Temporal Directionality Between Symptoms During Treatment of Depressed Inpatients: A Dynamic Time Warp Network Analysis.

Background: Depression is generally perceived from the perspective of the common-cause disease model. However, the network perspective assumes mutual influence of individual symptoms and stresses the importance of investigating symptom dynamics. Gaining a better understanding of symptom dynamics within individuals might contribute to more effective treatments.

Methods: Current exploratory longitudinal research studied the associations and directionality between 43 symptoms from the generic questionnaire Symptom Questionnaire-48 (SQ-48) using dynamic time warp (DTW) analyses, in which trajectories with similar time-dependent patterns can be identified. Data from individuals were analysed first, yielding distance matrices for all symptom trajectories, after which the data were aggregated.

Results: The 148 included patients were all admitted for the treatment of their clinical depression. Undirected DTW analyses of three patients with longer time series but otherwise randomly chosen showed large variability among individuals. Group-level undirected DTW analyses showed numerous significant edges between symptoms, largely clustering symptoms according to the eight pre-existing subscales of the SQ-48. Group-level directed DTW analyses showed five symptoms with significant outstrength: 'hopeless', 'restless', 'down/depressed', 'feeling tense' and 'no enjoyment', meaning that change in these key symptoms preceded change in other symptoms.

Limitations: The 43 included symptoms of the SQ-48 primarily focus on internalizing problems in severely depressed inpatients, potentially limiting generalizability.

Conclusions: DTW networks provided us with five key symptoms based on the dynamics of symptom scores. Future studies could explore whether process-based therapy targeted at symptoms with high outstrength might result in more effectivity as part of personalized treatment.