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      Altered ability to access a clinically relevant control network in patients remitted from major depressive disorder

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          Abstract

          Neurobiological models to explain vulnerability of major depressive disorder (MDD) are scarce and previous functional magnetic resonance imaging studies mostly examined “static” functional connectivity (FC). Knowing that FC constantly evolves over time, it becomes important to assess how FC dynamically differs in remitted‐MDD patients vulnerable for new depressive episodes. Using a recently developed method to examine dynamic FC, we characterized re‐emerging FC states during rest in 51 antidepressant‐free MDD patients at high risk of recurrence (≥2 previous episodes), and 35 healthy controls. We examined differences in occurrence, duration, and switching profiles of FC states after neutral and sad mood induction. Remitted MDD patients showed a decreased probability of an FC state ( p < 0.005) consisting of an extensive network connecting frontal areas—important for cognitive control—with default mode network, striatum, and salience areas, involved in emotional and self‐referential processing. Even when this FC state was observed in patients, it lasted shorter ( p < 0.005) and was less likely to switch to a smaller prefrontal–striatum network ( p < 0.005). Differences between patients and controls decreased after sad mood induction. Further, the duration of this FC state increased in remitted patients after sad mood induction but not in controls ( p < 0.05). Our findings suggest reduced ability of remitted‐MDD patients, in neutral mood, to access a clinically relevant control network involved in the interplay between externally and internally oriented attention. When recovering from sad mood, remitted recurrent MDD appears to employ a compensatory mechanism to access this FC state. This study provides a novel neurobiological profile of MDD vulnerability.

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          Groupwise whole-brain parcellation from resting-state fMRI data for network node identification.

          In this paper, we present a groupwise graph-theory-based parcellation approach to define nodes for network analysis. The application of network-theory-based analysis to extend the utility of functional MRI has recently received increased attention. Such analyses require first and foremost a reasonable definition of a set of nodes as input to the network analysis. To date many applications have used existing atlases based on cytoarchitecture, task-based fMRI activations, or anatomic delineations. A potential pitfall in using such atlases is that the mean timecourse of a node may not represent any of the constituent timecourses if different functional areas are included within a single node. The proposed approach involves a groupwise optimization that ensures functional homogeneity within each subunit and that these definitions are consistent at the group level. Parcellation reproducibility of each subunit is computed across multiple groups of healthy volunteers and is demonstrated to be high. Issues related to the selection of appropriate number of nodes in the brain are considered. Within typical parameters of fMRI resolution, parcellation results are shown for a total of 100, 200, and 300 subunits. Such parcellations may ultimately serve as a functional atlas for fMRI and as such three atlases at the 100-, 200- and 300-parcellation levels derived from 79 healthy normal volunteers are made freely available online along with tools to interface this atlas with SPM, BioImage Suite and other analysis packages. Copyright © 2013 Elsevier Inc. All rights reserved.
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            Mental health: a world of depression.

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              Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?

              During the last several years, the focus of research on resting-state functional magnetic resonance imaging (fMRI) has shifted from the analysis of functional connectivity averaged over the duration of scanning sessions to the analysis of changes of functional connectivity within sessions. Although several studies have reported the presence of dynamic functional connectivity (dFC), statistical assessment of the results is not always carried out in a sound way and, in some studies, is even omitted. In this study, we explain why appropriate statistical tests are needed to detect dFC, we describe how they can be carried out and how to assess the performance of dFC measures, and we illustrate the methodology using spontaneous blood-oxygen level-dependent (BOLD) fMRI recordings of macaque monkeys under general anesthesia and in human subjects under resting-state conditions. We mainly focus on sliding-window correlations since these are most widely used in assessing dFC, but also consider a recently proposed non-linear measure. The simulations and methodology, however, are general and can be applied to any measure. The results are twofold. First, through simulations, we show that in typical resting-state sessions of 10 min, it is almost impossible to detect dFC using sliding-window correlations. This prediction is validated by both the macaque and the human data: in none of the individual recording sessions was evidence for dFC found. Second, detection power can be considerably increased by session- or subject-averaging of the measures. In doing so, we found that most of the functional connections are in fact dynamic. With this study, we hope to raise awareness of the statistical pitfalls in the assessment of dFC and how they can be avoided by using appropriate statistical methods.
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                Author and article information

                Contributors
                carolinefigueroa8@gmail.com
                H.G.Ruhe@gmail.com
                Journal
                Hum Brain Mapp
                Hum Brain Mapp
                10.1002/(ISSN)1097-0193
                HBM
                Human Brain Mapping
                John Wiley & Sons, Inc. (Hoboken, USA )
                1065-9471
                1097-0193
                12 March 2019
                15 June 2019
                : 40
                : 9 ( doiID: 10.1002/hbm.v40.9 )
                : 2771-2786
                Affiliations
                [ 1 ] Department of Psychiatry, Academic Medical Center University of Amsterdam Amsterdam The Netherlands
                [ 2 ] Brain Imaging Center Academic Medical Center Amsterdam The Netherlands
                [ 3 ] School of Social Welfare University of California Berkeley Berkeley California
                [ 4 ] Department of Psychiatry University of Oxford Oxford United Kingdom
                [ 5 ] Life and Health Sciences Research Institute (ICVS), School of Medicine University of Minho Braga Portugal
                [ 6 ] Center for Music in the Brain Aarhus University Aarhus Denmark
                [ 7 ] Department of Psychological and Brain Sciences Dartmouth College Hanover New Hampshire
                [ 8 ] Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain
                [ 9 ] Institució Catalana de la Recerca i Estudis Avançats (ICREA) Barcelona Spain
                [ 10 ] Department of Neuropsychology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany
                [ 11 ] School of Psychological Sciences Monash University Melbourne Australia
                [ 12 ] Centre for Mathematics of Precision Healthcare Imperial College London London United Kingdom
                [ 13 ] Department of Mathematics Imperial College London London United Kingdom
                [ 14 ] Department of Psychiatry Radboud University Medical Center Nijmegen The Netherlands
                [ 15 ] Donders Institute for Brain, Cognition and Behavior Radboud University Nijmegen The Netherlands
                Author notes
                [*] [* ] Correspondence

                Caroline A. Figueroa, Haviland Hall 105, Department of Social Welfare, University of California Berkeley, Berkeley, CA 94709, United States of America.

                Email: carolinefigueroa8@ 123456gmail.com

                H.G. Ruhé

                Department of Psychiatry, Radboudumc Reinier Postlaan 4 (route 966), P.O.Box 9101, 6500 HB Nijmegen, The Netherlands.

                Email: H.G.Ruhe@ 123456gmail.com

                Author information
                https://orcid.org/0000-0003-0692-2244
                https://orcid.org/0000-0002-6715-0826
                Article
                HBM24559
                10.1002/hbm.24559
                6865599
                30864248
                47b934c5-005b-4ad7-b06e-24e74c02f9f7
                © 2019 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 08 July 2018
                : 30 January 2019
                : 21 February 2019
                Page count
                Figures: 6, Tables: 1, Pages: 16, Words: 13190
                Funding
                Funded by: Danish National Research Foundation , open-funder-registry 10.13039/501100001732;
                Award ID: (DNRF117)
                Funded by: Dutch Brain Foundation
                Award ID: 2009(2)‐72
                Funded by: ERC Consolidator Grant
                Award ID: CAREGIVING (n. 615539)
                Funded by: European Regional Development Fund , open-funder-registry 10.13039/501100008530;
                Award ID: NORTE‐01‐0145‐FEDER‐000023
                Funded by: Horizon 2020 Framework Programme , open-funder-registry 10.13039/100010661;
                Award ID: No. 785907 (Human Brain Project SGA2)
                Funded by: Spanish Research Project
                Award ID: PSI2016‐75688‐P (AEI/FEDER)
                Funded by: ZonMw , open-funder-registry 10.13039/501100001826;
                Award ID: VENI‐Grant #016.126.059
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                June 15, 2019
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.7.2 mode:remove_FC converted:15.11.2019

                Neurology
                cognitive control,dynamic fc,functional networks,major depressive disorder,resting‐state fmri

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