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How predictable are symptoms in psychopathological networks? A reanalysis of 18 published datasets

  • J. M. B. Haslbeck (a1) and E. I. Fried (a1)
Abstract
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 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.

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Copyright
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Corresponding author
*Address for correspondence: J. M. B. Haslbeck, University of Amsterdam, Psychological Methods, Nieuwe Achtergracht 129-B, 1018 WT, Amsterdam, The Netherlands. (Email: jonashaslbeck@gmail.com, http://jmbh.github.io )
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  • ISSN: 0033-2917
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