Accelerating the computation of the volume of tissue activated during deep brain stimulation using Gaussian processes
AbstractThe volume of tissue activated (VTA) is a well-established approach to model the direct effect of deep brain stimulation (DBS) on neural tissue. Previous studies have pointed to its potential clinical applications. However, the elevated computational runtime required to estimate the VTA with standard techniques used in biological neural modeling limits its suitability for practical use. The goal of this study was to develop a novel methodology to reduce the computation time of VTA estimation. To that end, we built a Gaussian process emulator. It combines multicompartment axon models coupled to the stimulating electric field with a Gaussian process classifier (GPC), following the premise that computing the VTA from a field of axons is in essence a binary classification problem. We achieved a considerable reduction in the average time required to estimate the VTA, under both ideal isotropic and realistic anisotropic brain tissue conductive conditions, limiting the loss of accuracy and overcoming other drawbacks entailed by alternative methods.
C. McIntyre, W. Grill, D. Sherman and N. Thakor, “Cellular effects of deep brain stimulation: Model-based analysis of activation and inhibition”, Journal of Neurophysiology, vol. 91, no. 4, pp. 1457-1469, 2004.
S. Miocinovic, et al., “Computational analysis of subthalamic nucleus and lenticular fasciculus activation during therapeutic deep brain stimulation”, Journal of neurophysiology, vol. 96, no. 3, pp. 1569–1580, 2006.
C. McIntyre and P. Hahn, “Network perspectives on the mechanisms of deep brain stimulation”, Neurobiology of Disease, vol. 38, no. 3, pp. 329-337, 2010.
R. So, A. Kent and W. Grill, “Relative contributions of local cell and passing fiber activation and silencing to changes in thalamic fidelity during deep brain stimulation and lesioning: A computational modeling study”, Journal of Computational Neuroscience, vol. 32, no. 3, pp. 499-519, 2012.
M. Birdno et al., Response of human thalamic neurons to high-frequency stimulation, PloS one, vol. 9, no. 5, pp. e96026, 2014.
C. Butson and C. McIntyre, “Tissue and electrode capacitance reduce neural activation volumes during deep brain stimulation”, Clinical Neurophysiology, vol. 116, no. 10, pp. 2490-2500, 2005.
C. Butson, S. Cooper, J. Henderson and C. McIntyre, “Patient-specific analysis of the volume of tissue activated during deep brain stimulation”, Neuroimage, vol. 34, no. 2, pp. 661-670, 2007.
N. Yousif et al., “Evaluating the impact of the deep brain stimulation induced electric field on subthalamic neurons: A computational modelling study”, Journal of Neuroscience Methods, vol. 188, no. 1, pp. 105-112, 2010.
N. Yousif et al., “Spatiotemporal visualization of deep brain stimulation-induced effects in the subthalamic nucleus”, European Journal of Neuroscience, vol.36, no. 2, pp. 2252-2259, 2012.
M. Åström et al., “Relationship between neural activation and electric field distribution during deep brain stimulation”, IEEE Trans. Biomed. Eng., vol. 62, no. 2, pp. 664-672, 2015.
A. Frankemolle et al., “Reversing cognitive–motor impairments in Parkinson’s disease patients using a computational modelling approach to deep brain stimulation programming”, Brain, vol. 133, no. 3, pp. 746-761, 2010.
A. Chaturvedi, J. Luján and C. McIntyre, “Artificial neural network based characterization of the volume of tissue activated during deep brain stimulation”, Journal of neural engineering, vol. 10, no. 5, pp. 056023, 2015.
C. Butson and C. McIntyre, “Role of electrode design on the volume of tissue activated during deep brain stimulation”, Journal of Neural Engineering, vol. 3, no. 1, pp. 1-8, 2006.
L. Bastos and A. O’Hagan, “Diagnostics for Gaussian process emulators”, Technometrics, vol. 51, no. 4, pp. 425-438, 2009.
A. O’Hagan, “Bayesian analysis of computer code outputs: A tutorial”, Reliability Engineering & System Safety, vol. 91, no. 10, pp. 1290-1300, 2006.
I. Pava et al., “A Gaussian Process Emulator for Estimating the Volume of Tissue Activated During Deep Brain Stimulation”, in 7th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), Santiago de Compostela, Spain, 2015, pp. 691-699.
T. Zhang and W. Grill, “Modeling deep brain stimulation: Point source approximation versus realistic representation of the electrode”, Journal of neural engineering, vol. 7, no. 6, pp. 066009, 2010.
L. Chang, D. Jones and C. Pierpaoli, “Restore: Robust estimation of tensors by outlier rejection”, Magnetic Resonance in Medicine, vol. 53, no. 5, pp. 1088-1095, 2005.
D. Tuch et al., “Conductivity tensor mapping of the human brain using diffusion tensor MRI”, Proceedings of the National Academy of Sciences, vol. 98, no. 20, pp. 11697-11701, 2001.
A. Chaturvedi et al., “Patient-specific models of deep brain stimulation: Influence of field model complexity on neural activation predictions”, Brain stimulation, vol. 3, no. 2, pp. 65–77, 2010.
F. Rattay, “Analysis of models for extracellular fiber stimulation”, IEEE Transactions on Biomedical Engineering, vol. 36, no. 7, pp. 676–682, 1989.
R. Plonsey and R. Barr, Bioelectricity: A quantitative approach. 3rd ed. New York, USA: Springer Science & Business Media, 2007.
D. Sterratt et al., Principles of computational modelling in neuroscience. 1 ed. Cambridge, UK: Cambridge University Press, 2011.
C. McIntyre, W. Grill, “Excitation of central nervous system neurons by nonuniform electric fields”, Biophysical journal, vol. 76, no. 2, pp. 878–888, 1999.
D. McNeal, “Analysis of a model for excitation of myelinated nerve”, IEEE Transactions on Biomedical Engineering, no. 4, pp. 329–337, 1976.
F. Rattay, “Analysis of models for external stimulation of axons”, IEEE Transactions on Biomedical Engineering, no.10, pp. 974–977, 1986.
C. McIntyre, A. Richardson, W. Grill, “Modeling the excitability of mammalian nerve fibers: Influence of afterpotentials on the recovery cycle”, Journal of neurophysiology, vol. 8, no. 2, pp. 995–1006, 2002.
G. Walckiers, “Bio-electromagnetic model of deep brain stimulation”, Ph.D. dissertation, École polytechnique fédérale de Lausanne EPFL, Lausanne, Switzerland, 2009.
M. Hines, A. Davison, E. Muller, “Neuron and python”, Frontiers in neuroinformatics, vol. 3, pp. 1-12, 2009.
M. Hines, N. Carnevale, “The neuron simulation environment”, Neural computation, vol. 9, no. 6, pp. 1179–1209, 1997.
C. Bishop, Pattern recognition and machine learning. 1 ed. New York, USA: Springer, 2006.
C. Rasmussen and C. Williams, Gaussian Processes for Machine Learning. 1 ed. Cambridge, USA: MIT Press, 2006, pp 33-45.
M. Kuss, C. Rasmussen, “Assessing approximate inference for binary gaussian process classification”, The Journal of Machine Learning Research, vol. 6, pp. 1679–1704, 2005.
H. Nickisch, C. Rasmussen, “Approximations for binary gaussian process classification”, Journal of Machine Learning Research, vol. 9, pp. 2035–2078, 2008.
D. Dos Santos, R. Deutsch, “The positive matching index: A new similarity measure with optimal characteristics”, Pattern Recognition Letters, vol. 31, no. 12, pp. 1570–1576, 2010.
American Association of Neuroscience Nurses (AANN), Care of the Movement Disorder Patient with Deep Brain Stimulation, AANN Clinical Practice Guideline Series, 2009. [Online]. Available: http://apps.aann.org/Default.aspx?TabID=251&productId=104303653. Accessed on: 2015, December 16.
M. Hines, N. Carnevale, “Translating network models to parallel hardware in neuron”, Journal of neuroscience methods, vol. 169, no. 2, pp. 425–455, 2008.
Copyright (c) 2017 Revista Facultad de Ingeniería Universidad de Antioquia
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
All the texts included in the Revista Facultad de Ingenieria Universidad de Antioquia -redin- are protected by copyrights. According to the law, their reproduction through any means, physical or electronic, without written consent by the Editorial Committee is forbidden. Complete texts of the articles will be fully and publically available, which means that they can be read, downloaded, copied, distributed, printed, searched for, or linked to. The opinions expressed in the published articles specifically belong to the authors and are not necessarily the same of the Editorial Committee or of the School of Engineering Management.