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      Gray matter alterations in chronic pain: A network-oriented meta-analytic approach

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          Abstract

          Several studies have attempted to characterize morphological brain changes due to chronic pain. Although it has repeatedly been suggested that longstanding pain induces gray matter modifications, there is still some controversy surrounding the direction of the change (increase or decrease in gray matter) and the role of psychological and psychiatric comorbidities. In this study, we propose a novel, network-oriented, meta-analytic approach to characterize morphological changes in chronic pain. We used network decomposition to investigate whether different kinds of chronic pain are associated with a common or specific set of altered networks. Representational similarity techniques, network decomposition and model-based clustering were employed: i) to verify the presence of a core set of brain areas commonly modified by chronic pain; ii) to investigate the involvement of these areas in a large-scale network perspective; iii) to study the relationship between altered networks and; iv) to find out whether chronic pain targets clusters of areas. Our results showed that chronic pain causes both core and pathology-specific gray matter alterations in large-scale networks. Common alterations were observed in the prefrontal regions, in the anterior insula, cingulate cortex, basal ganglia, thalamus, periaqueductal gray, post- and pre-central gyri and inferior parietal lobule. We observed that the salience and attentional networks were targeted in a very similar way by different chronic pain pathologies. Conversely, alterations in the sensorimotor and attention circuits were differentially targeted by chronic pain pathologies. Moreover, model-based clustering revealed that chronic pain, in line with some neurodegenerative diseases, selectively targets some large-scale brain networks. Altogether these findings indicate that chronic pain can be better conceived and studied in a network perspective.

          Highlights

          • Chronic pain causes both core and pathology-specific.
          • GM alterations in brain networks.
          • Model-based clustering revealed that chronic pain selectively targets brain networks.
          • Chronic pain can be better conceived and studied in a network perspective.

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          Most cited references 103

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          The assessment and analysis of handedness: The Edinburgh inventory

           R.C. Oldfield (1971)
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            The human brain is intrinsically organized into dynamic, anticorrelated functional networks.

            During performance of attention-demanding cognitive tasks, certain regions of the brain routinely increase activity, whereas others routinely decrease activity. In this study, we investigate the extent to which this task-related dichotomy is represented intrinsically in the resting human brain through examination of spontaneous fluctuations in the functional MRI blood oxygen level-dependent signal. We identify two diametrically opposed, widely distributed brain networks on the basis of both spontaneous correlations within each network and anticorrelations between networks. One network consists of regions routinely exhibiting task-related activations and the other of regions routinely exhibiting task-related deactivations. This intrinsic organization, featuring the presence of anticorrelated networks in the absence of overt task performance, provides a critical context in which to understand brain function. We suggest that both task-driven neuronal responses and behavior are reflections of this dynamic, ongoing, functional organization of the brain.
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              Voxel-based morphometry--the methods.

              At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the same stereotactic space. This is followed by segmenting the gray matter from the spatially normalized images and smoothing the gray-matter segments. Voxel-wise parametric statistical tests which compare the smoothed gray-matter images from the two groups are performed. Corrections for multiple comparisons are made using the theory of Gaussian random fields. This paper describes the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with nonuniformity artifact. We provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data. Copyright 2000 Academic Press.
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                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                16 April 2014
                16 April 2014
                2014
                : 4
                : 676-686
                Affiliations
                [a ]GCS fMRI, Koelliker Hospital, Department of Psychology, University of Turin, Turin, Italy
                [b ]Functional Neuroimaging and Complex Systems Group, Department of Psychology, University of Turin, Turin, Italy
                [c ]Department of Psychology, University of Turin, Turin, Italy
                [d ]Department of Neuroscience, AOU San Giovanni Battista, Turin, Italy
                [e ]Psycho-Oncology and Clinical Psychology Unit, University of Turin, Città della Salute e della Scienza, Turin, Italy
                Author notes
                [* ]Corresponding author at: Department of Psychology, Via Po 14, Turin 10123, Italy. franco.cauda@ 123456unito.it
                Article
                S2213-1582(14)00051-5
                10.1016/j.nicl.2014.04.007
                4053643
                © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

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