Bio
“Neuromodulation Therapy: Insights from Computational Studies”
Dr. Butson received a B.S. in Mechanical Engineering from the University of Maryland, M.S. in Electrical Engineering from George Washington University and Ph.D. in Biomedical Engineering from the University of Utah. He completed post-doctoral training at the Cleveland Clinic and is now an Associate Professor at the Medical College of Wisconsin in the Biotechnology & Bioengineering Center. His departmental affiliations are in the Departments of Neurology & Neurosurgery, and at Marquette University in the Department of Biomedical Engineering. His lab uses computational models and human experiments to characterize and predict the effects of neuromodulation therapies including deep brain stimulation, cortical stimulation and transcranial magnetic stimulation. He was a co-founder and contributing inventor for Intelect Medical, which was acquired by Boston Scientific in 2011. He is the founder and CEO of Neuropotential LLC, a neurotechnology company based in Milwaukee, WI. He is an active member of the Society for Neuroscience (SFN), the Institute of Electrical & Electronics Engineers (IEEE) and the Engineering in Medicine & Biology Society (EMBS).
Abstract
“Neuromodulation Therapy: Insights from Computational Studies”
Over recent years neuromodulation therapies such as deep brain stimulation, cortical stimulation and transcranial magnetic stimulation have been successfully used to treat a wide variety of conditions including Parkinson’s disease, depression and traumatic brain injury. During this type of therapy, electromagnetic energy is delivered to targeted anatomical brain regions in order to bring about a functional response. In the best case this results in good therapeutic benefit with minimal side effects. However, substantial variability has been observed in the effectiveness of stimulation. Response rates range from excellent to poor, and few tools are available to characterize the range of effects. We have developed computational tools for the predicting and evaluating the effects of neuromodulation therapy. The main components of this approach are: 1) Quantitative clinical evaluation; 2) Computational modeling to predict the effects of stimulation on a patient-specific basis; 3) Multi-modal imaging; 4) 3D, probabilistic atlases of clinical outcomes; 5) Interactive clinical decision support tools. These components have been combined into a pipeline with which we are able to evaluate individual patients relative to best evidence that has been accumulated from prior patients. This approach combines computational models and clinical outcomes to gain insights that would be difficult to obtain using either method alone. In this talk I will present recent results suggesting that this approach can quantify variability for a wide range of outcomes, and can facilitate the clinical application of neuromodulation therapy.
Note: Light lunch will be served starting at 11:30am.
