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      Effects of tumor necrosis factor-α polymorphism on the brain structural changes of the patients with major depressive disorder

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          Abstract

          Single Nucleotide Polymorphic (SNP) variations of proinflammatory cytokines such as Tumor Necrosis Factor-α (TNF-α) have been reported to be closely associated with the major depressive disorder (MDD). However, it is unclear if proinflammatory genetic burden adversely affects the regional gray matter volume in patients with MDD. The aim of this study was to test whether rs1799724, an SNP of TNF-α, contributes to the neuroanatomical changes in MDD. In this cross-sectional study, a total of 144 MDD patients and 111 healthy controls (HC) well matched for age, sex and education were recruited from Shanghai Mental Health Center. Voxel-based morphometry (VBM) followed by graph theory based structural covariance analysis was applied to locate diagnosis x genotype interactions. Irrespective of diagnosis, individuals with the high-risk genotype (T-carriers) had reduced volume in left angular gyrus (main effect of genotype). Diagnosis x genotype interaction was exclusively localized to the visual cortex (right superior occipital gyrus). The same region also showed reduced volume in patients with MDD than HC (main effect of diagnosis), with this effect being most pronounced in patients carrying the high-risk genotype. However, neither global nor regional network of structural covariance was found to have group difference. In conclusion, a genetic variation which can increase TNF-α expression selectively affects the anatomy of the visual cortex among the depressed subjects, with no effect on the topographical organization of multiple cortical regions. This supports the notion that anatomical changes in depression are in part influenced by the genetic determinants of inflammatory activity.

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          Interleukin (IL)-6, tumour necrosis factor alpha (TNF-α) and soluble interleukin-2 receptors (sIL-2R) are elevated in patients with major depressive disorder: a meta-analysis and meta-regression.

          Many studies have explored the association between soluble interleukin-2 receptor (sIL-2R), cytokines and major depressive disorder (MDD). However, the results of these studies were not consistent. The aim of our study is to compare the levels of sIL-2R and cytokines in the blood between MDD patients and controls by a meta-analysis and to identify moderators accounting for potential heterogeneity in the levels of sIL-2R and cytokines in MDD patients versus controls by meta-regression analyses. A comprehensive literature search was performed to identify studies comparing the levels of sIL-2R and cytokines between MDD patients and controls. We pooled the effect sizes for standardized mean differences (SMD) of the levels of sIL-2R and cytokines. We also performed meta-regression and sensitivity analyses to investigate the roles of age, gender, sample type, ethnic origin and selected studies' quality in explaining potential heterogeneity and differences in results respectively. Twenty-nine studies were selected for this analysis. The levels of sIL-2R, TNF-α and IL-6 in MDD patients were significantly higher than those of healthy controls (SMD=0.555, p<0.001, SMD=0.567, p=0.010; SMD=0.680, p<0.001). Mean age of all subjects was a significant moderator to explain the high heterogeneity of IL-6. Sensitivity analysis found that European but not non-European subjects have higher levels difference of sIL-2R, TNF-α and IL-1β between MDD patients and controls. The severity of MDD was not considered. The blood levels of sIL-2R, TNF-α and IL-6 were significantly higher in MDD patients than controls. Age, samples source and ethnic origins may play a potential role in heterogeneity. Copyright © 2011 Elsevier B.V. All rights reserved.
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            The effect of antidepressant medication treatment on serum levels of inflammatory cytokines: a meta-analysis.

            Serum levels of inflammatory cytokines, for example, tumor necrosis factor alpha (TNFα), interleukin-6 (IL-6), and IL-1 beta (IL-1β), are elevated in subjects with major depressive disorder (MDD). The reason why this occurs is unclear. Elevated levels of inflammatory cytokines could be a result of brain dysfunction in MDD. It is also possible that inflammatory cytokines contribute to depressive symptoms in MDD. If the first assumption is correct, one would expect levels to normalize with resolution of the depressive episode after treatment. Several studies have measured changes in cytokine levels during antidepressant treatment; however, the results vary. The purpose of this study was to pool all available data on changes in serum levels of TNFα, IL-6, and IL-1β during antidepressant treatment to determine whether these levels change. Studies were included if they used an approved pharmacological treatment for depression, patients had a diagnosis of MDD, and serum levels of TNFα, IL-6, and/or IL-1β were measured before and after treatment. Twenty-two studies fulfilled these criteria. Meta-analysis of these studies showed that, overall, while pharmacological antidepressant treatment reduced depressive symptoms, it did not reduce serum levels of TNFα. On the other hand, antidepressant treatment did reduce levels of IL-1β and possibly those of IL-6. Stratified subgroup analysis by class of antidepressant indicated that serotonin reuptake inhibitors may reduce levels of IL-6 and TNFα. Other antidepressants, while efficacious for depressive symptoms, did not appear to reduce cytokine levels. These results argue against the notion that resolution of a depressive episode is associated with normalization of levels of circulating inflammatory cytokines; however, the results are consistent with the possibility that inflammatory cytokines contribute to depressive symptoms and that antidepressants block the effects of inflammatory cytokines on the brain.
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              Can parametric statistical methods be trusted for fMRI based group studies?

              The most widely used task fMRI analyses use parametric methods that depend on a variety of assumptions. While individual aspects of these fMRI models have been evaluated, they have not been evaluated in a comprehensive manner with empirical data. In this work, a total of 2 million random task fMRI group analyses have been performed using resting state fMRI data, to compute empirical familywise error rates for the software packages SPM, FSL and AFNI, as well as a standard non-parametric permutation method. While there is some variation, for a nominal familywise error rate of 5% the parametric statistical methods are shown to be conservative for voxel-wise inference and invalid for cluster-wise inference; in particular, cluster size inference with a cluster defining threshold of p = 0.01 generates familywise error rates up to 60%. We conduct a number of follow up analyses and investigations that suggest the cause of the invalid cluster inferences is spatial auto correlation functions that do not follow the assumed Gaussian shape. By comparison, the non-parametric permutation test, which is based on a small number of assumptions, is found to produce valid results for voxel as well as cluster wise inference. Using real task data, we compare the results between one parametric method and the permutation test, and find stark differences in the conclusions drawn between the two using cluster inference. These findings speak to the need of validating the statistical methods being used in the neuroimaging field.
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                Author and article information

                Contributors
                yirufang@aliyun.com
                lpalaniy@uwo.ca
                doctorcj2010@gmail.com
                jijunwang27@163.com
                Journal
                Transl Psychiatry
                Transl Psychiatry
                Translational Psychiatry
                Nature Publishing Group UK (London )
                2158-3188
                11 October 2018
                11 October 2018
                2018
                : 8
                : 217
                Affiliations
                [1 ]ISNI 0000 0004 0368 8293, GRID grid.16821.3c, Division of Mood Disorders, Shanghai Mental Health Center, , Shanghai Jiao Tong University School of Medicine, ; Shanghai, China
                [2 ]CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China
                [3 ]ISNI 0000 0004 1782 6212, GRID grid.415630.5, Shanghai Key Laboratory of Psychotic disorders, ; Shanghai, China
                [4 ]ISNI 0000 0004 0368 8293, GRID grid.16821.3c, Department of EEG & Neuroimaging, Shanghai Mental Health Center, , Shanghai Jiao Tong University School of Medicine, ; Shanghai, China
                [5 ]ISNI 0000 0004 0369 313X, GRID grid.419897.a, Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), ; Shanghai, China
                [6 ]ISNI 0000 0004 0368 8293, GRID grid.16821.3c, Brain Science and Technology Research Center, , Shanghai Jiao Tong University, ; Shanghai, China
                [7 ]ISNI 0000 0004 1936 8884, GRID grid.39381.30, Robarts Research Institute& The Brain and Mind Institute, , University of Western Ontario, ; London, ON Canada
                [8 ]ISNI 0000 0004 1936 8884, GRID grid.39381.30, Department of Psychiatry, , University of Western Ontario, ; London, ON Canada
                [9 ]ISNI 0000 0001 0556 2414, GRID grid.415847.b, Lawson Health Research Institute, ; London, ON Canada
                [10 ]Hongkou District Mental Health Center of Shanghai, Shanghai, China
                [11 ]ISNI 0000 0004 1769 9639, GRID grid.460018.b, Department of Psychology, , Provincial Hospital Affiliated to Shandong University, ; Jinan, 250021 China
                [12 ]ISNI 0000 0004 0368 8293, GRID grid.16821.3c, Department of Medical Psychology, Xinhua Hospital, , Shanghai Jiao Tong University School of Medicine, ; Shanghai, China
                Article
                256
                10.1038/s41398-018-0256-x
                6181976
                30310056
                feecfbea-4924-4f8a-b73a-6b79b3885195
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 20 March 2018
                : 30 July 2018
                : 5 August 2018
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 91232719
                Award ID: 81771465
                Award ID: 81301159
                Award ID: 81201056
                Award Recipient :
                Funded by: National Key R&D Program of China (2016YFC1307100), The “12th Five-year Plan” of National Key Technologies R&D Program (2012BAI01B04).
                Categories
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                © The Author(s) 2018

                Clinical Psychology & Psychiatry
                Clinical Psychology & Psychiatry

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