Abstract

Deep brain stimulation (DBS) has quickly emerged as an established therapy for the treatment of medically refractory movement disorders. However, current DBS techniques for electrode targeting and stimulation parameter selection do not account for the biophysics of neural stimulation. We are currently developing an integrated patient-specific modeling approach that will provide the clinician with realistic estimates of the volume of tissue activated during stimulation. There are a variety of fundamental assumptions that are made in these models, and the effects of these assumptions are unclear. The principal hypothesis of this study is that the 3D conductivity of the brain and the capacitance of the electrode-tissue interface each play a fundamental role in the neural response to DBS. We will evaluate the effects of these biophysical features with the aid of a 3D finite element model (FEM) that integrates detailed brain morphology, tissue conductivity and electrode properties with an innovative Fourier FEM solver. This system has the potential to improve patient outcomes by improving target and trajectory selection, reducing time in surgery, and maximizing stimulation benefit while minimizing side effects. ? ?

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Postdoctoral Individual National Research Service Award (F32)
Project #
5F32NS052042-02
Application #
7052872
Study Section
Special Emphasis Panel (ZRG1-F01 (20))
Program Officer
Pancrazio, Joseph J
Project Start
2005-04-01
Project End
2008-03-31
Budget Start
2006-04-01
Budget End
2007-03-31
Support Year
2
Fiscal Year
2006
Total Cost
$45,976
Indirect Cost

  Institution

Name
Cleveland Clinic Lerner
Department
Other Basic Sciences
Type
Schools of Medicine
DUNS #
135781701
City
Cleveland
State
OH
Country
United States
Zip Code
44195

  Publications

Butson, Christopher R; Cooper, Scott E; Henderson, Jaimie M et al. (2011) Probabilistic analysis of activation volumes generated during deep brain stimulation. Neuroimage 54:2096-104
Chaturvedi, Ashutosh; Butson, Christopher R; Lempka, Scott F et al. (2010) Patient-specific models of deep brain stimulation: influence of field model complexity on neural activation predictions. Brain Stimul 3:65-7
Maks, C B; Butson, C R; Walter, B L et al. (2009) Deep brain stimulation activation volumes and their association with neurophysiological mapping and therapeutic outcomes. J Neurol Neurosurg Psychiatry 80:659-66
Butson, Christopher R; McIntyre, Cameron C (2008) Current steering to control the volume of tissue activated during deep brain stimulation. Brain Stimul 1:7-15
Butson, Christopher R; McIntyre, Cameron C (2007) Differences among implanted pulse generator waveforms cause variations in the neural response to deep brain stimulation. Clin Neurophysiol 118:1889-94
Butson, Christopher R; Cooper, Scott E; Henderson, Jaimie M et al. (2007) Patient-specific analysis of the volume of tissue activated during deep brain stimulation. Neuroimage 34:661-70
McIntyre, Cameron C; Miocinovic, Svjetlana; Butson, Christopher R (2007) Computational analysis of deep brain stimulation. Expert Rev Med Devices 4:615-22
Butson, C R; Noecker, A M; Maks, C B et al. (2007) StimExplorer: deep brain stimulation parameter selection software system. Acta Neurochir Suppl 97:569-74
Butson, Christopher R; Cooper, Scott E; Henderson, Jaimie M et al. (2006) Predicting the effects of deep brain stimulation with diffusion tensor based electric field models. Med Image Comput Comput Assist Interv 9:429-37
Butson, Christopher R; McIntyre, Cameron C (2006) Role of electrode design on the volume of tissue activated during deep brain stimulation. J Neural Eng 3:1-8

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