Computer Models of Neuroadaptation to Chronic Administration of Antidepressant Drugs
Antidepressants are by far the most widely used psychoactive drugs in the world. Despite that, antidepressants fail to alleviate depression in most patients. No one knows exactly why the effectiveness of antidepressants is so variable. What we do know is that the brain reacts to the long-term (chronic) use of antidepressants by changing the strengths of several different kinds of neurotransmitter receptors. We used a combination of computational methods to simulate and analyze the brain's neurotransmitter systems. We found that the brain can have many different reactions to chronic antidepressant. While some of these are probably associated with depression relief, many others are not. This could explain the variability in outcome experienced by depressed patients taking the same antidepressant. Our simulations also help to explain why certain antidepressants are more effective than others, and predict that certain combinations of antidepressants should be more effective than any single antidepressant. Our results offer new approaches to the treatment of depression, which is by far the most common mental illness in the world.
Thomas J. Anastasio received his Ph.D. in physiology and biophysics at the University of Texas Medical Branch in Galveston. He did his postdoc at Johns Hopkins University in neurophysiology and computational neuroscience. He held a research assistant professorship at the University of Southern California before coming to the University of Illinois, where he is now an associate professor. His primary appointment is in the Department of Molecular and Integrative Physiology, and he has affiliate appointments in bioengineering, computer science, and neuroscience. He is a full-time member of the Beckman Institute, where he is the director of the Computational Neurobiology Laboratory.