Network Models

This model is an elementary formulation of the network theory of psychopathology applied to depression.

This model simulates the joint evolution of Major Depressive Episode (MDE) and Generalized Anxiety Disorder (GAD) using a network model. The aim of the model is to explain the high levels of comorbidity between MDE and GAD.

A computational model of Panic Disorder defined as a non-linear dynamical system. This model explains, among others, individual differences in the propensity to experience panic attacks, key phenomenological characteristics of those attacks, the onset of Panic Disorder, and the efficacy of cognitive behavioral therapy. A panic attack occurs when an individual's perceived threat rises as a result of a negative appraisal of the current situation. Usually mitigated by escape behaviour, when such an option is not readily available, heightened perceived threat may result in a panic attack.

A network model that models the idea of personality types influencing the dynamical landscape of a symptoms network. This idea is applied to the personality trait neuroticism and the symptoms of major depressive disorder and is able to accommodate important phenomena of this disorder.

CAL model (Category Abstraction Learning), a cognitive framework formally describing category learning built on similarity-based generalization, dissimilarity-based abstraction, two attention learning mechanisms, error-driven knowledge structuring, and stimulus memorization.

In this model, we characterize major depression (MD) as a complex dynamic 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 non-depressed 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.