Major depressive disorder (MDD) and generalized anxiety disorder (GAD) often co-occur with somatic symptomatology. Little is known about the contributions of individual symptoms to this association and more insight into their relationships could help to identify symptoms that are central in the processes behind the co-occurrence. This study explores associations between individual MDD/GAD symptoms and somatic symptoms by using the network approach.

MDD/GAD symptoms were assessed in 2704 participants (mean age 41.7 years, 66.1% female) from the Netherlands Study of Depression and Anxiety using the Inventory of Depressive Symptomatology. Somatic symptoms were assessed with the somatization scale of the Four-Dimensional Symptom Questionnaire. The technique eLasso was used to estimate the network of MDD/GAD and somatic symptoms.

The network structure showed numerous associations between MDD/GAD and somatic symptoms. In general, neurovegetative and cognitive/affective MDD/GAD symptoms showed a similar strength of connections to the somatic domain. However, associations varied substantially across individual symptoms. MDD/GAD symptoms with many and strong associations to the somatic domain included anxiety and fatigue, whereas hypersomnia and insomnia showed no connections to somatic symptoms. Among somatic symptoms, excessive perspiration and pressure/tight feeling in chest were associated with the MDD/GAD domain, while muscle pain and tingling in fingers showed only a few weak associations.

Individual symptoms show differential associations in the co-occurrence of MDD/GAD with somatic symptomatology. Strongly interconnected symptoms are important in furthering our understanding of the interaction between the symptom domains, and may be valuable targets for future research and treatment.

Structured interviews and questionnaires are important tools to screen for major depressive disorder. Recent research suggests that, in addition to studying the mean level of total scores, researchers should focus on the dynamic relations among depressive symptoms as they unfold over time. Using network analysis, this paper is the first to investigate these patterns of short-term (i.e. session to session) dynamics for a widely used psychological questionnaire for depression – the Beck Depression Inventory (BDI-II).

With the newly developed vector autoregressive (VAR) multilevel method we estimated the network of symptom dynamics that characterizes the BDI-II, based on repeated administrations of the questionnaire to a group of depressed individuals who participated in a treatment study of an average of 14 weekly assessments. Also the centrality of symptoms and the community structure of the network were examined.

The analysis showed that all BDI-II symptoms are directly or indirectly connected through patterns of temporal influence. In addition, these influences are mutually reinforcing, ‘loss of pleasure’ being the most central item in the network. Community analyses indicated that the dynamic structure of the BDI-II involves two clusters, which is consistent with earlier psychometric analyses.

The network approach expands the range of depression research, making it possible to investigate the dynamic architecture of depression and opening up a whole new range of questions and analyses. Regarding clinical practice, network analyses may be used to indicate which symptoms should be targeted, and in this sense may help in setting up treatment strategies.