What drives the large differences across patients in terms of treatment efficacy of major depressive disorder (MDD) is unclear. A network approach to psychopathology may help to reveal underlying mechanisms determining patients' capacity for recovery. We used daily diary MDD symptom data and six-month follow-up data on depression to examine how dynamic associations between symptoms relate to the future course of MDD. Daily experiences of depressive symptoms of 69 participants were assessed by means of the SCL-90-R depression subscale, three days a week for a period of six weeks, as part of a larger intervention study. Multilevel vector autoregressive modelling was used to estimate networks of dynamic symptom connections. Long-term outcome was determined by the percentage change in Hamilton Depression Rating Scale (HDRS) score between pre-intervention and six-month follow-up. For patients with more persisting symptoms, the symptom 'feeling everything is an effort' most strongly predicted other symptoms. The networks of the two groups did not significantly differ in overall connectivity. Findings suggest that future research should not solely focus on the presence or intensity of individual symptoms when predicting long-term outcomes, but should also examine the role of a specific symptom in the larger network of dynamic symptom-to-symptom interactions.
Keywords: Major depressive disorder; Multilevel vector autoregressive (VAR) modelling; Network approach; Network connectivity; Psychiatry.
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