Deep brain stimulation (DBS) is an effective treatment for movement disorders and a promising therapy for treating epilepsy and psychiatric disorders. Despite the clinical successes of DBS, there are several aspects of the therapy that can be improved. For example, surgeries to replace primary cell batteries, or to correct misplaced leads, increase the cost and risks of the therapy. The overall research objective is to design and test novel DBS electrode geometries that increase the power efficiency and selectivity of brain stimulation. The outcome will reduce the cost and risks associated with revision surgeries, making DBS a more cost effective and safer therapy, and it will also broaden our understanding of the role of electrode geometry in electrical stimulation.
The first aim i s to use our biophysical model of electrical stimulation and engineering optimization to design electrodes that are more efficient at stimulating various neural elements. We will design two optimal electrode geometries for stimulating the white and grey matter regions of the brain, respectively, by coupling cable models of neurons, finite element models of electric fields, and a search heuristic, the genetic algorithm. The goal of this design optimization is to develop electrodes that are better suited to their intended anatomical target.
The second aim i s to measure experimentally the efficiency and selectivity of the optimized electrode designs during DBS in an animal model. We will quantify the electrical impedance of our electrode designs in vitro, and measure the in vivo stimulation efficiency and selectivity in anesthetized cats. The purpose of these experiments is to compare our results to the predications of the computational models from Aim 1, and to compare the performance of our optimized designs against the conventional DBS electrode used clinically (Medtronic model 3387). Successful execution of this research will impact the clinical efficacy of DBS, as well as other therapies using electrical stimulation, including spinal cord stimulation. In the long-term, the research will help transform electrode design from an ad hoc practice to a calculated science.

Public Health Relevance

Deep brain stimulation (DBS) is an effective treatment for movement disorders, including Parkinson's disease and essential tremor, as well as a promising therapy for treating epilepsy and drug-resistance psychiatric disorders. However, despite the successes of DBS, there are still several areas where the therapy can be improved, such as reducing the number of surgeries required to replace batteries and to correct misplaced leads. Developing innovative approaches to increase the efficiency and selectivity of DBS will increase battery life and reduce the sensitivity of clinical outcomes to electrode (mis) placement, respectively, which will mitigate the cost and risks of battery and lead replacement surgery.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Predoctoral Individual National Research Service Award (F31)
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Special Emphasis Panel (ZRG1-F03B-G (20))
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Ludwig, Kip A
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Duke University
Biomedical Engineering
Schools of Engineering
United States
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Howell, Bryan; Medina, Leonel E; Grill, Warren M (2015) Effects of frequency-dependent membrane capacitance on neural excitability. J Neural Eng 12:056015-56015
Howell, Bryan; Huynh, Brian; Grill, Warren M (2015) Design and in vivo evaluation of more efficient and selective deep brain stimulation electrodes. J Neural Eng 12:046030
Howell, Bryan; Naik, Sagar; Grill, Warren M (2014) Influences of interpolation error, electrode geometry, and the electrode-tissue interface on models of electric fields produced by deep brain stimulation. IEEE Trans Biomed Eng 61:297-307
Howell, Bryan; Lad, Shivanand P; Grill, Warren M (2014) Evaluation of intradural stimulation efficiency and selectivity in a computational model of spinal cord stimulation. PLoS One 9:e114938
Howell, Bryan; Grill, Warren M (2014) Evaluation of high-perimeter electrode designs for deep brain stimulation. J Neural Eng 11:046026