Major depression as a complex dynamic system

TL;DR

This paper models major depression as a complex network of interconnected symptoms, demonstrating how individual symptom network architecture influences depression vulnerability and recovery, and explaining phenomena like spontaneous remission.

Contribution

It introduces the first intra-individual, symptom-based dynamic model of major depression, linking network structure to depression development and maintenance.

Findings

Vulnerable networks have strong symptom connections.

Weakly connected networks are more resilient to stress.

Model explains spontaneous recovery phenomena.

Abstract

In this paper, we characterize major depression (MD) as a complex dynamical system in which symptoms (e.g., insomnia and fatigue) are directly connected to one another in a network structure. We hypothesize that individuals can be characterized by their own network with unique architecture and resulting dynamics. With respect to architecture, we show that individuals vulnerable to developing MD are those with strong connections between symptoms: e.g., only one night of poor sleep suffices to make a particular person feel tired. Such vulnerable networks, when pushed by forces external to the system such as stress, are more likely to end up in a depressed state; whereas networks with weaker connections tend to remain in or return to a healthy state. We show this with a simulation in which we model the probability of a symptom becoming active as a logistic function of the activity of its neighboring symptoms. Additionally, we show that this model potentially explains some well-known empirical phenomena such as spontaneous recovery as well as accommodates existing theories about the various subtypes of MD. To our knowledge, we offer the first intra-individual, symptom-based, process model with the potential to explain the pathogenesis and maintenance of major depression.