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      Intrinsic functional connectivity predicts remission on antidepressants: a randomized controlled trial to identify clinically applicable imaging biomarkers

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          Abstract

          Default mode network (DMN) dysfunction (particularly within the anterior cingulate cortex (ACC) and medial prefrontal cortex (mPFC)) has been implicated in major depressive disorder (MDD); however, its contribution to treatment outcome has not been clearly established. Here we tested the role of DMN functional connectivity as a general and differential biomarker for predicting treatment outcomes in a large, unmedicated adult sample with MDD. Seventy-five MDD outpatients completed fMRI scans before and 8 weeks after randomization to escitalopram, sertraline, or venlafaxine-XR. A whole-brain voxel-wise t-test identified profiles of pretreatment intrinsic functional connectivity that distinguished patients who were subsequently classified as remitters or non-remitters at follow-up. Connectivity was seeded in the PCC, an important node of the DMN. We further characterized differences between remitters, non-remitters, and 31 healthy controls and characterized changes pretreatment to posttreatment. Remitters were distinguished from non-remitters by relatively intact connectivity between the PCC and ACC/mPFC, not distinguishable from healthy controls, while non-remitters showed relative hypo-connectivity. In validation analyses, we demonstrate that PCC–ACC/mPFC connectivity predicts remission status with >80% cross-validated accuracy. In analyses testing whether intrinsic connectivity differentially relates to outcomes for a specific type of antidepressant, interaction models did not survive the corrected threshold. Our findings demonstrate that the overall capacity to remit on commonly used antidepressants may depend on intact organization of intrinsic functional connectivity between PCC and ACC/mPFC prior to treatment. The findings highlight the potential utility of functional scans for advancing a more precise approach to tailoring antidepressant treatment choices.

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          Lifetime and 12-month prevalence of DSM-III-R psychiatric disorders in the United States. Results from the National Comorbidity Survey.

          This study presents estimates of lifetime and 12-month prevalence of 14 DSM-III-R psychiatric disorders from the National Comorbidity Survey, the first survey to administer a structured psychiatric interview to a national probability sample in the United States. The DSM-III-R psychiatric disorders among persons aged 15 to 54 years in the noninstitutionalized civilian population of the United States were assessed with data collected by lay interviewers using a revised version of the Composite International Diagnostic Interview. Nearly 50% of respondents reported at least one lifetime disorder, and close to 30% reported at least one 12-month disorder. The most common disorders were major depressive episode, alcohol dependence, social phobia, and simple phobia. More than half of all lifetime disorders occurred in the 14% of the population who had a history of three or more comorbid disorders. These highly comorbid people also included the vast majority of people with severe disorders. Less than 40% of those with a lifetime disorder had ever received professional treatment, and less than 20% of those with a recent disorder had been in treatment during the past 12 months. Consistent with previous risk factor research, it was found that women had elevated rates of affective disorders and anxiety disorders, that men had elevated rates of substance use disorders and antisocial personality disorder, and that most disorders declined with age and with higher socioeconomic status. The prevalence of psychiatric disorders is greater than previously thought to be the case. Furthermore, this morbidity is more highly concentrated than previously recognized in roughly one sixth of the population who have a history of three or more comorbid disorders. This suggests that the causes and consequences of high comorbidity should be the focus of research attention. The majority of people with psychiatric disorders fail to obtain professional treatment. Even among people with a lifetime history of three or more comorbid disorders, the proportion who ever obtain specialty sector mental health treatment is less than 50%. These results argue for the importance of more outreach and more research on barriers to professional help-seeking.
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            Statistical improvements in functional magnetic resonance imaging analyses produced by censoring high-motion data points.

            Subject motion degrades the quality of task functional magnetic resonance imaging (fMRI) data. Here, we test two classes of methods to counteract the effects of motion in task fMRI data: (1) a variety of motion regressions and (2) motion censoring ("motion scrubbing"). In motion regression, various regressors based on realignment estimates were included as nuisance regressors in general linear model (GLM) estimation. In motion censoring, volumes in which head motion exceeded a threshold were withheld from GLM estimation. The effects of each method were explored in several task fMRI data sets and compared using indicators of data quality and signal-to-noise ratio. Motion censoring decreased variance in parameter estimates within- and across-subjects, reduced residual error in GLM estimation, and increased the magnitude of statistical effects. Motion censoring performed better than all forms of motion regression and also performed well across a variety of parameter spaces, in GLMs with assumed or unassumed response shapes. We conclude that motion censoring improves the quality of task fMRI data and can be a valuable processing step in studies involving populations with even mild amounts of head movement. Copyright © 2013 Wiley Periodicals, Inc.
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              Functional connectivity in the cognitive control network and the default mode network in late-life depression.

              Abnormalities have been identified in the Cognitive Control Network (CCN) and the Default Mode Network (DMN) during episodes of late-life depression. This study examined whether functional connectivity at rest (FC) within these networks characterizes late-life depression and predicts antidepressant response. 26 non-demented, non-MCI older adults were studied. Of these, 16 had major depression and 10 had no psychopathology. Depressed patients were treated with escitalopram (target dose 20 mg) for 12 weeks after a 2-week placebo phase. Resting state time series was determined prior to treatment. FC within the CCN was determined by placing seeds in the dACC and the DLPFC bilaterally. FC within the DMN was assessed from a seed placed in the posterior cingulate. Low resting FC within the CCN and high resting FC within the DMN distinguished depressed from normal elderly subjects. Beyond this "double dissociation", low resting FC within the CCN predicted low remission rate and persistence of depressive symptoms and signs, apathy, and dysexecutive behavior after treatment with escitalopram. In contrast, resting FC within the DMN was correlated with pessimism but did not predict treatment response. If confirmed, these findings may serve as a signature of the brain's functional topography characterizing late-life depression and sustaining its symptoms. By identifying the network abnormalities underlying biologically meaningful characteristics (apathy, dysexecutive behavior, pessimism) and sustaining late-life depression, these findings can provide a novel target on which new somatic and psychosocial treatments can be tested. Published by Elsevier B.V.
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                Author and article information

                Contributors
                +650-723-3579 , leawilliams@stanford.edu
                Journal
                Transl Psychiatry
                Transl Psychiatry
                Translational Psychiatry
                Nature Publishing Group UK (London )
                2158-3188
                6 March 2018
                6 March 2018
                2018
                : 8
                : 57
                Affiliations
                [1 ]ISNI 0000000419368956, GRID grid.168010.e, Department of Psychiatry and Behavioral Sciences, , Stanford University, ; 401 Quarry Road, Stanford, CA 94305 USA
                [2 ]ISNI 0000 0004 0419 2556, GRID grid.280747.e, Sierra-Pacific Mental Illness Research, , Education, and Clinical Center (MIRECC) Veterans Affairs Palo Alto Health Care System, ; 3801 Miranda Avenue, Palo Alto, CA 94304 USA
                [3 ]ISNI 0000 0004 1936 834X, GRID grid.1013.3, Brain Dynamics Center, The Westmead Institute for Medical Research, , University of Sydney, ; Sydney, NSW Australia
                [4 ]ISNI 0000 0004 1936 834X, GRID grid.1013.3, Discipline of Psychiatry, , Sydney Medical School, ; Westmead, Sydney, NSW Australia
                Author information
                http://orcid.org/0000-0001-9987-7360
                Article
                100
                10.1038/s41398-018-0100-3
                5838245
                29507282
                9b77cd33-45bd-43c7-8032-18c09e4913f6
                © The Author(s) 2018

                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
                : 27 June 2017
                : 5 October 2017
                : 15 October 2017
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                © The Author(s) 2018

                Clinical Psychology & Psychiatry
                Clinical Psychology & Psychiatry

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