SCIENTIFIC COMPUTING AND IMAGING INSTITUTE
at the University of Utah

An internationally recognized leader in visualization, scientific computing, and image analysis

SCI Publications

2005


E. Bullitt, D. Zeng, G. Gerig, S. Aylward, S. Joshi, J.K. Smith, W. Lin, M. Ewend. “Vessel Tortuosity and Brain Tumor Malignancy: A Blinded Study,” In Acad Radiol, Vol. 12, No. 10, pp. 1232--1240. October, 2005.


C.R. Butson, C.C. McIntyre. “Tissue and electrode capacitance reduce neural activation volumes during deep brain stimulation,” In Clinical Neurophysiology, Vol. 116, No. 10, pp. 2490--500. October, 2005.
DOI: 10.1016/j.clinph.2005.06.023
PubMed ID: 16125463

ABSTRACT

OBJECTIVE: The growing clinical acceptance of neurostimulation technology has highlighted the need to accurately predict neural activation as a function of stimulation parameters and electrode design. In this study we evaluate the effects of the tissue and electrode capacitance on the volume of tissue activated (VTA) during deep brain stimulation (DBS).

METHODS: We use a Fourier finite element method (Fourier FEM) to calculate the potential distribution in the tissue medium as a function of time and space simultaneously for a range of stimulus waveforms. The extracellular voltages are then applied to detailed multi-compartment cable models of myelinated axons to determine neural activation. Neural activation volumes are calculated as a function of the stimulation parameters and magnitude of the capacitive components of the electrode-tissue interface.

RESULTS: Inclusion of either electrode or tissue capacitance reduces the VTA compared to electrostatic simulations in a manner dependent on the capacitance magnitude and the stimulation parameters (amplitude and pulse width). Electrostatic simulations with typical DBS parameter settings (-3 V or -3 mA, 90 micros, 130 Hz) overestimate the VTA by approximately 20\% for voltage- or current-controlled stimulation. In addition, strength-duration time constants decrease and more closely match clinical measurements when explicitly accounting for the effects of voltage-controlled stimulation.

CONCLUSIONS: Attempts to quantify the VTA from clinical neurostimulation devices should account for the effects of electrode and tissue capacitance.

SIGNIFICANCE: DBS has rapidly emerged as an effective treatment for movement disorders; however, little is known about the VTA during therapeutic stimulation. In addition, the influence of tissue and electrode capacitance has been largely ignored in previous models of neural stimulation. The results and methodology of this study provide the foundation for the quantitative analysis of the VTA during clinical neurostimulation.

Keywords: Algorithms, Axons, Axons: physiology, Computer Simulation, Deep Brain Stimulation, Electric Capacitance, Electrodes, Extracellular Space, Extracellular Space: physiology, Finite Element Analysis, Implanted, Models, Myelinated, Myelinated: physiology, Nerve Fibers, Neurological, Neurons, Neurons: physiology, Poisson Distribution, Statistical


S.P. Callahan, M. Ikits, J.L.D. Comba, C.T. Silva. “Hardware-Assisted Visibility Ordering for Unstructured Volume Rendering,” In IEEE Trans. Vis & Comp. Graph., Vol. 11, No. 3, IEEE Educational Activities Department, pp. 285--295. 2005.
ISSN: 1077-2626


F. Calderero, A. Ghodrati, D.H. Brooks, G. Tadmor, R.S. MacLeod. “A Method to Reconstruct Activation Wavefronts Without Isotropy Assumptions Using a Level Sets Approach,” In Functional Imaging and Modeling of the Heart: Third International Workshop (FIMH 2005), Barcelona, June 2-4, pp. 195. 2005.


S.P. Callahan, J.L.D. Comba, P. Shirley, C.T. Silva. “Interactive Rendering of Large Unstructured Grids Using Dynamic Level-of-Detail,” In Proceeding of IEEE Visualization 2005, pp. 26. 2005.


S.P. Callahan. “The k-Buffer and its Applications to Volume Rendering,” Note: Masters Thesis, School of Computing, University of Utah, 2005.


I. Corouge, P.T. Fletcher, S. Joshi, J.H. Gilmore, G. Gerig. “Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis,” In Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv, Vol. 8 (Pt. 1), pp. 131--139. 2005.


M.S. Dalton, B.J. Ellis, T.J. Lujan, J.A. Weiss. “MCL Insertion Site and Contact Forces in the ACL-Deficient Knee,” In Proceedings, 51th Annual Orthopaedic Research Society Meeting, Vol. 30, pp. 814. 2005.


R.E. Debski, J.A. Weiss, W.J. Newman, S.M. Moore, P.J. McMahon. “Stress and Strain in the Anterior Band of the Inferior Glenohumeral Ligament During a Simulated Clinical Examination,” In Journal of Shoulder and Elbow Surgery, Vol. 14, pp. 24S--31S. 2005.


D.E. DeMarle, C.P. Gribble, S. Boulos, S.G. Parker. “Memory Sharing for Interactive Ray Tracing on Clusters,” In Parallel Computing, Vol. 31, No. 2, pp. 221--242. 2005.


P. Fife, T. Wei, J.C. Klewicki, P.A. McMurtry. “Stress Gradient Balance Layers and Scale Hierarchies in Wall-Bounded Flows,” In Journal of Fluid Mechanics, Vol. 532, pp. 165--189. June, 2005.
DOI: 10.1017/S0022112005003988

ABSTRACT

Steady Couette and pressure-driven turbulent channel flows have large regions in which the gradients of the viscous and Reynolds stresses are approximately in balance (stress gradient balance regions). In the case of Couette flow, this region occupies the entire channel. Moreover, the relevant features of pressure-driven channel flow throughout the channel can be obtained from those of Couette flow by a simple transformation. It is shown that stress gradient balance regions are characterized by an intrinsic hierarchy of ‘scaling layers’ (analogous to the inner and outer domains), filling out the stress gradient balance region except for locations near the wall. The spatial extent of each scaling layer is found asymptotically to be proportional to its distance from the wall.

There is a rigorous connection between the scaling hierarchy and the mean velocity profile. This connection is through a certain function A(y+) defined in terms of the hierarchy, which remains O(1) for all y+. The mean velocity satisfies an exact logarithmic growth law in an interval of the hierarchy if and only if A is constant. Although A is generally not constant in any such interval, it is arguably almost constant under certain circumstances in some regions. These results are obtained completely independently of classical inner/outer/overlap scaling arguments, which require more restrictive assumptions.

The possible physical implications of these theoretical results are discussed.


M. Foskey, B. Davis, L. Goyal, S. Chang, E. Chaney, N. Strehl, S. Tomei, J. Rosenman, S. Joshi. “Large Deformation Three-Dimensional Image Registration in Image-Guided Radiation Therapy,” In Phys Med Biol, Vol. 50, No. 24, pp. 5869--5892. December 21, 2005.


N. Fout, H. Akiba, K-L. Ma, A.E. Lefohn, J.M. Kniss. “High-Quality Rendering of Compressed Volume Data Formats,” In Proceedings of The Joint EUROGRAPHICS-IEEE VGTC Symposium on Visualization 2005, 2005.


S.E. Geneser, S. Choe, R.M. Kirby, R.S. MacLeod. “Influence of Stochastic Organ Conductivity in 2D ECG Forward Modeling: A Stochastic Finite Element Study,” In Proceedings of The Joint Meeting of The 5th International Conference on Bioelectromagnetism and The 5th International Symposium on Noninvasive Functional Source Imaging within the Human Brain and Heart, pp. 5528--5531. 2005.


A. Ghodrati, D.H. Brooks, G. Tadmor, B.B. Punske, R.S. MacLeod. “Wavefront-based Inverse Electrocardiography using an Evolving Curve State Vector and Phenomenological Propagation and Potential Models,” In IJBEM, Vol. 7, No. 2, pp. 210--213. 2005.


C.E. Goodyer, M. Berzins. “Parallelisation and Scalability Issues in a Multilevel EHL Solver,” Report, No. 2005.05, School of Computing, University of Leeds, 2005.


L. Grady, T. Tasdizen. “A Geometric Multigrid Approach to Solving the 2D Inhomogeneous Laplace Equation with Internal Dirichlet Boundary Conditions,” In IEEE International Conference on Image Processing, Vol. 2, pp. 642--645. September, 2005.


C. Guerra, V. Pascucci. “Line-Based Object Recognition Using Hausdorff Distance: From Range Images to Molecular Secondary Structure,” In Image and Vision Computing, Vol. 23, No. 4, Note: UCRL-JRNL-208551, pp. 405-415. April, 2005.


J.E. Guilkey, J.B. Hoying JB, J.A. Weiss. “Computational modeling of multicellular constructs with the Material Point Method,” In Journal of Biomechanics, pp. (published online). June, 2005.


D. Gullmar, J.R. Reichenbach, A. Anwander, T. Knosche, C.H. Wolters, M. Eiselt, J. Haueisen. “Influence of Anisotropic Conductivity of the White Matter Tissue on EEG Source Reconstruction - An FEM Simulation Study,” In Int. J. Bioelectromag., Vol. 7, No. 1, pp. 108--110. 2005.