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      How Predictable are Symptoms in Psychopathological Networks? A Reanalysis of 17 Published Datasets

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          Abstract

          Background Network analysis in psychopathology is an emerging field. Previous publications limited their interpretation largely on the centrality (inter-connectedness) of nodes in network structures, where nodes with the highest centrality may be the best targets for interventions. However, centrality is a relative metric: the most central node could be indeed the best target for an intervention but still be a very bad target. This is the case if it shares very little variance with other nodes, and thus intervening on the node will only have a negligible impact on the network structure. Here we provide a solution for this problem by introducing the absolute metric predictability - the extent to which a node is determined by all its neighbors in the network - and estimate the predictability of all nodes in 17 prior empirical network papers on psychopathology. Methods We carried out a literature review and collected 24 datasets from 17 published papers in the field (several mood and disorders, substance abuse, and psychosis). We fit state-of-the-art regularized partial correlation networks to all datasets, and computed the predictability of all nodes. Results Predictability is moderately high in most symptom networks, and differs considerable both within and between datasets. Conclusions Predictability is an important characterization of symptom networks in addition to their structure. It provides a measure of practical relevance of connections among nodes that may be helpful to inform intervention strategies, and allows conclusions about how self-determined a symptom network is. Limitations of predictability along with future directions are discussed.

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          Most cited references 12

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          Mental Disorders as Causal Systems: A Network Approach to Posttraumatic Stress Disorder

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            Estimators of Relative Importance in Linear Regression Based on Variance Decomposition

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              From loss to loneliness: The relationship between bereavement and depressive symptoms.

              Spousal bereavement can cause a rise in depressive symptoms. This study empirically evaluates 2 competing explanations concerning how this causal effect is brought about: (a) a traditional latent variable explanation, in which loss triggers depression which then leads to symptoms; and (b) a novel network explanation, in which bereavement directly affects particular depression symptoms which then activate other symptoms. We used data from the Changing Lives of Older Couples (CLOC) study and compared depressive symptomatology, assessed via the 11-item Center for Epidemiologic Studies Depression Scale (CES-D), among those who lost their partner (N = 241) with a still-married control group (N = 274). We modeled the effect of partner loss on depressive symptoms either as an indirect effect through a latent variable, or as a direct effect in a network constructed through a causal search algorithm. Compared to the control group, widow(er)s' scores were significantly higher for symptoms of loneliness, sadness, depressed mood, and appetite loss, and significantly lower for happiness and enjoyed life. The effect of partner loss on these symptoms was not mediated by a latent variable. The network model indicated that bereavement mainly affected loneliness, which in turn activated other depressive symptoms. The direct effects of spousal loss on particular symptoms are inconsistent with the predictions of latent variable models, but can be explained from a network perspective. The findings support a growing body of literature showing that specific adverse life events differentially affect depressive symptomatology, and suggest that future studies should examine interventions that directly target such symptoms.
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                Author and article information

                Journal
                2016-11-28
                Article
                1612.06357

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                24 pages, 1 table, 4 figures
                q-bio.NC

                Neurosciences

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