A machine learning approach to support deep brain stimulation programming

Keywords: Volume of tissue activated, kernel-based learning, anisotropy

Abstract

Adjusting the stimulation parameters is a challenge in deep brain stimulation (DBS) therapy due to the vast number of different configurations available. As a result, systems based on the visualization of the volume of tissue activated (VTA) produced by a particular stimulation setting have been developed. However, the medical specialist still has to search, by trial and error, for a DBS set-up that generates the desired VTA. Therefore, our goal is developing a DBS parameter tuning strategy for current clinical devices that allows defining a target VTA under biophysically viable constraints. We propose a machine learning approach that allows estimating the DBS parameter values for a given VTA, which comprises two main stages: i) A K-nearest neighbors-based deformation to define a target VTA preserving biophysically viable constraints. ii) A parameter estimation stage that consists of a data projection using metric learning to highlight relevant VTA properties, and a regression/classification algorithm to estimate the DBS parameters that generate the target VTA. Our methodology allows setting a biophysically compliant target VTA and accurately predicts the required configuration of stimulation parameters. Also, the performance of our approach is stable for both isotropic and anisotropic tissue conductivities. Furthermore, the computational time of the trained system is acceptable for real-world implementations.
|Abstract
= 374 veces | PDF
= 235 veces|

Downloads

Download data is not yet available.

Author Biographies

Viviana Gómez-Orozco, Universidad Tecnológica de Pereira

Profesora asociada de ingeniería

Grupo de Investigación Automática, Facultad de Ingeniería

Iván De La Pava Panche, Universidad Tecnológica de Pereira

Profesor asociada de ingeniería

Grupo de Investigación Automática, Facultad de Ingeniería

Andrés Marino Álvarez-Meza, Universidad Nacional de Colombia

Profesor asociada de ingeniería

Grupo de Procesamiento y Reconocimiento de Señales

Mauricio Alexander Álvarez-López, University of Sheffield

Engineering Associate Research Professor

Department of Computer Science

Álvaro Ángel Orozco-Gutiérrez, Universidad Tecnológica de Pereira

Profesor asociada de ingeniería

Grupo de Investigación Automática, Facultad de Ingeniería

References

Claudio Da Cunha, Suelen L Boschen, Alexander Gómez-a, Erika K Ross, William SJ Gibson, Hoon-Ki Min, Kendall H Lee, and Charles D Blaha. Toward sophisticated basal ganglia neuromodulation: Review on basal ganglia deep brain stimulation. Neuroscience & Biobehavioral Reviews, 58:186–210, 2015.

Cameron C McIntyre and Ross W Anderson. Deep brain stimulation mechanisms: the control of network activity via neurochemistry modulation. Journal of Neurochemistry, 139(S1):338–345, 2016.

Kelly L Collins, Emily M Lehmann, and Parag G Patil. Deep brain stimulation for movement disorders. Neurobiology of Disease, 38(3):338–345, 2010.

Michael S Okun. Deep-brain stimulation for Parkinson’s disease. New England Journal of Medicine, 367(16):1529–1538, 2012.

XL Chen, YY Xiong, GL Xu, and XF Liu. Deep brain stimulation. Interventional Neurology, 1(3-4):200–212, 2013.

Thomas E Schlaepfer, Bettina H Bewernick, Sarah Kayser, Burkhard Mädler, and Volker A Coenen. Rapid effects of deep brain stimulation for treatment-resistant major depression. Biological Psychiatry, 73(12):1204–1212, 2013.

Nealen G Laxpati, Willard S Kasoff, and Robert E Gross. Deep brain stimulation for the treatment of epilepsy: circuits, targets, and trials. Neurotherapeutics, 11(3):508–526, 2014.

Daniel R Cleary, Alp Ozpinar, Ahmed M Raslan, and Andrew L Ko. Deep brain stimulation for psychiatric disorders: where we are now. Neurosurgical Focus, 38(6):E2, 2015.

Avin Veerakumar and Olivier Berton. Cellular mechanisms of deep brain stimulation: activity-dependent focal circuit reprogramming? Current Opinion in Behavioral Sciences, 4:48–55, 2015.

Andrea A Kühn and Jens Volkmann. Innovations in deep brain stimulation methodology. Movement Disorders, 32(1):11–19, 2017.

Svjetlana Miocinovic, Suvarchala Somayajula, Shilpa Chitnis, and Jerrold L Vitek. History, applications, and mechanisms of deep brain stimulation. JAMA neurology, 70(2):163–171, 2013.

Karen Hunka, Oksana Suchowersky, Susan Wood, Lorelei Derwent, and Zelma HT Kiss. Nursing time to program and assess deep brain stimulators in movement disorder patients. Journal of Neuroscience Nursing, 37(4):204, 2005.

Jens Volkmann, Elena Moro, and Rajesh Pahwa. Basic algorithms for the programming of deep brain stimulation in Parkinson’s disease. Movement Disorders, 21(S14):S284–S289, 2006.

Mattias Åström, Ludvic U Zrinzo, Stephen Tisch, Elina Tripoliti, Marwan I Hariz, and Karin Wårdell. Method for patient-specific finite element modeling and simulation of deep brain stimulation. Medical & Biological Engineering & Computing, 47(1):21–28, 2009.

Michael H Pourfar, Alon Y Mogilner, Sierra Farris, Monique Giroux, Maria Gillego, Yufan Zhao, David Blum, Hemant Bokil, and Mark C Pierre. Model-based deep brain stimulation programming for Parkinson’s disease: the GUIDE pilot study. Stereotactic and Functional Neurosurgery, 93(4):231–239, 2015.

Christopher R Butson and Cameron C McIntyre. Tissue and electrode capacitance reduce neural activation volumes during deep brain stimulation. Clinical Neurophysiology, 116(10):2490–2500, 2005.

CR Butson, AM Noecker, CB Maks, and Cameron C McIntyre. Stim- Explorer: deep brain stimulation parameter selection software system. In Operative Neuromodulation, pages 569–574. Springer, 2007.

Nada Yousif, Roman Borisyuk, Nicola Pavese, Dipankar Nandi, and Peter Bain. Spatiotemporal visualization of deep brain stimulationinduced effects in the subthalamic nucleus. European Journal of Neuroscience, 36(2):2252–2259, 2012.

Nada Yousif, Nuri Purswani, Richard Bayford, Dipankar Nandi, Peter Bain, and Xuguang Liu. Evaluating the impact of the deep brain stimulation induced electric field on subthalamic neurons: A computational modelling study. Journal of Neuroscience Methods, 188(1):105– 112, 2010.

Peter M Lauro, Nora Vanegas-Arroyave, Ling Huang, Paul A Taylor, Kareem A Zaghloul, Codrin Lungu, Ziad S Saad, and Silvina G Horovitz. DBSproc: an open source process for DBS electrode localization and tractographic analysis. Human Brain Mapping, 37(1):422– 433, 2016.

Christopher R Butson, Scott E Cooper, Jaimie M Henderson, and Cameron C McIntyre. Patient-specific analysis of the volume of tissue activated during deep brain stimulation. Neuroimage, 34(2):661– 670, 2007.

Till A Dembek, Michael T Barbe, Mattias Åström, Mauritius Hoevels, Veerle Visser-Vandewalle, Gereon R Fink, and Lars Timmermann. Probabilistic mapping of deep brain stimulation effects in essential tremor. NeuroImage: Clinical, 13:164–173, 2017.

Anneke MM Frankemolle, Jennifer Wu, Angela M Noecker, Claudia Voelcker-Rehage, Jason C Ho, Jerrold L Vitek, Cameron C McIntyre, and Jay L Alberts. Reversing cognitive–motor impairments in Parkinson’s disease patients using a computational modelling approach to deep brain stimulation programming. Brain, 133(3):746– 761, 2010.

Christopher R Butson and Cameron C McIntyre. Current steering to control the volume of tissue activated during deep brain stimulation. Brain Stimulation, 1(1):7–15, 2008.

Ashutosh Chaturvedi, Christopher R Butson, Scott F Lempka, Scott E Cooper, and Cameron C McIntyre. Patient-specific models of deep brain stimulation: influence of field model complexity on neural activation predictions. Brain Stimulation, 3(2):65–77, 2010.

Grégoire Walckiers, Benjamin Fuchs, Jean-Philippe Thiran, Juan R Mosig, and Claudio Pollo. Influence of the implanted pulse generator as reference electrode in finite element model of monopolar deep brain stimulation. Journal of Neuroscience Methods, 186(1):90–96, 2010.

Tianhe C Zhang and Warren M Grill. Modeling deep brain stimulation: point source approximation versus realistic representation of the electrode. Journal of Neural Engineering, 7(6):066009, 2010.

Kees J Van Dijk, Rens Verhagen, Ashutosh Chaturvedi, Cameron C McIntyre, Lo J Bour, Ciska Heida, and Peter H Veltink. A novel lead design enables selective deep brain stimulation of neural populations in the subthalamic region. Journal of Neural Engineering, 12(4):046003, 2015.

Bryan Howell and Cameron C McIntyre. Analyzing the tradeoff between electrical complexity and accuracy in patient-specific computational models of deep brain stimulation. Journal of Neural Engineering, 13(3):036023, 2016.

Christopher R Butson and Cameron C McIntyre. Role of electrode design on the volume of tissue activated during deep brain stimulation. Journal of Neural Engineering, 3(1):1, 2006.

EJ Peterson, O Izad, and Dustin J Tyler. Predicting myelinated axon activation using spatial characteristics of the extracellular field. Journal of neural engineering, 8(4):046030, 2011.

M Astrom, Elin Diczfalusy, Hubert Martens, and K Wardell. Relationship between neural activation and electric field distribution during deep brain stimulation. IEEE Trans. Biomed. Eng., 62(2):664–672, 2015.

Ashutosh Chaturvedi, J Luis Luján, and Cameron C McIntyre. Artificial neural network based characterization of the volume of tissue activated during deep brain stimulation. Journal of Neural Engineering, 10(5):056023, 2013.

I De La Pava, J Mejía, A Álvarez-Meza, M Álvarez, A Orozco, and O Henao. A hierarchical K-nearest neighbor approach for volume of tissue activated estimation. In Iberoamerican Congress on Pattern Recognition, pages 125–133. Springer, 2016.

M Fiorella Contarino, Lo J Bour, Rens Verhagen, Marcel AJ Lourens, Rob MA de Bie, Pepijn van den Munckhof, and PR Schuurman. Directional steering: A novel approach to deep brain stimulation. Neurology, 83(13):1163–1169, 2014.

Marwan Hariz. Deep brain stimulation: new techniques. Parkinsonism & Related Disorders, 20:S192–S196, 2014.

Claudio Pollo, Alain Kaelin-Lang, Markus F Oertel, Lennart Stieglitz, Ethan Taub, Peter Fuhr, Andres M Lozano, Andreas Raabe, and Michael Schüpbach. Directional deep brain stimulation: an intraoperative double-blind pilot study. Brain, page awu102, 2014.

V Gómez-Orozco, J Cuellar, Hernán F García, A Álvarez, M Álvarez, A Orozco, and O Henao. A kernel-based approach for DBS parameter estimation. In Iberoamerican Congress on Pattern Recognition, pages 158–166. Springer, 2016.

Edgar Peña, Simeng Zhang, Steve Deyo, YiZi Xiao, and Matthew D Johnson. Particle swarm optimization for programming deep brain stimulation arrays. Journal of Neural Engineering, 14(1):016014, 2017.

Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh. Algorithms for learning kernels based on centered alignment. The Journal of Machine Learning Research, 13:795–828, 2012.

D Cárdenas-Peña, D Collazos-Huertas, A Álvarez-Meza, and G Castellanos-Dominguez. Supervised kernel approach for automated learning using general stochastic networks. Engineering Applications of Artificial Intelligence, 68:10–17, 2018.

Bernhard Schölkopf, Alexander J Smola, Francis Bach, et al. Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT press, 2002.

Andre Machado, Ali R Rezai, Brian H Kopell, Robert E Gross, Ashwini D Sharan, and Alim-Louis Benabid. Deep brain stimulation for Parkinson’s disease: surgical technique and perioperative management. Movement Disorders, 21(S14):S247–S258, 2006.

Lin-Ching Chang, Derek K Jones, and Carlo Pierpaoli. RESTORE: robust estimation of tensors by outlier rejection. Magnetic Resonance in Medicine, 53(5):1088–1095, 2005.

David S Tuch, Van J Wedeen, Anders M Dale, John S George, and John W Belliveau. Conductivity tensor mapping of the human brain using diffusion tensor MRI. Proceedings of the National Academy of Sciences, 98(20):11697–11701, 2001.

Olgierd Cecil Zienkiewicz, Robert Leroy Taylor, Robert Leroy Taylor, and JZ Zhu. Finite Element Method: Its Basis and Fundamentals. Elsevier, Incorporated, 2013.

Cameron C McIntyre, Andrew G Richardson, and Warren M Grill. Modeling the excitability of mammalian nerve fibers: influence of afterpotentials on the recovery cycle. Journal of Neurophysiology, 87(2):995–1006, 2002.

Michael L Hines, Andrew P Davison, and Eilif Muller. NEURON and Python. Frontiers in Neuroinformatics, 3, 2009.

Joaquın Pizarro, Elisa Guerrero, and Pedro L Galindo. Multiple comparison procedures applied to model selection. Neurocomputing, 48(1-4):155–173, 2002.

M Okun and P Zeilman. Parkinson’s disease: Guide to deep brain stimulation therapy. Hagerstown, MD: National Parkinson Foundation, 2017.

Published
2019-12-10