Semantically Enabled Modeling of Major Depressive Disorder

Although antidepressant treatment is readily available, the US government reports that Major Depressive Disorder is the leading cause of disability in the U.S. for ages 15-44. Why? Depression is complex. The diagnosis for Major Depressive Disorder describes the signs of depression, but not the cause. There are several distinct biological, physiological, and psychological systems that, when not functioning properly, could result in depression. Furthermore, clinicians are unable to predict which treatment will be successful, since not all patients respond to all antidepressant treatments. In order to properly treat depression, we need to understand the different types of depression, their underlying mechanisms of action, and how they interact in all three aspects: (1) behavioral (emotional and cognitive processes), (2) functional (chemical pathways from brain region to brain region), and (3) mechanistic (dynamics of the neurotransmitters and their receptors).

In this project, we study the effects of how different antidepressant treatments, including non-pharmacological treatments, affect the underlying brain regions, clinical symptoms, and behaviors. We use mathematical modeling and computer simulation to combine clinical research with neuroscience research. So far, these methods have indicated that different treatments result in different response patterns, i.e. with different treatments, the individual symptoms of depression (i.e. anxiety, mood, sleep disturbance, etc.) improve at different times and in a different order. Our current goals include increasing the number of treatments that we explore, incorporating semantic technologies, and ultimately having clinical impact. Findings like these have the potential for huge clinical impact; they could help reduce patient suffering and suicide by reducing the trial and error of current treatment practices.

TWC Faculty

Research Staff

Selected Publications