A network theory of mental disorders.
TL;DR: The network theory has direct implications for how to understand diagnosis and treatment, and suggests a clear agenda for future research in psychiatry and associated disciplines.
About: This article is published in World Psychiatry.The article was published on 2017-02-01 and is currently open access. It has received 1311 citations till now. The article focuses on the topics: Mind-blindness & Psychological intervention.
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TL;DR: In this article, the authors describe how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data, and demonstrate the method in an empirical example on post-traumatic stress disorder data.
Abstract: Recent years have seen an emergence of network modeling applied to moods, attitudes, and problems in the realm of psychology. In this framework, psychological variables are understood to directly affect each other rather than being caused by an unobserved latent entity. In this tutorial, we introduce the reader to estimating the most popular network model for psychological data: the partial correlation network. We describe how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data. We show how to perform these analyses in R and demonstrate the method in an empirical example on post-traumatic stress disorder data. In addition, we discuss the effect of the hyperparameter that needs to be manually set by the researcher, how to handle non-normal data, how to determine the required sample size for a network analysis, and provide a checklist with potential solutions for problems that can arise when estimating regularized partial correlation networks.
839 citations
TL;DR: Challenges to network theory may propel the network approach from its adolescence into adulthood and promises advances in understanding psychopathology both at the nomothetic and idiographic level.
Abstract: Since the introduction of mental disorders as networks of causally interacting symptoms, this novel framework has received considerable attention. The past years have resulted in over 40 scientific publications and numerous conference symposia and workshops. Now is an excellent moment to take stock of the network approach: What are its most fundamental challenges, and what are potential ways forward in addressing them? After a brief conceptual introduction, we first discuss challenges to network theory: (1) What is the validity of the network approach beyond some commonly investigated disorders such as major depression? (2) How do we best define psychopathological networks and their constituent elements? And (3) how can we gain a better understanding of the causal nature and real-life underpinnings of associations among symptoms? Next, after a short technical introduction to network modeling, we discuss challenges to network methodology: (4) heterogeneity of samples studied with network analytic models, and (5) a lurking replicability crisis in this strongly data-driven and exploratory field. Addressing these challenges may propel the network approach from its adolescence into adulthood and promises advances in understanding psychopathology both at the nomothetic and idiographic level.
485 citations
Cites background from "A network theory of mental disorder..."
...However, the network theory of mental disorders (Borsboom, 2017; Kendler, Zachar, & Craver, 2011; McNally, 2012) was only recently connected to sophisticated psychometric models that allow us to estimate such networks for empirical data (Bringmann et al....
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...1 left): Insomnia can cause fatigue, psychomotor problems, and concentration problems, and these depression symptoms (APA, 2013) can form vicious circles of problems that are hard to escape (Borsboom, 2017; Borsboom & Cramer, 2013; Fried, van Borkulo, Cramer et al., 2016)....
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...As explained by Borsboom (2017), such external factors need not necessarily be outside the person....
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...1 left): Insomnia can cause fatigue, psychomotor problems, and concentration problems, and these depression symptoms (APA, 2013) can form vicious circles of problems that are hard to escape (Borsboom, 2017; Borsboom & Cramer, 2013; Fried, van Borkulo, Cramer et al., 2016)....
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...However, the network theory of mental disorders (Borsboom, 2017; Kendler, Zachar, & Craver, 2011; McNally, 2012) was only recently connected to sophisticated psychometric models that allow us to estimate such networks for empirical data (Bringmann et al., 2013; Bulteel, Tuerlinckx, Brose, &…...
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TL;DR: This tutorial introduces the reader to estimating the most popular network model for psychological data: the partial correlation network and describes how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data.
Abstract: Recent years have seen an emergence of network modeling applied to moods, attitudes, and problems in the realm of psychology. In this framework, psychological variables are understood to directly affect each other rather than being caused by an unobserved latent entity. In this tutorial, we introduce the reader to estimating the most popular network model for psychological data: the partial correlation network. We describe how regularization techniques can be used to efficiently estimate a parsimonious and interpretable network structure in psychological data. We show how to perform these analyses in R and demonstrate the method in an empirical example on posttraumatic stress disorder data. In addition, we discuss the effect of the hyperparameter that needs to be manually set by the researcher, how to handle non-normal data, how to determine the required sample size for a network analysis, and provide a checklist with potential solutions for problems that can arise when estimating regularized partial correlation networks. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
453 citations
Cites background from "A network theory of mental disorder..."
...Psychological networks thus offer a different view of item clusters: syndromes such as depression or anxiety disorder in the realm of mental disorders (Cramer et al., 2010; Borsboom, 2017; Fried et al., 2017b), personality facets or domains such as extraversion or neuroticism in personality research (Mõttus and Allerhand, 2017; Cramer et al....
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...…offer a different view of item clusters: syndromes such as depression or anxiety disorder in the realm of mental disorders (Cramer et al., 2010; Borsboom, 2017; Fried et al., 2017b), personality facets or domains such as extraversion or neuroticism in personality research (Mõttus and…...
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TL;DR: Critically examine several issues with the use of the most popular centrality indices in psychological networks: degree, betweenness, and closeness centrality, and conclude that betweenness and closness centrality seem especially unsuitable as measures of node importance.
Abstract: Centrality indices are a popular tool to analyze structural aspects of psychological networks. As centrality indices were originally developed in the context of social networks, it is unclear to what extent these indices are suitable in a psychological network context. In this article we critically examine several issues with the use of the most popular centrality indices in psychological networks: degree, betweenness, and closeness centrality. We show that problems with centrality indices discussed in the social network literature also apply to the psychological networks. Assumptions underlying centrality indices, such as presence of a flow and shortest paths, may not correspond with a general theory of how psychological variables relate to one another. Furthermore, the assumptions of node distinctiveness and node exchangeability may not hold in psychological networks. We conclude that, for psychological networks, betweenness and closeness centrality seem especially unsuitable as measures of node importance. We therefore suggest three ways forward: (a) using centrality measures that are tailored to the psychological network context, (b) reconsidering existing measures of importance used in statistical models underlying psychological networks, and (c) discarding the concept of node centrality entirely. Foremost, we argue that one has to make explicit what one means when one states that a node is central, and what assumptions the centrality measure of choice entails, to make sure that there is a match between the process under study and the centrality measure that is used. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
438 citations
Cites background from "A network theory of mental disorder..."
...…shows that shifting the focus away from single variables, to how the network emerges and behaves as a whole, might reveal more insights into the dynamics of psychopathology, leading to more fruitful therapy approaches (Borsboom, 2017; Cramer et al., 2016; Wichers, Wigman, & Myin-Germeys, 2015)....
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...For example, sleeping problems can lead to tiredness, which in turn can trigger sadness, and as the downward spiral progresses, symptoms reinforce one other, eventually resulting in a full-blown depression (Borsboom, 2017; Cramer et al., 2010)....
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TL;DR: Four network statistics to identify bridge symptoms are developed: bridge strength, bridge betweenness, bridge closeness, and bridge expected influence, which are nonspecific to the type of network estimated, making them potentially useful in individual-level psychometric networks, group-level psychology networks, and networks outside the field of psychopathology such as social networks.
Abstract: Recently, researchers in clinical psychology have endeavored to create network models of the relationships between symptoms, both within and across mental disorders. Symptoms that connect two mental disorders are called "bridge symptoms." Unfortunately, no formal quantitative methods for identifying these bridge symptoms exist. Accordingly, we developed four network statistics to identify bridge symptoms: bridge strength, bridge betweenness, bridge closeness, and bridge expected influence. These statistics are nonspecific to the type of network estimated, making them potentially useful in individual-level psychometric networks, group-level psychometric networks, and networks outside the field of psychopathology such as social networks. We first tested the fidelity of our statistics in predicting bridge nodes in a series of simulations. Averaged across all conditions, the statistics achieved a sensitivity of 92.7% and a specificity of 84.9%. By simulating datasets of varying sample sizes, we tested the robustness of our statistics, confirming their suitability for network psychometrics. Furthermore, we simulated the contagion of one mental disorder to another, showing that deactivating bridge nodes prevents the spread of comorbidity (i.e., one disorder activating another). Eliminating nodes based on bridge statistics was more effective than eliminating nodes high on traditional centrality statistics in preventing comorbidity. Finally, we applied our algorithms to 18 group-level empirical comorbidity networks from published studies and discussed the implications of this analysis.
355 citations
Cites background or methods from "A network theory of mental disorder..."
...An emerging approach to psychopathology and comorbidity is the network model (Borsboom, 2017; Borsboom & Cramer, 2013; Cramer, Waldorp, van der Maas, & Borsboom, 2010; McNally, 2016)....
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...This simulation therefore rests on assumptions made in network theory (Borsboom, 2017) that nodes do indeed spread activation through causal patterns and represents a simulation of important bridges being detected given a causal network structure....
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...In this article, we use psychometric network terminology, especially as it pertains to clinical psychology (e.g., Borsboom, 2017)....
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References
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TL;DR: The art and science of cause and effect have been studied in the social sciences for a long time as mentioned in this paper, see, e.g., the theory of inferred causation, causal diagrams and the identification of causal effects.
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TL;DR: The qgraph package for R is presented, which provides an interface to visualize data through network modeling techniques, and is introduced by applying the package functions to data from the NEO-PI-R, a widely used personality questionnaire.
Abstract: We present the qgraph package for R, which provides an interface to visualize data through network modeling techniques. For instance, a correlation matrix can be represented as a network in which each variable is a node and each correlation an edge; by varying the width of the edges according to the magnitude of the correlation, the structure of the correlation matrix can be visualized. A wide variety of matrices that are used in statistics can be represented in this fashion, for example matrices that contain (implied) covariances, factor loadings, regression parameters and p values. qgraph can also be used as a psychometric tool, as it performs exploratory and confirmatory factor analysis, using sem and lavaan; the output of these packages is automatically visualized in qgraph ,w hich may aid the interpretation of results. In this article, we introduce qgraph by applying the package functions to data from the NEO-PI-R, a widely used personality questionnaire.
2,338 citations
TL;DR: An examines methodologies suited to identify such symptom networks and discusses network analysis techniques that may be used to extract clinically and scientifically useful information from such networks (e.g., which symptom is most central in a person's network).
Abstract: In network approaches to psychopathology, disorders result from the causal interplay between symptoms (e.g., worry → insomnia → fatigue), possibly involving feedback loops (e.g., a person may engage in substance abuse to forget the problems that arose due to substance abuse). The present review examines methodologies suited to identify such symptom networks and discusses network analysis techniques that may be used to extract clinically and scientifically useful information from such networks (e.g., which symptom is most central in a person's network). The authors also show how network analysis techniques may be used to construct simulation models that mimic symptom dynamics. Network approaches naturally explain the limited success of traditional research strategies, which are typically based on the idea that symptoms are manifestations of some common underlying factor, while offering promising methodological alternatives. In addition, these techniques may offer possibilities to guide and evaluate therape...
1,824 citations
TL;DR: The structure of psychopathology is examined, taking into account dimensionality, persistence, co-occurrence, and sequential comorbidity of mental disorders across 20 years, from adolescence to midlife, to explain why it is challenging to find causes, consequences, biomarkers, and treatments with specificity to individual mental disorders.
Abstract: Mental disorders traditionally have been viewed as distinct, episodic, and categorical conditions. This view has been challenged by evidence that many disorders are sequentially comorbid, recurrent/chronic, and exist on a continuum. Using the Dunedin Multidisciplinary Health and Development Study, we examined the structure of psychopathology, taking into account dimensionality, persistence, co-occurrence, and sequential comorbidity of mental disorders across 20 years, from adolescence to midlife. Psychiatric disorders were initially explained by three higher-order factors (Internalizing, Externalizing, and Thought Disorder) but explained even better with one General Psychopathology dimension. We have called this dimension the p factor because it conceptually parallels a familiar dimension in psychological science: the g factor of general intelligence. Higher p scores are associated with more life impairment, greater familiality, worse developmental histories, and more compromised early-life brain function. The p factor explains why it is challenging to find causes, consequences, biomarkers, and treatments with specificity to individual mental disorders. Transdiagnostic approaches may improve research.
1,715 citations