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      Grey and white matter differences in Chronic Fatigue Syndrome – A voxel-based morphometry study

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          Abstract

          Objective

          Investigate global and regional grey and white matter volumes in patients with Chronic Fatigue Syndrome (CFS) using magnetic resonance imaging (MRI) and recent voxel-based morphometry (VBM) methods.

          Methods

          Forty-two patients with CFS and thirty healthy volunteers were scanned on a 3-Tesla MRI scanner. Anatomical MRI scans were segmented, normalized and submitted to a VBM analysis using randomisation methods. Group differences were identified in overall segment volumes and voxel-wise in spatially normalized grey matter (GM) and white matter (WM) segments.

          Results

          Accounting for total intracranial volume, patients had larger GM volume and lower WM volume. The voxel-wise analysis showed increased GM volume in several structures including the amygdala and insula in the patient group. Reductions in WM volume in the patient group were seen primarily in the midbrain, pons and right temporal lobe.

          Conclusion

          Elevated GM volume in CFS is seen in areas related to processing of interoceptive signals and stress. Reduced WM volume in the patient group partially supports earlier findings of WM abnormalities in regions of the midbrain and brainstem.

          Highlights

          • VBM study of patients with Chronic Fatigue Syndrome without depression.

          • Patients show increased grey matter in insular cortex and parts of the limbic system.

          • Patients show decrease in white matter in midbrain and temporal lobe.

          • Findings suggest potentially altered processing of interoceptive signals.

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          Most cited references29

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          The Cognitive Failures Questionnaire (CFQ) and its correlates.

          This paper describes a questionnaire measure of self-reported failures in perception, memory, and motor function. Responses to all questions tend to be positively correlated, and the whole questionnaire correlates with other recent measures of self-reported deficit in memory, absent-mindedness, or slips of action. The questionnaire is however only weakly correlated with indices of social desirability set or of neuroticism. It is significantly correlated with ratings of the respondent by his or her spouse, and accordingly does have some external significance rather than purely private opinion of the self. The score is reasonably stable over long periods, to about the same extent as traditional measures of trait rather than state. Furthermore, it has not thus far been found to change in persons exposed to life-stresses. However, it does frequently correlate with the number of current psychiatric symptoms reported by the same person on the MHQ; and in one study it has been found that CFQ predicts subsequent MHQ in persons who work at a stressful job in the interval. It does not do so in those who work in a less stressful environment. The most plausible view is that cognitive failure makes a person vulnerable to showing bad effects of stress, rather than itself resulting from stress.
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            COMPASS 31: a refined and abbreviated Composite Autonomic Symptom Score.

            To develop a concise and statistically robust instrument to assess autonomic symptoms that provides clinically relevant scores of autonomic symptom severity based on the well-established 169-item Autonomic Symptom Profile (ASP) and its validated 84-question scoring instrument, the Composite Autonomic Symptom Score (COMPASS).
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              Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer's disease participants.

              The purpose of this paper is to establish single-participant white matter atlases based on diffusion tensor imaging. As one of the applications of the atlas, automated brain segmentation was performed and the accuracy was measured using Large Deformation Diffeomorphic Metric Mapping (LDDMM). High-quality diffusion tensor imaging (DTI) data from a single-participant were B0-distortion-corrected and transformed to the ICBM-152 atlas or to Talairach coordinates. The deep white matter structures, which have been previously well documented and clearly identified by DTI, were manually segmented. The superficial white matter areas beneath the cortex were defined, based on a population-averaged white matter probability map. The white matter was parcellated into 176 regions based on the anatomical labeling in the ICBM-DTI-81 atlas. The automated parcellation was achieved by warping this parcellation map to normal controls and to Alzheimer's disease patients with severe anatomical atrophy. The parcellation accuracy was measured by a kappa analysis between the automated and manual parcellation at 11 anatomical regions. The kappa values were 0.70 for both normal controls and patients while the inter-rater reproducibility was 0.81 (controls) and 0.82 (patients), suggesting "almost perfect" agreement. A power analysis suggested that the proposed method is suitable for detecting FA and size abnormalities of the white matter in clinical studies.
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                Author and article information

                Contributors
                Journal
                Neuroimage Clin
                Neuroimage Clin
                NeuroImage : Clinical
                Elsevier
                2213-1582
                28 September 2017
                2018
                28 September 2017
                : 17
                : 24-30
                Affiliations
                [a ]Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, England, UK
                [b ]Aberdeen Biomedical Imaging Centre, University of Aberdeen, Scotland, UK
                [c ]Department of Public Health and Community Medicine, Göteborgs Universitet, Göteborg, Sweden
                [d ]Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne, England, UK
                [e ]Newcastle Magnetic Resonance Centre, Newcastle University, Newcastle upon Tyne, England, UK
                Author notes
                [* ]Corresponding author at: Wolfson Research Centre, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, UK.Wolfson Research CentreCampus for Ageing and VitalityNewcastle upon TyneNE4 5PLUK Andreas.finkelmeyer@ 123456newcastle.ac.uk
                Article
                S2213-1582(17)30236-X
                10.1016/j.nicl.2017.09.024
                5633338
                29021956
                5ba3e306-4f31-42a4-b44a-46cd81101938
                © 2017 The Author(s)

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 28 April 2017
                : 7 September 2017
                : 26 September 2017
                Categories
                Regular Article

                chronic fatigue syndrome,voxel-based morphometry,insula,amygdala,midbrain

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