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      How fMRI Analysis Using Structural Equation Modeling Techniques Can Improve Our Understanding of Pain Processing in Fibromyalgia

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          The purpose of this study was to investigate the utility of data-driven analyses of functional magnetic resonance imaging (fMRI) data, by means of structural equation modeling, for the investigation of pain processing in fibromyalgia (FM).

          Patients and Methods

          Datasets from two separate pain fMRI studies involving healthy controls (HC) and participants with FM were re-analyzed using both a conventional model-driven approach and a data-driven approach, and the results from these analyses were compared. The first dataset contained 15 women with FM and 15 women as healthy controls. The second dataset contained 15 women with FM and 11 women as healthy controls.


          Consistent with previous studies, the model-driven analyses did not identify differences in pain processing between the HC and FM study groups in both datasets. On the other hand, the data-driven analyses identified significant group differences in both datasets.


          Data-driven analyses can enhance our understanding of pain processing in healthy controls and in clinical populations by identifying activity associated with pain processing specific to the clinical groups that conventional model-driven analyses may miss.

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          Most cited references 62

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          Descending control of pain.

           Mark J Millan (2002)
          Upon receipt in the dorsal horn (DH) of the spinal cord, nociceptive (pain-signalling) information from the viscera, skin and other organs is subject to extensive processing by a diversity of mechanisms, certain of which enhance, and certain of which inhibit, its transfer to higher centres. In this regard, a network of descending pathways projecting from cerebral structures to the DH plays a complex and crucial role. Specific centrifugal pathways either suppress (descending inhibition) or potentiate (descending facilitation) passage of nociceptive messages to the brain. Engagement of descending inhibition by the opioid analgesic, morphine, fulfils an important role in its pain-relieving properties, while induction of analgesia by the adrenergic agonist, clonidine, reflects actions at alpha(2)-adrenoceptors (alpha(2)-ARs) in the DH normally recruited by descending pathways. However, opioids and adrenergic agents exploit but a tiny fraction of the vast panoply of mechanisms now known to be involved in the induction and/or expression of descending controls. For example, no drug interfering with descending facilitation is currently available for clinical use. The present review focuses on: (1) the organisation of descending pathways and their pathophysiological significance; (2) the role of individual transmitters and specific receptor types in the modulation and expression of mechanisms of descending inhibition and facilitation and (3) the advantages and limitations of established and innovative analgesic strategies which act by manipulation of descending controls. Knowledge of descending pathways has increased exponentially in recent years, so this is an opportune moment to survey their operation and therapeutic relevance to the improved management of pain.
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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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              An Inventory for Measuring Depression

               A. Beck (1961)

                Author and article information

                J Pain Res
                J Pain Res
                Journal of Pain Research
                10 February 2021
                : 14
                : 381-398
                [1 ]Centre for Neuroscience Studies, Queen’s University , Kingston, Ontario, Canada
                [2 ]Department of Biomedical and Molecular Sciences, Queen’s University , Kingston, Ontario, Canada
                [3 ]Department of Physics, Queen’s University , Kingston, Ontario, Canada
                Author notes
                Correspondence: Patrick W Stroman Queen’s University, Centre for Neuroscience Studies , 2nd Floor, Botterell Hall, 18 Stuart Street, Kingston, K7L 3N6, Ontario, CanadaTel +1-613-533-3245 Email stromanp@queensu.ca
                © 2021 Warren et al.

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                Page count
                Figures: 7, Tables: 7, References: 64, Pages: 18
                Original Research

                Anesthesiology & Pain management

                chronic pain, brain, pain, neuroimaging, fibromyalgia, human


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