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.

Dynamic Network Analysis of Negative Emotions and DSM‐5 Posttraumatic Stress Disorder Symptom Clusters During Conflict

“…The contemporaneous network is a Gaussian graphical model (GGM) based on the covariance of the residuals from the VAR model, which estimates the within-time window associations of variables, somewhat akin to a multilevel partial correlation network, after controlling for temporal effects (Epskamp et al, 2017). In the current study, this showed the within-measurement associations between negative emotions and PTSD clusters.…”

“…This model produces three networks: temporal, contemporaneous, and between-persons. In the current study, the temporal network is a directed network of regression coefficients depicting the lagged associations between PTSD clusters and negative emotions from one time point to the next time point (Epskamp et al, 2017. This shows whether a deviation from a person's mean in one variable predicts a deviation from a person's mean in another variable at the next measurement occasion, for which Grangercausal connections (Granger, 1969) between symptoms can be inferred (Bringmann et al, 2013;Schuurman, Ferrer, de Boer-Sonnenschein, & Hamaker, 2016).…”

Fluctuations in Affective States and Self-Efficacy to Resist Non-Suicidal Self-Injury as Real-Time Predictors of Non-Suicidal Self-Injurious Thoughts and Behaviors

“…Based on existing knowledge (40,41,48), we hypothesized that higher-than-usual negative affect, and lower-than-usual positive affect, would each be contemporaneously and temporally associated with NSSI thoughts. However, as we expected that momentary fluctuations in affective states would trigger NSSI thoughts more strongly within minutes than hours, stronger effects were anticipated in contemporaneous than temporal models (54).…”

Towards a precision psychiatry approach to anxiety disorders with ecological momentary assessment: the example of panic disorder

“…To illustrate this approach, we used a vector autoregressive modelling approach to examine the contemporaneous network of associations among symptoms for Alice and Bob at baseline and at post-treatment. 32 As seen in figure 2, the structure of relationships among symptoms for Alice and Bob exhibit both similarities and differences. In both patients, physical sensations, panic-related cognitions and feelings of anxiety or panic are strongly related to one another.…”