Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections (2024)

Epskamp, S., van Borkulo, C., van de Veen, D., Servaas, M., Isvoranu, A.-M., Riese, H., & Cramer, A. O. J. (2018). Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections. Clinical Psychological Science, 6(3), 416-427. https://doi.org/10.1177/2167702617744325

Epskamp, Sacha ; van Borkulo, Claudia ; van de Veen, Date et al. / Personalized Network Modeling in Psychopathology : The Importance of Contemporaneous and Temporal Connections. In: Clinical Psychological Science. 2018 ; Vol. 6, No. 3. pp. 416-427.

@article{67138e4d3844434eb5934540e1d41202,

title = "Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections",

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.",

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

author = "Sacha Epskamp and {van Borkulo}, Claudia and {van de Veen}, Date and Michelle Servaas and Adela-Maria Isvoranu and Harriette Riese and Cramer, {Angelique O. J.}",

year = "2018",

doi = "10.1177/2167702617744325",

language = "English",

volume = "6",

pages = "416--427",

journal = "Clinical Psychological Science",

issn = "2167-7026",

publisher = "SAGE Publications Inc.",

number = "3",

}

Epskamp, S, van Borkulo, C, van de Veen, D, Servaas, M, Isvoranu, A-M, Riese, H & Cramer, AOJ 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

Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections. / Epskamp, Sacha; van Borkulo, Claudia; van de Veen, Date et al.
In: Clinical Psychological Science, Vol. 6, No. 3, 2018, p. 416-427.

Research output: Contribution to journal › Article › Academic › peer-review

TY - JOUR

T1 - Personalized Network Modeling in Psychopathology

T2 - The Importance of Contemporaneous and Temporal Connections

AU - Epskamp, Sacha

AU - van Borkulo, Claudia

AU - van de Veen, Date

AU - Servaas, Michelle

AU - Isvoranu, Adela-Maria

AU - Riese, Harriette

AU - Cramer, Angelique O. J.

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - CAUSALITY

KW - DEPRESSION

KW - PSYCHOTHERAPY

KW - longitudinal methods

KW - NETWORK ANALYSIS

KW - SYMPTOMS

KW - MOOD

KW - TIME-SERIES

KW - MOMENTARY ASSESSMENT

KW - MENTAL-DISORDERS

KW - GRAPHICAL MODELS

KW - DAILY-LIFE

KW - PERSPECTIVE

KW - CENTRALITY

U2 - 10.1177/2167702617744325

DO - 10.1177/2167702617744325

M3 - Article

C2 - 29805918

SN - 2167-7026

VL - 6

SP - 416

EP - 427

JO - Clinical Psychological Science

JF - Clinical Psychological Science

IS - 3

ER -

Epskamp S, van Borkulo C, van de Veen D, Servaas M, Isvoranu AM, Riese H et al. Personalized Network Modeling in Psychopathology: The Importance of Contemporaneous and Temporal Connections. Clinical Psychological Science. 2018;6(3):416-427. doi: 10.1177/2167702617744325