Format

Send to

Choose Destination
PLoS One. 2013 Apr 4;8(4):e60188. doi: 10.1371/journal.pone.0060188. Print 2013.

A network approach to psychopathology: new insights into clinical longitudinal data.

Author information

1
Department of Psychology, University of Leuven, Leuven, Belgium. laura.bringmann@ppw.kuleuven.be

Erratum in

  • PLoS One. 2014;9(4):e96588.

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 within-subject 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.

PMID:
23593171
PMCID:
PMC3617177
DOI:
10.1371/journal.pone.0060188
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center