The replicability and generalizability of internalizing symptom networks across five samples.
TL;DR: There were considerable similarities in network structure, the presence and sign of individual edges, and the most central symptom within and across internalizing symptom networks estimated from nonclinical samples, but global metrics suggested network structure and symptom centrality had weak to moderate generalizability from non clinical to clinical samples.
Abstract: The popularity of network analysis in psychopathology research has increased exponentially in recent years. Yet, little research has examined the replicability of cross-sectional psychopathology network models, and those that have used single items for symptoms rather than multiitem scales. The present study therefore examined the replicability and generalizability of regularized partial correlation networks of internalizing symptoms within and across 5 samples (total N = 2,573) using the Inventory for Depression and Anxiety Symptoms, a factor analytically derived measure of individual internalizing symptoms. As different metrics may yield different conclusions about the replicability of network parameters, we examined both global and specific metrics of similarity between networks. Correlations within and between nonclinical samples suggested considerable global similarities in network structure (rss = .53-.87) and centrality strength (rss = .37-.86), but weaker similarities in network structure (rss = .36-.66) and centrality (rss = .04-.54) between clinical and nonclinical samples. Global strength (i.e., connectivity) did not significantly differ across all 5 networks and few edges (0-5.5%) significantly differed between networks. Specific metrics of similarity indicated that, on average, approximately 80% of edges were consistently estimated within and between all 5 samples. The most central symptom (i.e., dysphoria) was consistent within and across samples, but there were few other matches in centrality rank-order. In sum, there were considerable similarities in network structure, the presence and sign of individual edges, and the most central symptom within and across internalizing symptom networks estimated from nonclinical samples, but global metrics suggested network structure and symptom centrality had weak to moderate generalizability from nonclinical to clinical samples. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
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TL;DR: This Introduction to a Special Section on Transdiagnostic Approaches to Psychopathology provides a narrative review of the transdiagnostic literature and outlines several challenges it currently faces that arguably limit its applicability in current mental health science and practice.
Abstract: Despite a longstanding and widespread influence of the diagnostic approach to mental ill health, there is an emerging and growing consensus that such psychiatric nosologies may no longer be fit for purpose in research and clinical practice. In their place, there is gathering support for a "transdiagnostic" approach that cuts across traditional diagnostic boundaries or, more radically, sets them aside altogether, to provide novel insights into how we might understand mental health difficulties. Removing the distinctions between proposed psychiatric taxa at the level of classification opens up new ways of classifying mental health problems, suggests alternative conceptualizations of the processes implicated in mental health, and provides a platform for novel ways of thinking about onset, maintenance, and clinical treatment and recovery from experiences of disabling mental distress. In this Introduction to a Special Section on Transdiagnostic Approaches to Psychopathology, we provide a narrative review of the transdiagnostic literature in order to situate the Special Section articles in context. We begin with a brief history of the diagnostic approach and outline several challenges it currently faces that arguably limit its applicability in current mental health science and practice. We then review several recent transdiagnostic approaches to classification, biopsychosocial processes, and clinical interventions, highlighting promising novel developments. Finally, we present some key challenges facing transdiagnostic science and make suggestions for a way forward. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
227 citations
TL;DR: Investigating the network models of four different trauma samples of complex posttraumatic stress disorder (CPTSD) found the replicability of CPTSD network models across different samples is supported and provides further evidence about the robust structure of the disorder.
Abstract: The 11th revision of the World Health Organization's International Classification of Diseases (ICD‐11) includes a new disorder, complex posttraumatic stress disorder (CPTSD). The network approach to psychopathology enables investigation of the structure of disorders at the symptom level, which allows for analysis of direct symptom interactions. The network structure of ICD‐11 CPTSD has not yet been studied, and it remains unclear whether similar networks replicate across different samples. We investigated the network models of four different trauma samples that included a total of 879 participants (M age = 47.17 years, SD = 11.92; 59.04% women) drawn from Austria, Lithuania, and Scotland and Wales in the United Kingdom. The International Trauma Questionnaire was used to assess symptoms of ICD‐11 CPTSD in all samples. The prevalence of PTSD and CPTSD ranged from 23.7% to 37.3% and from 9.3% to 53.1%, respectively. Regularized partial correlation networks were estimated and the resulting networks compared. Despite several differences in the symptom presentation and cultural background, the networks across the four samples were considerably similar, with high correlations between symptom profiles (ρs = .48–.87), network structures (ρs = .69–.75), and centrality estimates (ρs = .59–.82). These results support the replicability of CPTSD network models across different samples and provide further evidence about the robust structure of CPTSD. The most central symptom in all four sample‐specific networks and the overall network was “feelings of worthlessness.” Implications of the network approach in research and practice are discussed.
28 citations
TL;DR: The findings demonstrate the directionality of relationships between individual symptoms in youth and highlight depressed mood, inattention, and worry as potential influencers of other symptoms.
Abstract: Background The network theory suggests that psychopathology may reflect causal relationships between individual symptoms. Several studies have examined cross-sectional relationships between individual symptoms in youth. However, these studies cannot address the directionality of the temporal relationships hypothesized by the network theory. Therefore, we estimated the longitudinal relationships between individual internalizing, externalizing, and attention symptoms in youth. Methods Data from 4,093 youth participants in the Adolescent Brain Cognitive Development (ABCD) study were used. Symptoms were assessed using the Brief Problem Monitor, which was administered at three time points spaced six months apart. Unique longitudinal relationships between symptoms at T1 and T2 were estimated using cross-lagged panel network modeling. Network replicability was assessed by comparing this network to an identically estimated replication network of symptoms at T2 predicting symptoms at T3. Results After controlling for all other symptoms and demographic covariates, depressed mood, inattention, and worry at T1 were most predictive of other symptoms at T2. In contrast, threats of violence and destructiveness at T2 were most prospectively predicted by other symptoms at T1. The reciprocal associations between depressed mood and worthlessness were among the strongest bivariate relationships in the network. Comparisons between the original network and the replication network (correlation between edge lists = .61; individual edge replicability = 64%-84%) suggested moderate replicability. Conclusions Although causal inferences are precluded by the observational design and methodological considerations, these findings demonstrate the directionality of relationships between individual symptoms in youth and highlight depressed mood, inattention, and worry as potential influencers of other symptoms.
26 citations
Cites methods or result from "The replicability and generalizabil..."
...As there is ongoing controversy regarding the replicability of network models (e.g., Forbes, Wright, Markon, & Krueger, 2019; Funkhouser et al., 2020) and symptoms were assessed at three time points, we examined the replicability of the CLPN by comparing the results of a CLPN using T1 symptoms to…...
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...The high out-EI of depressed mood is consistent with findings from cross-sectional network analyses in both youth and adults (Funkhouser et al., 2020; McElroy et al., 2018)....
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TL;DR: In this article , the authors performed an approximated replication of a previous network analysis study investigating how different clinical aspects, including psychopathology, cognition, personal resources, functional capacity, and real-life functioning, are interrelated in the context of schizophrenia spectrum disorders.
Abstract: Abstract Background and hypothesis Recovery from psychosis is a complex phenomenon determined by an array of variables mutually impacting each other in a manner that is not fully understood. The aim of this study is to perform an approximated replication of a previous network analysis study investigating how different clinical aspects—covering psychopathology, cognition, personal resources, functional capacity, and real-life functioning—are interrelated in the context of schizophrenia-spectrum disorders. Study design A sample of 843 subjects from a multisite cohort study, with the diagnosis of a schizophrenia-spectrum disorder, was used to estimate a network comprising 27 variables. The connectivity and relative importance of the variables was examined through network analysis. We used a quantitative and qualitative approach to infer replication quality. Study results Functional capacity and real-life functioning were central and bridged different domains of the network, in line with the replicated study. Neurocognition, interpersonal relationships, and avolition were also key elements of the network, in close relation to aspects of functioning. Despite significant methodological differences, the current study could substantially replicate previous findings. Conclusions Results solidify the network analysis approach in the context of mental disorders and further inform future studies about key variables in the context of recovery from psychotic disorders.
16 citations
TL;DR: This paper investigated conditional dependence relationships of impulse dyscontrol symptoms in mild cognitive impairment (MCI) and subjective cognitive decline (SCD) and found that Stubbornness/rigidity, agitation/aggressiveness, and argumentativeness were frequent and the most central symptoms in the network.
Abstract: Objectives To investigate conditional dependence relationships of impulse dyscontrol symptoms in mild cognitive impairment (MCI) and subjective cognitive decline (SCD). Design A prospective, observational study. Participants Two hundred and thirty-five patients with MCI (n = 159) or SCD (n = 76) from the Prospective Study for Persons with Memory Symptoms dataset. Measurements Items of the Mild Behavioral Impairment Checklist impulse dyscontrol subscale. Results Stubbornness/rigidity, agitation/aggressiveness, and argumentativeness were frequent and the most central symptoms in the network. Impulsivity, the fourth most central symptom in the network, served as the bridge between these common symptoms and less central and rare symptoms. Conclusions Impulse dyscontrol in at-risk states for dementia is characterized by closely connected symptoms of irritability, agitation, and rigidity. Compulsions and difficulties in regulating rewarding behaviors are relatively isolated symptoms.
11 citations
References
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TL;DR: In this article, a tripartite structure consisting of general distress, physiological hyperarousal (specific anxiety), and anhedonia (specific depression), and a diagnosis of mixed anxiety-depression was proposed.
Abstract: We review psychometric and other evidence relevant to mixed anxiety-depression. Properties of anxiety and depression measures, including the convergent and discriminant validity of self- and clinical ratings, and interrater reliability, are examined in patient and normal samples. Results suggest that anxiety and depression can be reliably and validly assessed; moreover, although these disorders share a substantial component of general affective distress, they can be differentiated on the basis of factors specific to each syndrome. We also review evidence for these specific factors, examining the influence of context and scale content on ratings, factor analytic studies, and the role of low positive affect in depression. With these data, we argue for a tripartite structure consisting of general distress, physiological hyperarousal (specific anxiety), and anhedonia (specific depression), and we propose a diagnosis of mixed anxiety-depression.
3,465 citations
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: In this article, the authors introduce the current state-of-the-art of network estimation and propose two novel statistical methods: the correlation stability coefficient and the bootstrapped difference test for edge-weights and centrality indices.
Abstract: 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.
1,584 citations
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.
1,311 citations
"The replicability and generalizabil..." refers background or result in this paper
...…the overall network structure of Community Network 2 significantly differed from that of the two undergraduate networks and the 9 Interestingly, this finding is inconsistent with the network theory, which suggests that networks should be more strongly connected in clinical samples (Borsboom, 2017)....
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...The network theory of psychopathology conceptually contrasts with this perspective, as it proposes that symptoms causally interact in dynamic networks (Borsboom, 2017)....
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...Modeling symptoms in a network as latent variables offers one potential solution to this problem (Epskamp, Rhemtulla, & Borsboom, 2017)....
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...9 Interestingly, this finding is inconsistent with the network theory, which suggests that networks should be more strongly connected in clinical samples (Borsboom, 2017)....
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TL;DR: A method to visualize comorbidity networks is proposed and it is argued that this approach generates realistic hypotheses about pathways to comor bidity, overlapping symptoms, and diagnostic boundaries, that are not naturally accommodated by latent variable models.
Abstract: The pivotal problem of comorbidity research lies in the psychometric foundation it rests on, that is, latent variable theory, in which a mental disorder is viewed as a latent variable that causes a constellation of symptoms. From this perspective, comorbidity is a (bi)directional relationship between multiple latent variables. We argue that such a latent variable perspective encounters serious problems in the study of comorbidity, and offer a radically different conceptualization in terms of a network approach, where comorbidity is hypothesized to arise from direct relations between symptoms of multiple disorders. We propose a method to visualize comorbidity networks and, based on an empirical network for major depression and generalized anxiety, we argue that this approach generates realistic hypotheses about pathways to comorbidity, overlapping symptoms, and diagnostic boundaries, that are not naturally accommodated by latent variable models: Some pathways to comorbidity through the symptom space are more likely than others; those pathways generally have the same direction (i.e., from symptoms of one disorder to symptoms of the other); overlapping symptoms play an important role in comorbidity; and boundaries between diagnostic categories are necessarily fuzzy.
918 citations
"The replicability and generalizabil..." refers background in this paper
...Specific characteristics such as individual edges have also been examined extensively in the psychopathology network literature (e.g., to understand the role of bridging edges in comorbidity; Cramer et al., 2010), and individual edges replicated and generalized approximately 80% of the time on average in the present study....
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...…such as individual edges have also been examined extensively in the psychopathology network literature (e.g., to understand the role of bridging edges in comorbidity; Cramer et al., 2010), and individual edges replicated and generalized approximately 80% of the time on average in the present study....
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