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Deep Brain Stimulation for Treating Parkinsons ’ Disease

Deep Brain Stimulation for Treating Parkinsons ’ Disease. Ishita Basu,ECE ; Daniela Tuninetti,ECE ; Daniel Graupe,ECE ; Konstantin Slavin,Neurosurgery Primary Grant Support: Dr. Tuninetti’s start-up package.

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Deep Brain Stimulation for Treating Parkinsons ’ Disease

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  1. Deep Brain Stimulation for Treating Parkinsons’ Disease IshitaBasu,ECE; Daniela Tuninetti,ECE; Daniel Graupe,ECE; Konstantin Slavin,Neurosurgery Primary Grant Support: Dr. Tuninetti’s start-up package. • MOTIVATION: Deep Brain Stimulation (DBS) is a surgical method of relieving advanced stage Parkinson’s Disease (PD) patients of most of their debilitating symptoms (like tremor). DBS involves stimulating the area of the brain that controls movements with a high frequency train of electrical pulses through an implanted electrode. • PROBLEMS: In today’s DBS systems the stimulation parameters are optimized manually by the physician with visual feedbacks from the patient. Moreover, the stimulation is continuous and constant over time. • OBJECTIVES: 1) Design an intermittent deep brain stimulation instead of a continuous stimulation. This ensures lower power requirements, a longer battery life, and possiblye reduce damage to healthy neurons in PD patients. 2) Tune the parameters of the DBS (frequency, pulse amplitude, pulse duration) by employing a closed-loop control. This allows to tailor the DBS stimulation to each individual patients thus enhancing DBS efficacy. • A cluster of actively firing neurons is modeled as a group of coupled oscillators that is mathematically described by stochastic differential (Langevin) equations. • The signals measured from PD patients, such as the local field potential from the brain and the muscular potential from surface EMG, are modeled parametrically. • The signal parameters are adaptively estimated for each patient from the measured signals and to optimize the DBS stimulation parameters. • Simulation results shows that on an average a train of high frequency pulses with its frequency and/or amplitude stochastically modulated with Gaussian noise performs better than its deterministic counterpart. • Next, we will test the above hypothesis on a model with parameters extracted from actual measured signals. • We will trace the evolution of the parameters extracted from the measured signals which will serve as a reference in the control loop. • We will optimize the DBS stimulation parameters.

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