  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.
