Don’t blame the model: Reconsidering the network approach to psychopathology.

Abstract

Citation for published version (APA): Bringmann, L. F., & Eronen, M. I. (2018). Don't blame the model: Reconsidering the network approach to psychopathology. Psychological Review, 125(4), 606-615. https://doi.org/10.1037/rev0000108 Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. The network approach to psychopathology is becoming increasingly popular. The motivation for this approach is to provide a replacement for the problematic common cause perspective and the associated latent variable model, where symptoms are taken to be mere effects of a common cause (the disorder itself). The idea is that the latent variable model is plausible for medical diseases, but unrealistic for mental disorders, which should rather be conceptualized as networks of directly interacting symptoms. We argue that this rationale for the network approach is misguided. Latent variable (or common cause) models are not inherently problematic, and there is not even a clear boundary where network models end and latent variable (or common cause) models begin. We also argue that focusing on this contrast has led to an unrealistic view of testing and finding support for the network approach, as well as an oversimplified picture of the relationship between medical diseases and mental disorders.As an alternative, we point out more essential contrasts, such as the contrast between dynamic and static modeling approaches, that can provide a better framework for conceptualizing mental disorders.Finally, we discuss several topics and open problems that need to be addressed in order to make the network approach more concrete and to move the field of psychological network research forward.

Self-Dislike and Sadness are Central Symptoms of Depression in College Students: A Network Analysis

“…symptoms) are presumed to cause any latent factor rather than the other way around (Fried et al, 2016), would allow us to weight the contribution of symptoms to the sum score based on their network centrality. This type of formative sum score has already been utilized (Boschloo et al, 2016) and may allow us to better represent the phenomenology of depression than using either method in isolation (Bringmann & Eronen, 2018).…”

The Study of Psychopathology from the Network Analysis Perspective: A Systematic Review

“…In fact, there is a debate, among experts in the field, on the extent to which NA should be used as an additional analytic tool to explore constellations of elements (signs, symptoms, stressors, etc.) rather than using it as a tool to criticize diagnostic systems [57, 101]. In this vein, as the Clinimetrics approach has repeatedly emphasized, the analysis of psychopathology exceeds the framework of self-reported symptoms as it should also include other important parameters, for instance, functional capacity, rate of progression of the disorder (staging), responses to other treatments, or even biomarkers [15, 102] and all this information should ultimately incorporate clinicians’ judgments to estimate the clinical utility of it [5].…”

Bridging the gap: A network approach to Dark Triad, Mental Toughness, the Big Five, and perceived stress

“…Some studies have shown that central elements of psychopathology networks have high clinical relevance (Boschloo, van Borkulo, Borsboom, & Schoevers, ; Christensen, Kenett, Aste, Silvia, & Kwapil, ; Rodebaugh et al, ). However, centrality indices have been originally developed for social networks and might not be adequate to capture the type of relationships encoded by psychological networks (Bringmann & Eronen, ). Therefore, in addition to centrality, we estimated also the predictability of each node, an index that has been developed specifically for psychological networks (Haslbeck & Waldorp, ).…”