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      Anhedonia as a transdiagnostic symptom across psychological disorders: a network approach

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          Abstract

          Background

          Anhedonia is apparent in different mental disorders and is suggested to be related to dysfunctions in the reward system and/or affect regulation. It may hence be a common underlying feature associated with symptom severity of mental disorders.

          Methods

          We constructed a cross-sectional graphical Least Absolute Shrinkage and Selection Operator (LASSO) network and a relative importance network to estimate the relationships between anhedonia severity and the severity of symptom clusters of major depressive disorder (MDD), anxiety sensitivity (AS), attention-deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD) in a sample of Dutch adult psychiatric patients ( N = 557).

          Results

          Both these networks revealed anhedonia severity and depression symptom severity as central to the network. Results suggest that anhedonia severity may be predictive of the severity of symptom clusters of MDD, AS, ADHD, and ASD. MDD symptom severity may be predictive of AS and ADHD symptom severity.

          Conclusions

          The results suggest that anhedonia may serve as a common underlying transdiagnostic psychopathology feature, predictive of the severity of symptom clusters of depression, AS, ADHD, and ASD. Thus, anhedonia may be associated with the high comorbidity between these symptom clusters and disorders. If our results will be replicated in future studies, it is recommended for clinicians to be more vigilant about screening for anhedonia and/or depression severity in individuals diagnosed with an anxiety disorder, ADHD and/or ASD.

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          Most cited references98

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          Sparse inverse covariance estimation with the graphical lasso.

          We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the graphical lasso--that is remarkably fast: It solves a 1000-node problem ( approximately 500,000 parameters) in at most a minute and is 30-4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.
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            Estimating psychological networks and their accuracy: A tutorial paper

            The usage of psychological networks that conceptualize behavior as a complex interplay of psychological and other components has gained increasing popularity in various research fields. While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation. We first introduce the current state-of-the-art of network estimation. Second, we provide a rationale why researchers should investigate the accuracy of psychological networks. Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices. We conducted and present simulation studies to assess the performance of both methods. Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods. We showcase bootnet in a tutorial, accompanied by R syntax, in which we analyze a dataset of 359 women with posttraumatic stress disorder available online. Electronic supplementary material The online version of this article (doi:10.3758/s13428-017-0862-1) contains supplementary material, which is available to authorized users.
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              Research domain criteria (RDoC): toward a new classification framework for research on mental disorders.

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                Author and article information

                Journal
                Psychol Med
                Psychol Med
                PSM
                Psychological Medicine
                Cambridge University Press (Cambridge, UK )
                0033-2917
                1469-8978
                July 2023
                29 March 2022
                : 53
                : 9
                : 3908-3919
                Affiliations
                [1 ]Behavioural Science Institute, Radboud University , Nijmegen, The Netherlands
                [2 ]Overwaal, Center of Expertise for Anxiety, Obsessive-Compulsive, and Posttraumatic Stress Disorders, Pro Persona, Institute for Integrated Mental Health Care , Nijmegen, The Netherlands
                [3 ]Department of Psychiatry, Radboud University Medical Center , Nijmegen, The Netherlands
                [4 ]Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen , Nijmegen, The Netherlands
                [5 ]Depression Expertise Center, Pro Persona Mental Health Care , Nijmegen, The Netherlands
                [6 ]Department of Psychiatry and Psychotherapy, University Hospital Essen , Essen, Germany
                Author notes
                Author for correspondence: Melissa G. Guineau, E-mail: m.guineau@ 123456propersona.nl
                [*]

                deceased

                Author information
                https://orcid.org/0000-0003-1729-4589
                Article
                S0033291722000575
                10.1017/S0033291722000575
                10317820
                35348051
                e498a5a4-25da-4473-a5f9-77b9c731e006
                © The Author(s) 2022

                This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.

                History
                : 21 June 2021
                : 09 February 2022
                : 16 February 2022
                Page count
                Figures: 3, Tables: 2, References: 100, Pages: 12
                Categories
                Original Article

                Clinical Psychology & Psychiatry
                anhedonia,comorbidity,network approach,rdoc
                Clinical Psychology & Psychiatry
                anhedonia, comorbidity, network approach, rdoc

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