Intervening on psychopathology networks: Evaluating intervention targets through simulations.

Autor: Lunansky G; Department of Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands. Electronic address: g.lunansky@uva.nl., Naberman J; Department of Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands., van Borkulo CD; Department of Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands; Centre for Urban Mental Health, University of Amsterdam, The Netherlands., Chen C; Laboratory for Traumatic Stress Studies, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China., Wang L; Laboratory for Traumatic Stress Studies, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China., Borsboom D; Department of Psychological Methods, University of Amsterdam, Amsterdam, the Netherlands.
Jazyk: angličtina
Zdroj: Methods (San Diego, Calif.) [Methods] 2022 Aug; Vol. 204, pp. 29-37. Date of Electronic Publication: 2021 Nov 16.
DOI: 10.1016/j.ymeth.2021.11.006
Abstrakt: Identifying the different influences of symptoms in dynamic psychopathology models may hold promise for increasing treatment efficacy in clinical applications. Dynamic psychopathology models study the behavioral patterns of symptom networks, where symptoms mutually enforce each other. Interventions could be tailored to specific symptoms that are most effective at lowering symptom activity or that hinder the further development of psychopathology. Simulating interventions in psychopathology network models fits in a novel tradition where symptom-specific perturbations are used as in silico interventions. Here, we present the NodeIdentifyR algorithm (NIRA) to identify the projected most efficient, symptom-specific intervention target in a network model (i.e., the Ising model). We implemented NIRA in a freely available R package. The technique studies the projected effects of symptom-specific interventions by simulating data while symptom parameters (i.e., thresholds) are systematically altered. The projected effect of these interventions is defined in terms of the expected change in overall symptom activity across simulations. With this algorithm, it is possible to study (1) whether symptoms differ in their projected influence on the behavior of the symptom network and, if so, (2) which symptom has the largest projected effect in lowering or increasing overall symptom activation. As an illustration, we apply the algorithm to an empirical dataset containing Post-Traumatic Stress Disorder symptom assessments of participants who experienced the Wenchuan earthquake in 2008. The most important limitations of the method are discussed, as well as recommendations for future research, such as shifting towards modeling individual processes to validate these types of simulation-based intervention methods.
(Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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