SCI Publications

2007

C.R. Butson, C.C. McIntyre. “Differences among implanted pulse generator waveforms cause variations in the neural response to deep brain stimulation,” In Clinical Neurophysiology, Vol. 118, pp. 1889--1894. August, 2007.
ISSN: 1388-2457
DOI: 10.1016/j.clinph.2007.05.061

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OBJECTIVE: Two different Medtronic implantable pulse generator (IPG) models are currently used in clinical applications of deep brain stimulation (DBS): Soletra and Kinetra. The goal of this study was to evaluate and compare the stimulation waveforms produced by each IPG model.

METHODS: We recorded waveforms from a broad range of stimulation parameter settings in each IPG model, and compared them to idealized waveforms that adhered to the parameters specified in the programming device. We then used a previously published computational model to predict the neural response to the various stimulation waveforms.

RESULTS: The stimulation waveforms produced by the IPGs differed from the idealized waveforms assumed in previous theoretical and clinical studies, and the waveforms differed among the IPG models. These differences were greater at higher frequencies and longer pulse widths, and caused variations of up to 0.4 V in activation thresholds for model axons located 3 mm from the DBS electrode contact.

CONCLUSIONS: The specific details of the stimulation waveform directly affect the neural response to DBS and should be accounted for in theoretical and experimental studies of DBS.

SIGNIFICANCE: While the clinical selection of DBS parameters is individualized to each patient based on behavioral outcomes, scientific analysis of stimulation parameter settings and clinical threshold measurements are subject to a previously unrecognized source of error unless the actual waveforms produced by the IPG are accounted for.

C.R. Butson, S.E. Cooper, J.M. Henderson, C.C. McIntyre. “Patient-specific analysis of the volume of tissue activated during deep brain stimulation,” In NeuroImage, Vol. 34, No. 2, pp. 661--670. January, 2007.
ISSN: 1053-8119
DOI: 10.1016/j.neuroimage.2006.09.034
PubMed ID: 17113789

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Despite the clinical success of deep brain stimulation (DBS) for the treatment of movement disorders, many questions remain about its effects on the nervous system. This study presents a methodology to predict the volume of tissue activated (VTA) by DBS on a patient-specific basis. Our goals were to identify the intersection between the VTA and surrounding anatomical structures and to compare activation of these structures with clinical outcomes. The model system consisted of three fundamental components: (1) a 3D anatomical model of the subcortical nuclei and DBS electrode position in the brain, each derived from magnetic resonance imaging (MRI); (2) a finite element model of the DBS electrode and electric field transmitted to the brain, with tissue conductivity properties derived from diffusion tensor MRI; (3) VTA prediction derived from the response of myelinated axons to the applied electric field, which is a function of the stimulation parameters (contact, impedance, voltage, pulse width, frequency). We used this model system to analyze the effects of subthalamic nucleus (STN) DBS in a patient with Parkinson's disease. Quantitative measurements of bradykinesia, rigidity, and corticospinal tract (CST) motor thresholds were evaluated over a range of stimulation parameter settings. Our model predictions showed good agreement with CST thresholds. Additionally, stimulation through electrode contacts that improved bradykinesia and rigidity generated VTAs that overlapped the zona incerta/fields of Forel (ZI/H2). Application of DBS technology to various neurological disorders has preceded scientific characterization of the volume of tissue directly affected by the stimulation. Synergistic integration of clinical analysis, neuroimaging, neuroanatomy, and neurostimulation modeling provides an opportunity to address wide ranging questions on the factors linked with the therapeutic benefits and side effects of DBS.

Keywords: Deep Brain Stimulation, Finite Element Analysis, Humans, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Models, Neurological, Parkinson Disease, Parkinson Disease: therapy, Pyramidal Tracts, Pyramidal Tracts: physiology, Subthalamic Nucleus, Subthalamic Nucleus: physiology

M. Callahan, M.J. Cole, J.F. Shepherd, J.G. Stinstra, C.R. Johnson. “BioMesh3D: A Meshing Pipeline for Biomedical Models,” SCI Institute Technical Report, No. UUSCI-2007-009, University of Utah, 2007.

M. Callahan, M.J. Cole, J.F. Shepherd, J.G. Stinstra, C.R. Johnson. “A Meshing Pipeline for Biomedical Computing,” In Engineering with Computers, Special Issue on Computational Bioengineering, pp. (in press). 2007.

S.P. Callahan, L. Bavoil, V. Pascucci, C.T. Silva. “Progressive Volume Rendering of Large Unstructured Grids,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 13, No. 1, pp. 1307-1314. 2007.

C.J. Cascio, G. Gerig, J. Piven. “Diffusion Tensor Imaging: Application to the Study of the Developing Brain,” In J Am Acad Child Adolesc Psychiatry, Vol. 46, No. 2, pp. 213--223. February, 2007.

J. Cates, P.T. Fletcher, M. Styner, M. Shenton, R.T. Whitaker. “Shape Modeling and Analysis with Entropy-Based Particle Systems,” In Proceedings of Information Processing in Medical Imaging (IPMI) 2007, LNCS 4584, pp. 333--345. 2007.

D. Chisnall, M. Chen, C.D. Hansen. “Ray-Driven Dynamic Working Set Rendering for Complex Volume Scene Graphs Involving Large Point Clouds,” In The Visual Computer, Vol. 23, No. 3, pp. 167--179. 2007.

S. Curtis, R.M. Kirby, J.K. Ryan, C.-W. Shu. “Post-Processing for the Discontinuous Galerkin Method Over Non-Uniform Meshes,” In SIAM Journal of Scientific Computing, Vol. 30, No. 1, pp. 272--289. 2007.

K. Damevski, K. Zhang, S.G. Parker. “Practical Parallel Remote Method Invocation for the Babel Compiler,” In Proceedings of the 2007 Symposium on Component and Framework Technology in High-Performance and Scientific Computing, pp. 131--140. 2007.

K. Damevski, A. Swaminathan, S.G. Parker. “Highly Scalable Distributed Component Framework for Scientific Computing,” In Proceedings of the 3rd International Conference on High Performance Computing and Communication (HPCC 2007), 2007.

K. Damevski, A. Swaminathan, S.G. Parker. “CCALoop: Scalable Design of a Distributed Component Framework,” In Proceedings of the 16th International High Performance Distributed Computing Symposium (HPDC 2007), pp. 213--214. 2007.

J.D. Daniels, L. Ha, T. Ochotta, C.T. Silva. “Robust Smooth Feature Extraction from Point Clouds,” In Proceedings of the 2007 International Conference on Shape Modeling and Applications, Lyon, France, 2007.

B. Davis, P.T. Fletcher, E. Bullitt, S. Joshi. “Population Shape Regression From Random Design Data,” In Proceedings of the Eleventh IEEE International Conference on Computer Vision (ICCV '07), pp. 1-7. 2007.

S. Davidson, S. Cohen-Boulakia, A. Eyal, B. Ludascher, T. McPhillips, S. Bowers, J. Freire. “Provenance in Scientific Workflow Systems,” In IEEE Data Engineering Bulletin, Vol. 32, No. 4, pp. 44--50. 2007.

C.C. Douglas, M.J. Cole, P. Dostert, Y. Efendiev, R.E. Ewing, G. Haase, J. Hatcher, M. Iskandarani, C.R. Johnson, R.A. Lodder. “Dynamically identifying and tracking contaminants in water bodies,” In Proceedings of the 7th International Conference on Computational Science (ICCS) 2007, Part I, Beijing, China, Lecture Notes in Computer Science (LNCS), Vol. 4887, Edited by Y. Shi and G.D. van Albada and P.M.A. Sloot and J.J. Dongarra, Springer-Verlag, Berlin Heidelberg, pp. 1002--1009. May, 2007.

C.C. Douglas, D. Bansal, J.D. Beezley, L.S. Bennethum, S. Chakraborty, J.L. Coen, Y. Efendiev, R.E. Ewing, J. Hatcher, M. Iskandarani, C.R. Johnson, M. Kim, D. Li, R.A. Lodder, J. Mandel, G. Qin, A. Vodacek. “Dynamic data-driven application systems for empty houses, contaminat tracking, and wildland fireline prediction,” In Grid-Based Problem Solving Environments, IFIP series, Edited by P.W. Gaffney and J.C.T. Pool, Springer-Verlag, Berlin, pp. 255-272. 2007.
DOI: 10.1007/978-0-387-73659-4_14

P.T. Fletcher, S. Joshi. “Riemannian Geometry for the Statistical Analysis of Diffusion Tensor Data,” In Signal Processing, Vol. 87, No. 2, pp. 250--262. February, 2007.

P.T. Fletcher, R. Tao, W.-K. Jeong, R.T. Whitaker. “A Volumetric Approach to Quantifying Region-to-Region White Matter Connectivity in Diffusion Tensor MRI,” In Information Processing in Medical Imaging, Vol. 4584/2007, pp. 346--358. 2007.

P.T. Fletcher, S. Powell, N.L. Foster, S. Joshi. “Quantifying Metabolic Asymmetry Modulo Structure in Alzheimer's Disease,” In Lecture Notes in Computer Science, Springer, pp. 446--457. 2007.
DOI: 10.1007/978-3-540-73273-0_37
PubMed ID: 17633720