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  5. Symptom Network Models of Psychosis

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Preprint
English
2017

Symptom Network Models of Psychosis

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0 Files

English
2017
DOI: 10.31234/osf.io/nk8yv

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Denny Borsboom
Denny Borsboom

University Of Amsterdam

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Adela‐Maria Isvoranu
Lindy‐Lou Boyette
Sinan Gülöksüz
+1 more

Abstract

Disorders within the psychosis spectrum are highly heterogeneous and multifactorial [1,2]. Despite intensive research over the past century, the causes and pathogenesis of psychosis are still unclear, and no genetic marker has been consistently linked to developing a psychotic disorder [3–5]. In recent years, in an attempt to overcome this conundrum, the conceptualization of mental disorders as networks of interacting symptoms has gained considerable ground [4,6]. This conceptualization aligns well with practitioners’ viewpoints as it focuses on concrete symptoms and their interrelations, rather than on abstract latent disorders or syndromes [7]. Even though direct influences of one symptom on another are routinely observed in clinical practice (e.g., if a patient shows social withdrawal, this may soon lead to the patient displaying paranoid ideation and vice versa), in classical (psychometric) approaches to psychosis, which underlie most common psychometric practices in research, such direct influences are not modeled. Instead, symptoms are treated as passive psychometric indicators of a (set of) latent variable(s) – thus, it is assumed that symptoms are simply a result of the underlying disorder, rather than influencing each other [4,6,8]. As a result, correlations between symptoms are in a nontrivial sense spurious: symptoms cluster together because of their common dependence on the disorder. The assumption that correlations between symptoms arise from a common cause, which has been deemed problematic by both psychometricians and clinicians [4,7], has spurred the development of alternative psychometric approaches to mental disorders, in which symptoms are viewed instead as networks of mutually interacting components. Collectively, these lines of research have become known as the network approach to mental disorders. The centerpiece of the network approach is the idea that symptoms are active causal agents in producing disorder states, and that the study of their causal interaction is central to progress in understanding and treating mental disorders. This chapter aims to introduce the network approach to mental disorders in the context of psychotic symptomatology. We first discuss standard approaches to psychotic disorders, highlighting unresolved issues, and we then provide an introduction to network models of psychosis, with a focus on the general theoretical framework. We concentrate on how (environmental and genetic) risk factors can be understood from a network perspective and how they can be included in network models. We complete the chapter with a discussion on how network models can be integrated into treatment approaches.

How to cite this publication

Adela‐Maria Isvoranu, Lindy‐Lou Boyette, Sinan Gülöksüz, Denny Borsboom (2017). Symptom Network Models of Psychosis. , DOI: 10.31234/osf.io/nk8yv.

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Publication Details

Type

Preprint

Year

2017

Authors

4

Datasets

0

Total Files

0

Language

English

DOI

10.31234/osf.io/nk8yv

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