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      Insights from a laboratory and naturalistic investigation on stress, rumination and frontal brain functioning in MDD: An fNIRS study

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          Abstract

          Recent research has emphasized rumination as an important maintaining factor in various mental disorders. However, operationalization and therefore induction of rumination in experimental settings poses a major challenge in terms of ecological validity. As stress seems to play a key role in everyday situations eliciting rumination, we conducted two stress paradigms while assessing behavioral and neurophysiological measures.

          Aiming to replicate previous findings on induced rumination by means of the Trier Social Stress Test (TSST) and comparing them to physiological (pain) stress, a clinical sample of patients with Major Depressive Disorder (MDD; n = 22) and healthy controls (HC; n = 23) was recruited. Cortical blood oxygenation was assessed during the stress paradigms using functional near-infrared spectroscopy (fNIRS). Further, we used ecological momentary assessment (EMA) of stress, rumination and mood to be able to correlate ruminative responses during induced stress and everyday rumination.

          Our results showed that social stress but not physiological stress induced depressive rumination in MDD but not in HC. Further, rumination reactivity in response to social stress but not to physiological stress was significantly associated with rumination reactivity in everyday life as assessed with EMA. With respect to cortical oxygenation, MDD subjects showed hypoactivity in the Cognitive Control Network during the TSST, which mediated the differences between MDD and HC in post-stress rumination.

          Our findings emphasize the role of negative social triggers in depressive rumination and validate the TSST as an induction method for depressive rumination. The results inform future developments in psychotherapeutic treatment for depressive rumination.

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          Most cited references64

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          lmerTest Package: Tests in Linear Mixed Effects Models

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            Fitting Linear Mixed-Effects Models Using lme4

            Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660
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              Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs

              Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to calculate and report effect sizes for t-tests and ANOVA's such that effect sizes can be used in a-priori power analyses and meta-analyses. Whereas many articles about effect sizes focus on between-subjects designs and address within-subjects designs only briefly, I provide a detailed overview of the similarities and differences between within- and between-subjects designs. I suggest that some research questions in experimental psychology examine inherently intra-individual effects, which makes effect sizes that incorporate the correlation between measures the best summary of the results. Finally, a supplementary spreadsheet is provided to make it as easy as possible for researchers to incorporate effect size calculations into their workflow.
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                Author and article information

                Contributors
                Journal
                Neurobiol Stress
                Neurobiol Stress
                Neurobiology of Stress
                Elsevier
                2352-2895
                24 May 2021
                November 2021
                24 May 2021
                : 15
                : 100344
                Affiliations
                [a ]Department of Psychiatry and Psychotherapy, University Hospital of Tuebingen, Tuebingen, Germany
                [b ]Department of Psychology, University of Tuebingen, Germany
                [c ]Psychotherapy Research Lab, Psychology and Sport Sciences, Justus-Liebig-University Giessen, Giessen, Germany
                [d ]Machine Learning Solutions, Luxembourg, Luxembourg
                [e ]LEAD Graduate School & Research Network, University of Tuebingen, Tuebingen, Germany
                [f ]German Center for Neurodegenerative Disorders, Tuebingen, Germany
                Author notes
                []Corresponding author. Universitätsklinikum, Calwerstraße 14, 72076, Tuebingen, Germany. david.rosenbaum@ 123456med.uni-tuebingen.de
                Article
                S2352-2895(21)00052-7 100344
                10.1016/j.ynstr.2021.100344
                8173308
                34124320
                b750badc-e22e-48e5-bbd6-138a5cce303a
                © 2021 The Authors

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

                History
                : 2 September 2020
                : 19 January 2021
                : 18 May 2021
                Categories
                Original Research Article

                rumination,trier social stress test (tsst),socially evaluated cold-pressor test (secpt),functional near-infrared spectroscopy (fnirs),repetitive negative thought,major depressive disorder

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