Personalized network modeling in psychopathology: The importance of contemporaneous and temporal connections

Sacha Epskamp, Claudia Van Borkulo, Date van der Veen, Michelle Servaas, Adela Isvoranu, Harriette Riese, A.O.J. Cramer

Research output: Contribution to journalArticleScientificpeer-review

84 Citations (Scopus)

Abstract

Recent literature has introduced (1) the network perspective to psychology, and (2) collection of time-series data in order to capture symptom fluctuations and other time varying factors in daily life. Combining these trends allows for the estimation of intra-individual 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
JournalClinical Psychological Science
Volume6
Issue number3
DOIs
Publication statusPublished - 2018

Keywords

  • CENTRALITY
  • DAILY-LIFE
  • DEPRESSION
  • GRAPHICAL MODELS
  • MENTAL-DISORDERS
  • MOMENTARY ASSESSMENT
  • MOOD
  • PERSPECTIVE
  • SYMPTOMS
  • TIME-SERIES
  • causality
  • depression
  • longitudinal methods
  • network analysis
  • psychotherapy

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