An investigation of emotion dynamics in major depressive disorder patients and healthy persons using sparse longitudinal networks

Abstract

BackgroundDifferences in within-person emotion dynamics may be an important source of heterogeneity in depression. To investigate these dynamics, researchers have previously combined multilevel regression analyses with network representations. However, sparse network methods, specifically developed for longitudinal network analyses, have not been applied. Therefore, this study used this approach to investigate population-level and individual-level emotion dynamics in healthy and depressed persons and compared this method with the multilevel approach.MethodsTime-series data were collected in pair-matched healthy persons and major depressive disorder (MDD) patients (n = 54). Seven positive affect (PA) and seven negative affect (NA) items were administered electronically at 90 times (30 days; thrice per day). The population-level (healthy vs. MDD) and individual-level time series were analyzed using a sparse longitudinal network model based on vector autoregression. The population-level model was also estimated with a multilevel approach. Effects of different preprocessing steps were evaluated as well. The characteristics of the longitudinal networks were investigated to gain insight into the emotion dynamics.ResultsIn the population-level networks, longitudinal network connectivity was strongest in the healthy group, with nodes showing more and stronger longitudinal associations with each other. Individually estimated networks varied strongly across individuals. Individual variations in network connectivity were unrelated to baseline characteristics (depression status, neuroticism, severity). A multilevel approach applied to the same data showed higher connectivity in the MDD group, which seemed partly related to the preprocessing approach.ConclusionsThe sparse network approach can be useful for the estimation of networks with multiple nodes, where overparameterization is an issue, and for individual-level networks. However, its current inability to model random effects makes it less useful as a population-level approach in case of large heterogeneity. Different preprocessing strategies appeared to strongly influence the results, complicating inferences about network density.

The Study of Psychopathology from the Network Analysis Perspective: A Systematic Review

“…It was found that people with high neuroticism and healthy people had denser emotion networks in comparison with people low in neuroticism or depressed people, respectively. Authors tried to see the relationship between neuroticism and the centrality in the networks and their variability, but they arrived at different conclusions [75, 76], remaining unclear how this trait influences emotional changes in time.…”

Robust symptom networks in recurrent major depression across different levels of genetic and environmental risk

“…Second, we used patients’ retrospective reports of symptoms during their lifetime worst episode of MD, so we can only examine average undirected relations between symptoms in networks. Longitudinal data, on the other hand, would have given the opportunity to discover specific temporal or directed relationships between symptoms for specific subsamples (e.g., guilt predicting suicidality in one subgroup, but suicidality predicting guilt in another subgroup) or for specific individuals (De Vos et al, 2017). In addition, more fine-grained assessments of symptoms as opposed to dichotomous (present/absent) assessments of symptoms could have improved power to pick up differences in network structure.…”

The Differential Role of Central and Bridge Symptoms in Deactivating Psychopathological Networks

“…The analysis of the global connectivity of longitudinal dynamic networks has produced mixed results. Two studies concluded that the networks of individuals diagnosed with mental disorders displayed higher connectivity (Pe et al, 2015; Wichers et al, 2016), but these results may be dependent on the methodological options during data pre-processing and network estimation (de Vos et al, 2017). Another study (Groen et al, 2019) did not observe differences in the connectivity of the dynamic networks of individuals with persisting symptoms compared to individuals displaying symptom remission.…”