Temporal dynamics of depressive symptomatology: An idiographic time series analysis applying network models to patients with depressive disorders

Abstract

Background: As phenotypes of depressive disorders (DD) are highly heterogenous, a growing number of studies investigate person-specific associations of depressive symptoms in time series data. Most available methods for estimating applicable models rely on the assumption that the associations between variables stay constant over time, which can be unrealistic in clinical contexts. To circumvent this limitation, we used a recently developed technique to estimate time-varying vector autoregressive models. Methods: In daily diary data of 20 participants with DD with a mean length of 274 days (SD = 82.4, range = 154-539), we modeled idiographic associations between core depressive symptoms, rumination, sleep, and quantity and quality of social contacts as idiographic time-varying dynamical networks. Results: Resulting models showed marked inter- as well as intraindividual differences. For some participants, associations between variables changed fast over time, whereas for others they showed more stability. Our results further indicated nonstationarity in all time series. Discussion: Idiographic symptom networks of depression can be of interest to clinicians and researchers as they can capture changes over time and provide detailed insights into the temporal course of mental disorders. Whilst the assumption of stationarity can hinder insights into important change processes, time-varying network models are a promising approach. We discuss limitations, their possible solutions, and recommendations for further use of the modeling technique.

Depressive symptoms as a heterogeneous and constantly evolving dynamical system: Idiographic depressive symptom networks of rapid symptom changes among persons with major depressive disorder.

“…In the current study, we conducted a novel investigation of the dynamics of depressive symptoms within 105 participants over the course of 90 days using EMA data and a time-varying VAR approach and used five individuals as exemplars to illustrate this approach. In line with prior research, our results indicate that there is high heterogeneity across persons, such that the individual network composition is unique from person to person (de Vos et al, 2017; Kaiser & Laireiter, 2018; Siepe et al, 2022). Moreover, our results show that for most persons, individual depressive symptom networks can change dramatically in form across a 3-month period, as evidenced by some participants exhibiting significant variability within their symptom networks.…”

“…Their findings indicate that MDD symptoms can substantially vary both across and within persons over time, providing further evidence highlighting the importance of investigating MDD as a nonstationary, dynamic system. While Siepe et al (2022) uncovered important information about MDD symptom dynamics, they only assessed two depressive symptoms per day for 20 individuals. In addition, while they were able to examine variability across days, it is also important to examine variability within days given that symptoms can change over the course of hours (Ebrahimi et al, 2021;Fried et al, 2022;Lorenz et al, 2020;Wichers et al, 2016Wichers et al, , 2020.…”

“…There is one existing study that has examined the dynamic changes of MDD with a time-varying VAR approach (Siepe et al, 2022). Their findings indicate that MDD symptoms can substantially vary both across and within persons over time, providing further evidence highlighting the importance of investigating MDD as a nonstationary, dynamic system.…”

“…In addition, using time-varying VAR modeling to examine the dynamics of all MDD symptoms may provide added insight into how symptoms influence each other over time. Siepe et al’s (2022) analyses also utilized block bootstrapping methods to assess the stability of their estimates, which suggested that the parameter estimates were unstable; however, this method may not be suitable to use with time-varying coefficient models.…”

A systematic review of the application of experience sampling methodology to measure anhedonia in psychiatry and mental health

“…Network models showed contemporaneous and temporal within-person relationships between anhedonia, anxiety, depression, and borderline personality symptoms at the group-level (e.g., Ebrahimi et al, 2024;Ebrahimi et al, 2021;Fatimah et al, 2023;Fisher et al, 2017;Starr & Davila, 2012) and idiographic-level (Fried et al, 2021;Nemesure et al, 2024;Siepe et al, 2022). There was some evidence that anhedonia is an influential symptom and is involved in vicious cycles at the group-level (Fried et al, 2021;Kraft et al, 2022;.…”