A Network Approach to Psychopathology: New Insights into Clinical Longitudinal Data

Laura F. Bringmann*, Nathalie Vissers, Marieke Wichers, Nicole Geschwind, Peter Kuppens, Frenk Peeters, Denny Borsboom, Francis Tuerlinckx

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

243 Citations (Scopus)
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Abstract

In the network approach to psychopathology, disorders are conceptualized as networks of mutually interacting symptoms (e.g., depressed mood) and transdiagnostic factors (e.g., rumination). This suggests that it is necessary to study how symptoms dynamically interact over time in a network architecture. In the present paper, we show how such an architecture can be constructed on the basis of time-series data obtained through Experience Sampling Methodology (ESM). The proposed methodology determines the parameters for the interaction between nodes in the network by estimating a multilevel vector autoregression (VAR) model on the data. The methodology allows combining between-subject and withinsubject information in a multilevel framework. The resulting network architecture can subsequently be analyzed through network analysis techniques. In the present study, we apply the method to a set of items that assess mood-related factors. We show that the analysis generates a plausible and replicable network architecture, the structure of which is related to variables such as neuroticism; that is, for subjects who score high on neuroticism, worrying plays a more central role in the network. Implications and extensions of the methodology are discussed.
Original languageEnglish
Article number e60188
Number of pages13
JournalPLoS ONE
Volume8
Issue number4
DOIs
Publication statusPublished - 4-Apr-2013
Externally publishedYes

Keywords

  • LINEAR MIXED MODELS
  • TIME-SERIES
  • POSITIVE EMOTIONS
  • NEGATIVE AFFECT
  • WORRY
  • EXPERIENCE
  • RUMINATION
  • LIFE
  • ACTIVATION
  • DYNAMICS

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