A Network Approach to Bipolar Symptomatology in Patients with Different Course Types

The longitudinal mood course is highly variable among patients with bipolar disorder(BD). One of the strongest predictors of the future disease course is the past disease course, implying that the vulnerability for developing a specific pattern of symptoms is rather consistent over time. We therefore investigated whether BD patients with different longitudinal course types have symptom correlation networks with typical characteristics. To this end we used network analysis, a rather novel approach in the field of psychiatry.

Based on two-year monthly life charts, 125 patients with complete 2 year data were categorized into three groups: i.e., a minimally impaired (n = 47), a predominantly depressed (n = 42) and a cycling course (n = 36). Associations between symptoms were defined as the groupwise Spearman's rank correlation coefficient between each pair of items of the Young Mania Rating Scale (YMRS) and the Quick Inventory of Depressive Symptomatology (QIDS). Weighted symptom networks and centrality measures were compared among the three groups.

The weighted networks significantly differed among the three groups, with manic and depressed symptoms being most strongly interconnected in the cycling group. The symptoms with top centrality that were most interconnected also differed among the course group; central symptoms in the stable group were elevated mood and increased speech, in the depressed group loss of self-esteem and psychomotor slowness, and in the cycling group concentration loss and suicidality.

Symptom networks based on the timepoints with most severe symptoms of bipolar patients with different longitudinal course types are significantly different. The clinical interpretation of this finding and its implications are discussed.