Identifying the bridge between depression and mania: A machine learning and network approach to bipolar disorder

Abstract

Objectives: Although the cyclic nature of bipolarity is almost by definition a network system, no research to date has attempted to specifically scrutinize the relation of the two bipolar poles, using network psychometrics. We used state-of-the-art network and machine-learning methods to identify symptoms, as well as relations thereof, that bridge depression and mania.Methods: Observational study that made use of mental health data (in particular, 12 symptoms for depression and 12 for mania; all binary) from a large, representative Canadian sample (i.e., Canadian Community Health Survey of 2002). Complete data (N = 36,557; 54.6% female) were analysed using network psychometrics, in conjunction with a random forest algorithm, so as to examine the bidirectional interplay of depressive and manic symptoms.Results: Centrality analyses pointed to symptoms relating to emotionality and hyperactivity as being the most central aspects to depression and mania, respectively. The two syndromes were spatially segregated in the bipolar model and four symptoms appeared crucial in bridging them: sleep disturbance (insomnia and hypersomnia), anhedonia, suicidal ideation, and impulsivity. Our machine-learning algorithm validated the clinical utility of central and bridge symptoms (in the prediction of lifetime episodes of mania and depression), and further suggested that the centrality metrics map almost perfectly onto a data-driven measure of diagnostic utility.Conclusions: Our results replicate key findings from previous clinical network investigations on bipolar disorder, but also extend them by highlighting symptoms that bridge the two bipolar poles, as well as validating their clinical utility. If replicated, these endophenotypes could prove fruitful targets for prevention and intervention strategies on bipolar disorder.

A formal theory of mood instability

“…For example, much like our simulation illustrates (figure 2C), bipolarity is characterized by severe episodes of mania (or hypomania) and depression that persist temporally (over a week for depression; over four days for mania) and recur cyclically (Goodwin et al, 2007). Further, such affective episodes are typically triggered by significant life experiences, such as severe adversities (in the case of depression) and goal-attainment events (in the case of mania) (Johnson et al, 2011;Zavlis et al, 2023). Our model captures this pattern as well, since it suggests that the mechanism by which manic and depressive episodes emerge, persist, and alternate is through emotionally intense events that signal significant life changes.…”