Background Previous studies have used network models to investigate how PTSD symptoms associate with each other. However, analyses examining the degree to which these networks are stable over time... Show moreBackground Previous studies have used network models to investigate how PTSD symptoms associate with each other. However, analyses examining the degree to which these networks are stable over time, which are critical to identifying symptoms that may contribute to the chronicity of this disorder, are scarce. In the current study, we evaluated the temporal stability of DSM-5 PTSD symptom networks over a three-year period in a nationally representative sample of trauma-exposed U.S. military veterans. Methods Data were analyzed from 611 trauma-exposed U.S. military veterans who participated in the National Health and Resilience in Veterans Study (NHRVS). We estimated regularized partial correlation networks of DSM-5 PTSD symptoms at baseline (Time 1) and at three-year follow-up (Time 2), and examined their temporal stability. Results Evaluation of the network structure of PTSD symptoms at Time 1 and Time 2 using a formal network comparison indicated that the Time 1 network did not differ significantly from the Time 2 network with regard to network structure (p = 0.12) or global strength (sum of all absolute associations, i.e. connectivity; p = 0.25). Centrality estimates of both networks (r = 0.86) and adjacency matrices (r = 0.69) were highly correlated. In both networks, avoidance, intrusive, and negative cognition and mood symptoms were among the more central nodes. Limitations This study is limited by the use of a self-report instrument to assess PTSD symptoms and recruitment of a relatively homogeneous sample of predominantly older, Caucasian veterans. Conclusion Results of this study demonstrate the three-year stability of DSM-5 PTSD symptom network structure in a nationally representative sample of trauma-exposed U.S. military veterans. They further suggest that trauma-related avoidance, intrusive, and dysphoric symptoms may contribute to the chronicity of PTSD symptoms in this population. Show less
Recent years have seen increasing attention on posttraumatic stress disorder (PTSD) research. While research has largely focused on the dichotomy between patients diagnosed with mental disorders... Show moreRecent years have seen increasing attention on posttraumatic stress disorder (PTSD) research. While research has largely focused on the dichotomy between patients diagnosed with mental disorders and healthy controls — in other words, investigations at the level of diagnoses — recent work has focused on psychopathology symptoms. Symptomics research in the area of PTSD has been scarce so far, although several studies have focused on investigating the network structures of PTSD symptoms. The present special issue of EJPT adds to the literature by curating additional PTSD network studies, each looking at a different aspect of PTSD. We hope that this special issue encourages researchers to conceptualize and model PTSD data from a network perspective, which arguably has the potential to inform and improve the efficacy of therapeutic interventions. Show less
Introduction We compared DSM-IV criteria for major depression (MD) with clinically selected non-DSM criteria in their ability to represent clinical features of depression. Method We conducted... Show moreIntroduction We compared DSM-IV criteria for major depression (MD) with clinically selected non-DSM criteria in their ability to represent clinical features of depression. Method We conducted network analyses of 19 DSM and non-DSM symptoms of MD assessed at personal interview in 5952 Han Chinese women meeting DSM-IV criteria for recurrent MD. We estimated an Ising model (the state-of-the-art network model for binary data), compared the centrality (interconnectedness) of DSM-IV and non-DSM symptoms, and investigated the community structure (symptoms strongly clustered together). Results The DSM and non-DSM criteria were intermingled within the same symptom network. In both the DSM-IV and non-DSM criteria sets, some symptoms were central (highly interconnected) while others were more peripheral. The mean centrality of the DSM and non-DSM criteria sets did not significantly differ. In at least two cases, non-DSM criteria were more central than symptomatically related DSM criteria: lowered libido vs. sleep and appetite changes, and hopelessness versus worthlessness. The overall network had three sub-clusters reflecting neurovegetative/mood symptoms, cognitive changes and anxiety/irritability. Limitations The sample were severely ill Han Chinese females limiting generalizability. Conclusions Consistent with prior historical reviews, our results suggest that the DSM-IV criteria for MD reflect one possible sub-set of a larger pool of plausible depressive symptoms and signs. While the DSM criteria on average perform well, they are not unique and may not be optimal in their ability to describe the depressive syndrome Show less
Background Genetic risk and environmental adversity—both important risk factors for major depression (MD)—are thought to differentially impact on depressive symptom types and associations. Does... Show moreBackground Genetic risk and environmental adversity—both important risk factors for major depression (MD)—are thought to differentially impact on depressive symptom types and associations. Does heterogeneity in these risk factors result in different depressive symptom networks in patients with MD? Methods A clinical sample of 5784 Han Chinese women with recurrent MD were interviewed about their depressive symptoms during their lifetime worst episode of MD. The cases were classified into subgroups based on their genetic risk for MD (family history, polygenic risk score, early age at onset) and severe adversity (childhood sexual abuse, stressful life events). Differences in MD symptom network structure were statistically examined for these subgroups using permutation-based network comparison tests. Results Although significant differences in symptom endorsement rates were seen in 18.8% of group comparisons, associations between depressive symptoms were similar across the different subgroups of genetic and environmental risk. Network comparison tests showed no significant differences in network strength, structure, or specific edges (P-value > 0.05) and correlations between edges were strong (0.60–0.71). Limitations This study analyzed depressive symptoms retrospectively reported by severely depressed women using novel statistical methods. Future studies are warranted to investigate whether similar findings hold in prospective longitudinal data, less severely depressed patients, and men. Conclusions Similar depressive symptom networks for MD patients with a higher or lower genetic or environmental risk suggest that differences in these etiological influences may produce similar symptom networks downstream for severely depressed women. Show less
Murphy, J.; McBride, O.; Fried, E.I.; Shevlin, M. 2017
It has been proposed that subclinical psychotic experiences (PEs) may causally impact on each other over time and engage with one another in patterns of mutual reinforcement and feedback. This... Show moreIt has been proposed that subclinical psychotic experiences (PEs) may causally impact on each other over time and engage with one another in patterns of mutual reinforcement and feedback. This subclinical network of experiences in turn may facilitate the onset of psychotic disorder. PEs, however, are not inherently distressing, nor do they inevitably lead to impairment. The question arises therefore, whether nondistressing PEs, distressing PEs, or both, meaningfully inform an extended psychosis phenotype. The current study first aimed to exploit valuable ordinal data that captured the absence, occurrence and associated impairment of PEs in the general population to construct a general population based severity network of PEs. The study then aimed to partition the available ordinal data into 2 sets of binary data to test whether an occurrence network comprised of PE data denoting absence (coded 0) and occurrence/impairment (coded 1) was comparable to an impairment network comprised of binary PE data denoting absence/occurrence (coded 0) and impairment (coded 1). Networks were constructed using state-of-the-art regularized pairwise Markov Random Fields (PMRF). The severity network revealed strong interconnectivity between PEs and nodes denoting paranoia were among the most central in the network. The binary PMRF impairment network structure was similar to the occurrence network, however, the impairment network was characterized by significantly stronger PE interconnectivity. The findings may help researchers and clinicians to consider and determine how, when, and why an individual might transition from experiences that are nondistressing to experiences that are more commonly characteristic of psychosis symptomology in clinical settings. Show less
Background Network analyses on psychopathological data focus on the network structure and its derivatives such as node centrality. One conclusion one can draw from centrality measures is that the... Show moreBackground Network analyses on psychopathological data focus on the network structure and its derivatives such as node centrality. One conclusion one can draw from centrality measures is that the node with the highest centrality is likely to be the node that is determined most by its neighboring nodes. However, centrality is a relative measure: knowing that a node is highly central gives no information about the extent to which it is determined by its neighbors. Here we provide an absolute measure of determination (or controllability) of a node – its predictability. We introduce predictability, estimate the predictability of all nodes in 18 prior empirical network papers on psychopathology, and statistically relate it to centrality.Methods We carried out a literature review and collected 25 datasets from 18 published papers in the field (several mood and anxiety disorders, substance abuse, psychosis, autism, and transdiagnostic data). We fit state-of-the-art network models to all datasets, and computed the predictability of all nodes.Results Predictability was unrelated to sample size, moderately high in most symptom networks, and differed considerable both within and between datasets. Predictability was higher in community than clinical samples, highest for mood and anxiety disorders, and lowest for psychosis.Conclusions Predictability is an important additional characterization of symptom networks because it gives an absolute measure of the controllability of each node. It allows conclusions about how self-determined a symptom network is, and may help to inform intervention strategies. Limitations of predictability along with future directions are discussed. Show less
