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Modeling the Dynamics of Disease States in Depression Computational Neuroscience

Major depressive disorder(MDD) is a disabling condition that adversely affects a person general health, work or school life, sleeping and eating habits, and person's family. MDD can strike anyone regardless of age, ethnic background, socioeconomic status or gender. Nearly 1 in 5 people will experience a major depressive episode at some point in their lives.

The disorder is clinically and etiologically heterogeneous. Despite intense research efforts, the response rate of antidepressant treatments are relatively low and the etiology and progression of MDD remain poorly understood.

To advance our understanding of MDD, we use computational modeling. First, we propose a systematic and comprehensive definition of disease states, which is based on a type of mathematical model called a finite-state machine. Second, we propose a dynamical systems model for the progression, or dynamics, of MDD. We study under what conditions the model can account for the occurrence and recurrence of depressive episodes and how we can model the effects of antidepressant treatments and cognitive behavioral therapy within the same dynamical systems model through changing a small subset of parameters.

The model to simulate the dynamics of disease states in depression can be downloaded as a zip File and can be used with Matlab.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science.

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