New method to depicting longitudinal depression symptoms data in a network

What is it about?

In this paper we propose ConNEcT, a network approach for binary symptom data across time. ConNEcT allows to visualize and study the prevalence of different symptoms as well as their co-occurrence, measured by means of a contingency measure in one single network picture. ConNEcT can be complemented with a significance test that accounts for the serial dependence in the data. We apply this approach to data from a study in which patients diagnosed with major depressive disorder weekly reported the absence or presence of eight depression symptoms. We first construct symptom networks for all patients (with rapports for at least 104 weeks) and find strong differences between individuals in which symptom pairs co-occur significantly. Second, to gain insight into these differences, we apply Hierarchical Classes Analysis on the co-occurrence patterns of all patients, showing that they can be grouped into meaningful clusters. As a result, we find that core depression symptoms (i.e., depressed mood and/or diminished interest), cognitive problems and loss of energy seem to co-occur universally, but preoccupation with death, psychomotor problems or eating problems only co-occur with other symptoms for specific patient subgroups.

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Photo by Stefano Pollio on Unsplash

Photo by Stefano Pollio on Unsplash

Why is it important?

Network analysis is an increasingly popular approach to study mental disorders in all their complexity. Multiple methods have been developed to extract networks from cross-sectional data, with these data being either continuous or binary. However, when it comes to time series data, most efforts have focused on continuous data. We propose a method that is capable to investigate binary time series data.

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