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      Exploration of Functional Connectivity Changes Previously Reported in Fibromyalgia and Their Relation to Psychological Distress and Pain Measures

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          Abstract

          Neural functional connectivity changes in the default mode network (DMN), Central executive network (CEN), and insula have been implicated in fibromyalgia (FM) but stem from a sparse set of small-scale studies with limited power for the investigation of confounding effects. We investigated whether anxiety, depression, pain sensitivity, and pain intensity modulated functional connectivity related to DMN nodes, CEN nodes, and insula. Resting-state functional magnetic resonance imaging data were collected from 31 females with FM and 28 age-matched healthy controls. Connectivity was analysed with a region-based connectivity analysis between DMN nodes in ventromedial prefrontal cortex (vmPFC) and posterior cingulate cortex, CEN nodes in the intraparietal sulcus (IPS), and bilateral insula. FM patients displayed significantly higher levels of anxiety and depressive symptoms than controls. The right IPS node of the CEN showed a higher level of connectivity strength with right insula in FM with higher pain intensity compared to controls. More anxiety symptoms in FM correlated with higher levels of connectivity strength between the vmPFC DMN node and right sensorimotor cortex. These findings support the theory of altered insular connectivity in FM and also suggest altered IPS connectivity in FM. Interestingly, no change in insular connectivity with DMN was observed.

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

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              Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks.

              Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn ( www.nitrc.org/projects/conn ) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method allows for interpretation of anticorrelations as there is no regression of the global signal. The toolbox implements fcMRI measures, such as estimation of seed-to-voxel and region of interest (ROI)-to-ROI functional correlations, as well as semipartial correlation and bivariate/multivariate regression analysis for multiple ROI sources, graph theoretical analysis, and novel voxel-to-voxel analysis of functional connectivity. We describe the methods implemented in the Conn toolbox for the analysis of fcMRI data, together with examples of use and interscan reliability estimates of all the implemented fcMRI measures. The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fcMRI measures.
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                Author and article information

                Journal
                J Clin Med
                J Clin Med
                jcm
                Journal of Clinical Medicine
                MDPI
                2077-0383
                05 November 2020
                November 2020
                : 9
                : 11
                : 3560
                Affiliations
                [1 ]Department of Clinical Neuroscience, Karolinska Institute, S-171 77 Stockholm, Sweden; rikard.wicksell@ 123456ki.se
                [2 ]Center for Medical Image Science and Visualization (CMIV), Linköping University, S-581 85 Linköping, Sweden; hakan.olausson@ 123456liu.se (H.O.); bjorn.gerdle@ 123456liu.se (B.G.)
                [3 ]Center for Social and Affective Neuroscience (CSAN), Linköping University, S-581 85 Linköping, Sweden
                [4 ]Pain and Rehabilitation Centre, and Department of Health, Medicine and Caring Sciences, Linköping University, S-581 85 Linköping, Sweden; bijar.ghafouri@ 123456liu.se
                Author notes
                Author information
                https://orcid.org/0000-0003-3587-3568
                https://orcid.org/0000-0003-2803-3069
                https://orcid.org/0000-0002-6396-5104
                https://orcid.org/0000-0002-4316-1264
                Article
                jcm-09-03560
                10.3390/jcm9113560
                7694394
                33167371
                248b2e4a-17b9-4588-b692-1c7d2ac319c1
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 08 October 2020
                : 03 November 2020
                Categories
                Article

                insula,central executive network,intraparietal sulcus,resting state,anxiety,pain intensity,fmri

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