Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections

Sacha Epskamp, Claudia van Borkulo, Date van de Veen, Michelle Servaas, Adela-Maria Isvoranu, Harriette Riese, Angelique O. J. Cramer

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Recent literature has introduced (a) the network perspective to psychology and (b) collection of time series data to capture symptom fluctuations and other time varying factors in daily life. Combining these trends allows for the estimation of intraindividual network structures. We argue that these networks can be directly applied in clinical research and practice as hypothesis generating structures. Two networks can be computed: a temporal network, in which one investigates if symptoms (or other relevant variables) predict one another over time, and a contemporaneous network, in which one investigates if symptoms predict one another in the same window of measurement. The contemporaneous network is a partial correlation network, which is emerging in the analysis of cross-sectional data but is not yet utilized in the analysis of time series data. We explain the importance of partial correlation networks and exemplify the network structures on time series data of a psychiatric patient.

Original languageEnglish
Pages (from-to)416-427
Number of pages12
JournalClinical Psychological Science
Volume6
Issue number3
DOIs
Publication statusPublished - 2018

Keywords

  • CAUSALITY
  • DEPRESSION
  • PSYCHOTHERAPY
  • longitudinal methods
  • NETWORK ANALYSIS
  • SYMPTOMS
  • MOOD
  • TIME-SERIES
  • MOMENTARY ASSESSMENT
  • MENTAL-DISORDERS
  • GRAPHICAL MODELS
  • DAILY-LIFE
  • PERSPECTIVE
  • CENTRALITY

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