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Brain Circuit Model Helps Understand Parkinson’s Disease

Leonid Rubchinsky (IUPUI School of Science)

Leonid Rubchinsky (IUPUI School of Science)

Researchers from Indiana University-Purdue University in Indianapolis have developed a mathematical model of the brain’s neural circuitry to better understand information disruptions in the brains of Parkinson’s disease patients. Their findings appear in the journal Chaos: An Interdisciplinary Journal of Nonlinear Science (paid subscription required).

Mathematical sciences professor Leonid Rubchinsky (pictured left) examined the exchange of electric signals in the brain of Parkinson’s patients, using the model to show that repetitious, overlapped firing of neurons can lead to waves of overly synchronized brain activity. The paper reports how Rubchinsky and postdoc (at the time) Choongseok Park investigated the activity of neural networks in the brain involved in motor control and affected by Parkinson’s disease.

Some 60,000 new cases of Parkinson’s disease are diagnosed annually in the United States according to the National Institutes of Health. Parkinson’s disease occurs when the nerve cells in the brain that make dopamine are slowly destroyed. Without dopamine, the nerve cells in that part of the brain cannot properly send messages, which leads to the loss of muscle function and gets worse with time.

Rubchinsky and Park aimed at understanding the algorithms that could explain the behavior of neural networks in the brain. They used geometric analysis and perturbation theory to help simplify the complex networks to a simpler set of equations. Their analysis identified three variables operating on two different time scales. The synchronization of the variables is generated by their interaction and the strength of the synapses in the nerve cells.

Rubchinsky says the modeling provides insights that cannot gained from clinical studies, and can help guide emerging therapies based on deep brain stimulation. The technology for deep brain stimulation may be available, notes Rubchinsky, “but we don’t have the algorithms — the formulas and other mathematical tools — necessary to know what we are trying to stimulate and how.  Our model, and others that will follow,” Rubchinsky adds, “should make deep brain stimulation a feasible therapy for Parkinson’s disease within the next decade.”

Read more: Neuroscience, Computational Physics Help Diagnose Autism

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