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      Shared and distinct structural brain networks related to childhood maltreatment and social support: connectome-based predictive modeling

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      Molecular Psychiatry
      Nature Publishing Group UK
      Neuroscience, Molecular biology

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          Abstract

          Childhood maltreatment (CM) has been associated with changes in structural brain connectivity even in the absence of mental illness. Social support, an important protective factor in the presence of childhood maltreatment, has been positively linked to white matter integrity. However, the shared effects of current social support and CM and their association with structural connectivity remain to be investigated. They might shed new light on the neurobiological basis of the protective mechanism of social support. Using connectome-based predictive modeling (CPM), we analyzed structural connectomes of N = 904 healthy adults derived from diffusion-weighted imaging. CPM predicts phenotypes from structural connectivity through a cross-validation scheme. Distinct and shared networks of white matter tracts predicting childhood trauma questionnaire scores and the social support questionnaire were identified. Additional analyses were applied to assess the stability of the results. CM and social support were predicted significantly from structural connectome data (all rs ≥ 0.119, all ps ≤ 0.016). Edges predicting CM and social support were inversely correlated, i.e., positively correlated with CM and negatively with social support, and vice versa, with a focus on frontal and temporal regions including the insula and superior temporal lobe. CPM reveals the predictive value of the structural connectome for CM and current social support. Both constructs are inversely associated with connectivity strength in several brain tracts. While this underlines the interconnectedness of these experiences, it suggests social support acts as a protective factor following adverse childhood experiences, compensating for brain network alterations. Future longitudinal studies should focus on putative moderating mechanisms buffering these adverse experiences.

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          Most cited references87

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          The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection.

          A 36-item short-form (SF-36) was constructed to survey health status in the Medical Outcomes Study. The SF-36 was designed for use in clinical practice and research, health policy evaluations, and general population surveys. The SF-36 includes one multi-item scale that assesses eight health concepts: 1) limitations in physical activities because of health problems; 2) limitations in social activities because of physical or emotional problems; 3) limitations in usual role activities because of physical health problems; 4) bodily pain; 5) general mental health (psychological distress and well-being); 6) limitations in usual role activities because of emotional problems; 7) vitality (energy and fatigue); and 8) general health perceptions. The survey was constructed for self-administration by persons 14 years of age and older, and for administration by a trained interviewer in person or by telephone. The history of the development of the SF-36, the origin of specific items, and the logic underlying their selection are summarized. The content and features of the SF-36 are compared with the 20-item Medical Outcomes Study short-form.
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            The effect of multiple adverse childhood experiences on health: a systematic review and meta-analysis

            A growing body of research identifies the harmful effects that adverse childhood experiences (ACEs; occurring during childhood or adolescence; eg, child maltreatment or exposure to domestic violence) have on health throughout life. Studies have quantified such effects for individual ACEs. However, ACEs frequently co-occur and no synthesis of findings from studies measuring the effect of multiple ACE types has been done.
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              Network-based statistic: identifying differences in brain networks.

              Large-scale functional or structural brain connectivity can be modeled as a network, or graph. This paper presents a statistical approach to identify connections in such a graph that may be associated with a diagnostic status in case-control studies, changing psychological contexts in task-based studies, or correlations with various cognitive and behavioral measures. The new approach, called the network-based statistic (NBS), is a method to control the family-wise error rate (in the weak sense) when mass-univariate testing is performed at every connection comprising the graph. To potentially offer a substantial gain in power, the NBS exploits the extent to which the connections comprising the contrast or effect of interest are interconnected. The NBS is based on the principles underpinning traditional cluster-based thresholding of statistical parametric maps. The purpose of this paper is to: (i) introduce the NBS for the first time; (ii) evaluate its power with the use of receiver operating characteristic (ROC) curves; and, (iii) demonstrate its utility with application to a real case-control study involving a group of people with schizophrenia for which resting-state functional MRI data were acquired. The NBS identified a expansive dysconnected subnetwork in the group with schizophrenia, primarily comprising fronto-temporal and occipito-temporal dysconnections, whereas a mass-univariate analysis controlled with the false discovery rate failed to identify a subnetwork. Copyright © 2010 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                udo.dannlowski@uni-muenster.de
                Journal
                Mol Psychiatry
                Mol Psychiatry
                Molecular Psychiatry
                Nature Publishing Group UK (London )
                1359-4184
                1476-5578
                15 September 2023
                15 September 2023
                2023
                : 28
                : 11
                : 4613-4621
                Affiliations
                [1 ]Institute for Translational Psychiatry, University of Münster, ( https://ror.org/00pd74e08) Münster, Germany
                [2 ]GRID grid.411088.4, ISNI 0000 0004 0578 8220, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, , University Hospital Frankfurt, Goethe University, ; Frankfurt, Germany
                [3 ]Institute for Translational Neuroscience, University of Münster, ( https://ror.org/00pd74e08) Münster, Germany
                [4 ]Department of Psychiatry and Psychotherapy, University of Marburg, ( https://ror.org/00g30e956) Marburg, Germany
                [5 ]Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, ( https://ror.org/033eqas34) Giessen, Germany
                [6 ]GRID grid.10253.35, ISNI 0000 0004 1936 9756, Core-Facility Brainimaging, Faculty of Medicine, , University of Marburg, ; Marburg, Germany
                [7 ]Department of Child and Adolescent Psychiatry, University Hospital Münster, ( https://ror.org/01856cw59) Münster, Germany
                [8 ]GRID grid.9613.d, ISNI 0000 0001 1939 2794, Department of Psychiatry and Psychotherapy, , University of Jena, ; Jena, Germany
                [9 ]GRID grid.484519.5, Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, , Vrije Universiteit Amsterdam, Amsterdam Neuroscience, ; Amsterdam, The Netherlands
                [10 ]GRID grid.484519.5, Department of Child Psychiatry, , Amsterdam University Medical Center, Amsterdam Neuroscience, ; Amsterdam, The Netherlands
                Author information
                http://orcid.org/0000-0003-3759-1425
                http://orcid.org/0000-0003-2177-7161
                http://orcid.org/0000-0003-3087-1002
                http://orcid.org/0000-0002-6241-1492
                http://orcid.org/0000-0002-0526-8095
                http://orcid.org/0000-0002-0371-7686
                http://orcid.org/0000-0003-4749-3298
                http://orcid.org/0000-0001-6541-3795
                Article
                2252
                10.1038/s41380-023-02252-3
                10914611
                37714950
                5bd0bc41-4a02-432a-b417-19b25ec35603
                © 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 March 2023
                : 30 August 2023
                : 5 September 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: DA 1151/5-1, DA 1151/5-2
                Award ID: JA 1890/7-1, JA 1890/7-2
                Award ID: STR 1146/18-1
                Award ID: HA 7070/2-2
                Award ID: KI 588/14-1, KI 588/14-2
                Award Recipient :
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                © Springer Nature Limited 2023

                Molecular medicine
                neuroscience,molecular biology
                Molecular medicine
                neuroscience, molecular biology

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