The role of stabilizing and communicating symptoms given overlapping communities in psychopathology networks

Tessa Blanken, Marie Deserno, Jonas Dalege, Denny Borsboom, Peter Blanken, Gerard Kerkhof, A.O.J. Cramer

Research output: Contribution to journalArticleScientificpeer-review

54 Citations (Scopus)
89 Downloads (Pure)

Abstract

Network theory, as a theoretical and methodological framework, is energizing many research fields, among which clinical psychology and psychiatry. Fundamental to the network theory of psychopathology is the role of specific symptoms and their interactions. Current statistical tools, however, fail to fully capture this constitutional property. We propose community detection tools as a means to evaluate the complex network structure of psychopathology, free from its original boundaries of distinct disorders. Unique to this approach is that symptoms can belong to multiple communities. Using a large community sample and spanning a broad range of symptoms (Symptom Checklist-90-Revised), we identified 18 communities of interconnected symptoms. The differential role of symptoms within and between communities offers a framework to study the clinical concepts of comorbidity, heterogeneity and hallmark symptoms. Symptoms with many and strong connections within a community, defined as stabilizing symptoms, could be thought of as the core of a community, whereas symptoms that belong to multiple communities, defined as communicating symptoms, facilitate the communication between problem areas. We propose that defining symptoms on their stabilizing and/or communicating role within and across communities accelerates our understanding of these clinical phenomena, central to research and treatment of psychopathology.
Original languageEnglish
Article number5854
JournalScientific Reports
DOIs
Publication statusPublished - 2018

Fingerprint

Dive into the research topics of 'The role of stabilizing and communicating symptoms given overlapping communities in psychopathology networks'. Together they form a unique fingerprint.

Cite this