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      Altered resting-state functional connectivity in adolescents is associated with PTSD symptoms and trauma exposure

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          Abstract

          Alterations in resting-state functional connectivity (rsFC) have been demonstrated in Posttraumatic Stress Disorder (PTSD). However, such reports have primarily focused on adult participants, whereas findings in adolescents with PTSD are mixed and not entirely consistent with the adult literature. Here, we examined rsFC in a non-treatment seeking adolescent sample with posttraumatic stress symptoms (PTSS; n = 59) relative to asymptomatic controls ( n = 226). We also examined differences between trauma-exposed and non-exposed control subgroups (TEC n = 73 and Non-TEC n = 153) to examine alterations associated with more general trauma exposure. Finally, we compared the PTSS and TEC groups, to confirm that the reported alterations in PTSS were not driven by trauma exposure. Using a seed-based approach, we examined connectivity of default-mode (DMN) and salience (SN) networks, where alterations have been previously reported. Results suggest that PTSS are associated with less within-DMN connectivity and greater SN-DMN connectivity, as well as altered connectivity with attention regions. Trauma exposure is associated with greater within-SN connectivity. Additionally, we report findings from exploratory connectome-based analysis, which demonstrate a number of topological alterations within DMN in the PTSS group. Overall, our findings replicate prior reports of altered rsFC in PTSD and extend them to non-treatment seeking, trauma-exposed adolescents, who did or did not report PTSS. They specifically highlight SN-DMN desegregation, lower within-DMN and greater within-SN connectivity, as well as altered connectivity with attention regions, in trauma-exposed adolescents. Future research is required to confirm that adolescents with diagnosed PTSD have similar/exacerbated connectivity patterns.

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          Using connectome-based predictive modeling to predict individual behavior from brain connectivity

          This protocol describes how to develop linear models to predict individual behavior from brain connectivity data with proper cross-validation, and how to use an online tool to visualize the most predictive features of the models.
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            Neuroimaging of the Philadelphia neurodevelopmental cohort.

            The Philadelphia Neurodevelopmental Cohort (PNC) is a large-scale, NIMH funded initiative to understand how brain maturation mediates cognitive development and vulnerability to psychiatric illness, and understand how genetics impacts this process. As part of this study, 1445 adolescents ages 8-21 at enrollment underwent multimodal neuroimaging. Here, we highlight the conceptual basis for the effort, the study design, and the measures available in the dataset. We focus on neuroimaging measures obtained, including T1-weighted structural neuroimaging, diffusion tensor imaging, perfusion neuroimaging using arterial spin labeling, functional imaging tasks of working memory and emotion identification, and resting state imaging of functional connectivity. Furthermore, we provide characteristics regarding the final sample acquired. Finally, we describe mechanisms in place for data sharing that will allow the PNC to become a freely available public resource to advance our understanding of normal and pathological brain development. © 2013 Elsevier Inc. All rights reserved.
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              Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data

              Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term ( 5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest.
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                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                19 February 2020
                2020
                19 February 2020
                : 26
                : 102215
                Affiliations
                [a ]Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, MI, USA
                [b ]Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
                [c ]Neuropsychiatry Division, and the Lifespan Brain Institute, Department of Psychiatry, Perelman School of Medicine, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
                [d ]VISN4 Mental Illness Research, Education and Clinical Center, Philadelphia VA Medical Center, Philadelphia, PA, USA
                [e ]Department of Statistics, University of Michigan, Ann Arbor, MI, USA
                [f ]Mental Health Institute, the Second Xiangya Hospital of Central South University, National Clinical Research Center on Mental Disorders, National Technology Institute on Mental Disorders, Hunan Key Laboratory of Psychiatry and Mental Health of Hunan Province, Changsha, Hunan, China
                [g ]Department of Psychiatry and Behavioral Science, Texas A&M University Health Science Center, TX, USA
                [h ]Texas A&M Institute for Neuroscience, Texas A&M University, College Station, TX, USA
                Author notes
                [* ]Corresponding author at: Department of Psychiatry and Behavioral Science, Texas A&M University Health Science Center, 8441 Riverside Pkwy, Clinical Building 1, 1st Floor Suite 1100, Bryan, TX 77807, USA. liberzon@ 123456tamu.edu
                [1]

                Both authors contributed equally.

                Article
                S2213-1582(20)30052-8 102215
                10.1016/j.nicl.2020.102215
                7184176
                32339825
                246577ae-c3d1-4dab-960a-cf763421d9b6
                © 2020 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 8 August 2019
                : 13 February 2020
                : 16 February 2020
                Categories
                Regular Article

                posttraumatic stress disorder,trauma,adolescents,resting-state,functional connectivity,fmri

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