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Personalized network modeling in psychopathology: The importance of contemporaneous and temporal connections
Epskamp,Sacha Van Borkulo,Claudia van der Veen,Date Servaas,Michelle Isvoranu,Adela Riese,Harriette Cramer,A.O.J.
Epskamp,Sacha
Van Borkulo,Claudia
van der Veen,Date
Servaas,Michelle
Isvoranu,Adela
Riese,Harriette
Cramer,A.O.J.
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.
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2018
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Keywords
CENTRALITY, DAILY-LIFE, DEPRESSION, GRAPHICAL MODELS, MENTAL-DISORDERS, MOMENTARY ASSESSMENT, MOOD, PERSPECTIVE, SYMPTOMS, TIME-SERIES, causality, depression, longitudinal methods, network analysis, psychotherapy
Citation
Epskamp, S, Van Borkulo, C, van der Veen, D, Servaas, M, Isvoranu, A, Riese, H & Cramer, A O J 2018, 'Personalized network modeling in psychopathology : The importance of contemporaneous and temporal connections', Clinical Psychological Science, vol. 6, no. 3, pp. 416–427. https://doi.org/10.1177/2167702617744325