangzhou, Zhejiang 310058 China
                [24 ]GRID grid.478124.c, ISNI 0000 0004 1773 123X, Xi’an Central Hospital, ; Xi’an, Shaanxi 710003 China
                [25 ]National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, ( https://ror.org/05rzcwg85) Beijing, 100191 China
                [26 ]Key Laboratory of Mental Health, Ministry of Health, Peking University, ( https://ror.org/02v51f717) Beijing, 100191 China
                [27 ]Department of Psychiatry, The Fourth Affiliated Hospital, College of Medicine, Zhejiang University, ( https://ror.org/00a2xv884) Hangzhou, Zhejiang 310058 China
                [28 ]Institute of Neuroscience, Chongqing Medical University, ( https://ror.org/017z00e58) Chongqing, China
                [29 ]GRID grid.203458.8, ISNI 0000 0000 8653 0555, Chongqing Key Laboratory of Neurobiology, ; Chongqing, 400016 China
                [30 ]Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, ( https://ror.org/033vnzz93) Chongqing, 400016 China
                [31 ]The First Affiliated Hospital of Xi’an Jiaotong University, ( https://ror.org/02tbvhh96) Shanxi, 710061 China
                [32 ]First Hospital of Shanxi Medical University, ( https://ror.org/02vzqaq35) Taiyuan, Shanxi 030001 China
                [33 ]Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, ( https://ror.org/014v1mr15) Hangzhou, Zhejiang 311121 China
                [34 ]GRID grid.410595.c, ISNI 0000 0001 2230 9154, Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, ; Hangzhou, Zhejiang 311121 China
                Author information
                http://orcid.org/0000-0001-5480-0888
                http://orcid.org/0009-0006-8927-7170
                Article
                2655
                10.1038/s41398-023-02655-4
                10682490
                38012129
                267f77d7-cb24-4a2f-b2ff-edb91ef09035
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 February 2023
                : 30 October 2023
                : 7 November 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 82271574
                Award ID: 82071204
                Award ID: 81871069
                Award Recipient :
                Funded by: the Science and Technology Innovation 2030 Major Projects [grant number 2022ZD0211600], and the Foundation of Jiangsu Commission of Health [grant number Z2018023].
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                © Springer Nature Limited 2023

                Clinical Psychology & Psychiatry
                depression,diagnostic markers
                Clinical Psychology & Psychiatry
                depression, diagnostic markers

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