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

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Abstract

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.

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Sparse inverse covariance estimation with the graphical lasso.

J. Friedman, T. Hastie, R. Tibshirani (2008)

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

Sacha Epskamp, Denny Borsboom, Eiko I Fried (2017)

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.

Thomas Insel, Bruce Cuthbert, Marjorie Garvey … (2010)

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Author and article information

Journal

Journal ID (iso-abbrev): Psychol Med

Title: Psychological medicine

Publisher: Cambridge University Press (CUP)

ISSN (Electronic): 1469-8978

ISSN (Print): 0033-2917

Publication date (Electronic): Mar 29 2022

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.