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      Childhood trauma history is linked to abnormal brain connectivity in major depression

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          Significance

          The primary finding in this study was the dramatic primary association of brain resting-state network (RSN) connectivity abnormalities with a history of childhood trauma in major depressive disorder (MDD). Even though participants in this study were not selected for a history of trauma and the brain imaging took place decades after trauma occurrence, the scar of prior trauma was evident in functional dysconnectivity. In addition to childhood trauma, dimensions of MDD symptoms were related to abnormal network connectivity. Further, we found that a network model of MDD described within- and between-network connectivity differences from controls in multiple RSNs, including the default mode network, frontoparietal network, and attention and sensory systems.

          Abstract

          Patients with major depressive disorder (MDD) present with heterogeneous symptom profiles, while neurobiological mechanisms are still largely unknown. Brain network studies consistently report disruptions of resting-state networks (RSNs) in patients with MDD, including hypoconnectivity in the frontoparietal network (FPN), hyperconnectivity in the default mode network (DMN), and increased connection between the DMN and FPN. Using a large, multisite fMRI dataset ( n = 189 patients with MDD, n = 39 controls), we investigated network connectivity differences within and between RSNs in patients with MDD and healthy controls. We found that MDD could be characterized by a network model with the following abnormalities relative to controls: ( i) lower within-network connectivity in three task-positive RSNs [FPN, dorsal attention network (DAN), and cingulo-opercular network (CON)], ( ii) higher within-network connectivity in two intrinsic networks [DMN and salience network (SAN)], and ( iii) higher within-network connectivity in two sensory networks [sensorimotor network (SMN) and visual network (VIS)]. Furthermore, we found significant alterations in connectivity between a number of these networks. Among patients with MDD, a history of childhood trauma and current symptoms quantified by clinical assessments were associated with a multivariate pattern of seven different within- and between-network connectivities involving the DAN, FPN, CON, subcortical regions, ventral attention network (VAN), auditory network (AUD), VIS, and SMN. Overall, our study showed that traumatic childhood experiences and dimensional symptoms are linked to abnormal network architecture in MDD. Our results suggest that RSN connectivity may explain underlying neurobiological mechanisms of MDD symptoms and has the potential to serve as an effective diagnostic biomarker.

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          Most cited references43

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.

            Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed. Copyright © 2012 Elsevier Inc. All rights reserved.
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              A dual-networks architecture of top-down control.

              Complex systems ensure resilience through multiple controllers acting at rapid and slower timescales. The need for efficient information flow through complex systems encourages small-world network structures. On the basis of these principles, a group of regions associated with top-down control was examined. Functional magnetic resonance imaging showed that each region had a specific combination of control signals; resting-state functional connectivity grouped the regions into distinct 'fronto-parietal' and 'cingulo-opercular' components. The fronto-parietal component seems to initiate and adjust control; the cingulo-opercular component provides stable 'set-maintenance' over entire task epochs. Graph analysis showed dense local connections within components and weaker 'long-range' connections between components, suggesting a small-world architecture. The control systems of the brain seem to embody the principles of complex systems, encouraging resilient performance.
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                Author and article information

                Journal
                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                pnas
                pnas
                PNAS
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                0027-8424
                1091-6490
                23 April 2019
                8 April 2019
                8 April 2019
                : 116
                : 17
                : 8582-8590
                Affiliations
                [1] aCenter for Neuromodulation in Depression and Stress, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104;
                [2] bDepartment of Psychiatry, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104;
                [3] cDepartment of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104;
                [4] dDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104;
                [5] eBrain and Behavior Laboratory, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104;
                [6] fDepartment of Psychiatry, University of Pittsburgh School of Medicine , Pittsburgh, PA 15260;
                [7] gDepartment of Psychiatry, University of Michigan School of Medicine , Ann Arbor, MI 48109;
                [8] hDepartment of Psychiatry, Massachusetts General Hospital , Boston, MA 02114;
                [9] iCenter for Depression Research and Clinical Care, Peter O'Donnell Institute, University of Texas Southwestern Medical Center , Dallas, TX 75390;
                [10] jDepartment of Psychiatry, Columbia University College of Physicians & Surgeons , New York, NY 10032;
                [11] kDepartment of Psychiatry, Stony Brook University , Stony Brook, NY 11794;
                [12] lDepartment of Neurology, Perelman School of Medicine, University of Pennsylvania , Philadelphia, PA 19104
                Author notes
                1To whom correspondence should be addressed. Email: sheline@ 123456pennmedicine.upenn.edu .

                Edited by Marcus E. Raichle, Washington University in St. Louis, St. Louis, MO, and approved March 12, 2019 (received for review January 18, 2019)

                Author contributions: M.Y. and Y.I.S. designed research; M.Y., K.A.L., R.T.S., and Y.I.S. performed research; M.A.O., M.L.P., M.M., M.F., M.H.T., P.M., R.P., and M.M.W. collected data; M.Y., P.A.C., and T.M.M. analyzed data; and M.Y., K.A.L., R.T.S., D.J.O., R.D., and Y.I.S. wrote the paper.

                Author information
                http://orcid.org/0000-0002-1384-0151
                Article
                201900801
                10.1073/pnas.1900801116
                6486762
                30962366
                a783992c-2690-4c81-a481-ec77c9631ec0
                Copyright © 2019 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).

                History
                Page count
                Pages: 9
                Categories
                PNAS Plus
                Biological Sciences
                Neuroscience
                PNAS Plus

                childhood trauma,dimensional symptoms,major depressive disorder,network connectivity,resting-state networks

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