Un acercamiento basado en aprendizaje de máquina para apoyar la programación de la estimulación cerebral profunda
DOI:
https://doi.org/10.17533/udea.redin.20190729Palabras clave:
volumen de tejido activado, aprendizaje basado en kernels, anisotropíaResumen
Ajustar los parámetros de estimulación es un desafío en la estimulación cerebral profunda (DBS), debido a la gran cantidad de configuraciones disponibles. Como resultado, se han desarrollado sistemas basados en la visualización del volumen de tejido activado (VTA) producido por una configuración de estimulación particular. Sin embargo, el especialista todavía tiene que buscar, mediante ensayo y error, una configuración DBS que genere el VTA deseado. Por lo tanto, nuestro objetivo es desarrollar una estrategia de ajuste de los parámetros de DBS para los dispositivos clínicos actuales que permita definir un VTA objetivo bajo restricciones biofísicamente viables. Proponemos un enfoque de aprendizaje de máquina que permite estimar los valores de los parámetros de DBS para un VTA dado, que consta de dos etapas principales: i) Una deformación basada en K-vecinos más cercanos para definir un VTA objetivo sujeto a restricciones biofísicas. ii) Una etapa de estimación de parámetros que consiste en una proyección de datos para resaltar las propiedades relevantes del VTA, y un algoritmo de regresión/clasificación para estimar los parámetros DBS necesarios para generar el VTA objetivo. Nuestra metodología permite establecer un VTA objetivo compatible biofísicamente y predice con precisión la configuración requerida de los parámetros de estimulación. Además, el rendimiento de nuestro enfoque es estable tanto para conductividades del tejido isotrópicas como anisotrópicas. Además, el tiempo de cómputo del sistema entrenado es aceptable para implementaciones en el mundo real.
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D. Cunha and et al , “Toward sophisticated basal ganglia neuromodulation: Review on basal ganglia deep brain stimulation,” Neuroscience & Biobehavioral Reviews , vol. 58, pp. 186–210, Nov. 2015.
C. C. McIntyre and R. W. Anderson, “Deep brain stimulation mechanisms: the control of network activity via neurochemistry modulation,” Journal of Neurochemistry , vol. 139, no. S1, pp. 338–345, 2016.
K. L. Collins, E. M. Lehmann, and P. G. Patil, “Deep brain stimulation for movement disorders,” Neurobiology of Disease , vol. 38, no. 3, pp. 338–345, 2010.
M. S. Okun, “Deep-brain stimulation for Parkinson’s disease,” New England Journal of Medicine , vol. 367, no. 16, pp. 1529–1538, 2012.
X. Chen, Y. Xiong, G. Xu, and X. Liu, “Deep brain stimulation,” Interventional Neurology , vol. 1, no. 3-4, pp. 200–212, 2013.
T. E. Schlaepfer, B. H. Bewernick, S. Kayser, B. Mädler, and V. A. Coenen, “Rapid effects of deep brain stimulation for treatment-resistant major depression,” BiologicalPsychiatry , vol. 73, no. 12, pp. 1204–1212, 2013.
N. G. Laxpati, W. S. Kasoff, and R. E. Gross, “Deep brain stimulation for the treatment of epilepsy: circuits, targets, and trials,” Neurotherapeutics , vol. 11, no. 3, pp. 508–526, 2014.
D. R. Cleary, A. Ozpinar, A. M. Raslan, and A. L. Ko, “Deep brain stimulation for psychiatric disorders: where we are now,” Neurosurgical Focus , vol. 38, no. 6, p. E2, 2015.
A. Veerakumar and O. Berton, “Cellular mechanisms of deep brain stimulation: activity-dependent focal circuit reprogramming?” Current Opinion in Behavioral Sciences , vol. 4, pp. 48–55, 2015.
A. A. Kühn and J. Volkmann, “Innovations in deep brain stimulation methodology,” Movement Disorders , vol. 32, no. 1, pp. 11–19, 2017.
S. Miocinovic, S. Somayajula, S. Chitnis, and J. L. Vitek, “History, applications, and mechanisms of deep brain stimulation,” JAMA neurology , vol. 70, no. 2, pp. 163–171, 2013.
K. Hunka, O. Suchowersky, S. Wood, L. Derwent, and Z. H. Kiss, “Nursing time to program and assess deep brain stimulators in movement disorder patients,” Journal of Neuroscience Nursing , vol. 37, no. 4, p. 204, 2005.
J. Volkmann, E. Moro, and R. Pahwa, “Basic algorithms for the programming of deep brain stimulation in Parkinson’s disease,” Movement Disorders , vol. 21, no. S4, pp. S284–S289, 2006.
M. Astrom and et al , “Method for patient-specific finite element modeling and simulation of deep brain stimulation,” Medical & Biological Engineering & Computing , vol. 47, no. 1, pp. 21–28, 2009.
M. H. Pourfar and et al , “Model-based deep brain stimulation programming for Parkinson’s disease: the GUIDE pilot study,” Stereotactic and Functional Neurosurgery , vol. 93, no. 4, pp. 231–239, 2015.
C. R. Butson and C. C. McIntyre, “Tissue and electrode capacitance reduce neural activation volumes during deep brain stimulation,” Clinical Neurophysiology , vol. 116, no. 10, October 2005. [Online]. Available: https://doi.org/10.1016/j.clinph.2005.06.023
C. Butson, A. Noecker, C. Maks, and C. C. McIntyre, “StimExplorer: deep brain stimulation parameter selection software system,” Acta Neurochirurgica. Supplement , vol. 97, no. pt2, pp. 569–74, 2007.
N. Yousif, R. Borisyuk, N. Pavese, D. Nandi, and P. Bain, “Spatiotemporal visualization of deep brain stimulation-induced effects in the subthalamic nucleus,” European Journal of Neuroscience , vol. 36, no. 2, July 2012. [Online]. Available: https://doi.org/10.1111/j.1460-9568.2012.08086.x
N. Yousif and 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, April 30 2010. [Online]. Available: https://doi.org/10.1016/j.jneumeth.2010.01.026
P. M. Lauro and et al , “DBSproc: an open source process for DBS electrode localization and tractographic analysis,” HumanBrain Mapping , vol. 37, no. 1, pp. 422–433, 2016.
C. R. Butson, S. E. Cooper, J. M. Henderson, and C. C. McIntyre, “Patient-specific analysis of the volume of tissue activated during deep brain stimulation,” Neuroimage , vol. 34, no. 2, January 15 2007. [Online]. Available: https://doi.org/10.1016/j.neuroimage.2006.09.034
T. A. Dembek and et al , “Probabilistic mapping of deep brain stimulation effects in essential tremor,” NeuroImage: Clinical , vol. 13, pp. 164–173, 2017.
A. M. Frankemolle and et al , “Reversing cognitive-motor impairments in Parkinson’s disease patients using a computational modelling approach to deep brain stimulation programming,” Neuroimage , vol. 133, no. 3, March 2010. [Online]. Available: https://doi.org/10.1093/brain/awp315
C. R. Butson and C. C. McIntyre, “Current steering to control the volume of tissue activated during deep brain stimulation,” Brain Stimulation , vol. 1, no. 1, pp. 7–15, 2008.
A. Chaturvedi, C. R. Butson, S. F. Lempka, S. E. Cooper, and C. C. McIntyre, “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.
G. Walckiers, B. Fuchs, J. P. Thiran, J. R. Mosig, and C. Pollo, “Influence of the implanted pulse generator as reference electrode in finite element model of monopolar deep brain stimulation,” Journal of Neuroscience Methods , vol. 186, no. 1, pp. 90–96, 2010.
T. C. Zhang and W. M. Grill, “Modeling deep brain stimulation: point source approximation versus realistic representation of the electrode,” Journal of Neural Engineering , vol. 7, no. 6, December 2010. [Online]. Available: https://doi.org/10.1088/1741-2560/7/6/066009
K. J. Van Dijk and et al , “A novel lead design enables selective deep brain stimulation of neural populations in the subthalamic region,” Journal of Neural Engineering , vol. 12, no. 4, p. 046003, 2015.
B. Howell and C. C. McIntyre, “Analyzing the tradeoff between electrical complexity and accuracy in patient-specific computational models of deep brain stimulation,” Journal of Neural Engineering , vol. 13, no. 3, p. 036023, 2016.
C. R. Butson and C. C. McIntyre, “Role of electrode design on the volume of tissue activated during deep brain stimulation,” Journal of Neural Engineering , vol. 3, no. 1, March 2006. [Online]. Available: https://doi.org/10.1088/1741-2560/3/1/001
E. Peterson, O. Izad, and D. J. Tyler, “Predicting myelinated axon activation using spatial characteristics of the extracellular field,” Journal of Neural Engineering , vol. 8, no. 4, p. 046030, 2011.
M. Astrom, E. Diczfalusy, H. Martens, and K. Wardell, “Relationship between neural activation and electric field distribution during deep brain stimulation,” IEEE Trans. Biomed. Eng. , vol. 62, no. 2, February 2015. [Online]. Available: https://doi.org/10.1109/TBME. 2014.2363494
A. Chaturvedi, J. L. Luján, and C. 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, October 2013. [Online]. Available: https://doi.org/10.1088/1741-2560/10/5/056023
I. De La Pava and et al , “A hierarchical K-nearest neighbor approach for volume of tissue activated estimation,” in IberoamericanCongress on Pattern Recognition , Lima, Perú, 2016, pp. 125–133.
M. F. Contarino and et al , “Directional steering: A novel approach to deep brain stimulation,” Neurology , vol. 83, no. 13, pp. 1163–1169, 2014.
M. Hariz, “Deep brain stimulation: new techniques,” Parkinsonism& Related Disorders , vol. 20, no. 13, pp. S192–S196, 2014.
C. Pollo and et al , “Directional deep brain stimulation: an intraoperative double-blind pilot study,” Brain , vol. 137, no. pt7, pp. 2015–2026, 2014.
V. Gómez and et al , “A kernel-based approach for DBS parameter estimation,” in Iberoamerican Congresson Pattern Recognition , Lima, Perú, 2016, pp. 158–166.
E. Peña, S. Zhang, S. Deyo, Y. Xiao, and M. D. Johnson, “Particle swarm optimization for programming deep brain stimulation arrays,” JournalofNeuralEngineering , vol. 14, no. 1, p. 016014, 2017.
C. Cortes, M. Mohri, and A. Rostamizadeh, “Algorithms for learning kernels based on centered alignment,” Journal of Machine Learning Research , vol. 13, no. 1, pp. 795–828, 2012.
D. Cárdenas, D. Collazos, A. Álvarez, and G. Castellanos, “Algorithms for learning kernels based on centered alignment,” Engineering Applications of Artificial Intelligence , vol. 68, pp. 10–17, 2018.
B. Schölkopf and A. J. Smola, Learning with kernels: support vector machines, regularization, optimization, and beyond . Cambridge, USA: MIT press, 2001.
A. Machado and et al , “Deep brain stimulation for Parkinson’s disease: surgical technique and perioperative management,” Movement Disorders , vol. 21, no. S14, pp. S247–S258, 2006.
L. C. Chang, D. K. Jones, and C. Pierpaoli, “ARESTORE: robust estimation of tensors by outlier rejection,” Magnetic Resonance in Medicine , vol. 53, no. 5, May 2005. [Online]. Available: https://doi.org/10.1002/mrm.20426
D. S. Tuch, V. J. Wedeen, A. M. Dale, J. S. George, and J. W. Belliveau, “Conductivity tensor mapping of the human brain using diffusion tensor MRI,” in Proceedings of the National Academy of Sciences , USA, 2001, pp. 11 697–11 701.
O. C. Zienkiewicz, R. L. Taylor, and J. Zhu, Finite Element Method: Its Basis and Fundamentals . Elsevier, Incorporated, 2013.
C. C. McIntyre, A. G. Richardson, and W. M. Grill, “Modeling the excitability of mammalian nerve fibers: influence of afterpotentials on the recovery cycle,” Journal of Neurophysiology , vol. 87, no. 2, pp. 995–1006, 2002.
M. L. Hines, A. P. Davison, and E. Muller, “NEURON and Python,” Frontiers in Neuroinformatics , January 28 2009. [Online]. Available: https://doi.org/10.3389/neuro.11.001.2009
J. Pizarro, E. Guerrero, and P. L. Galindo, “Multiple comparison procedures applied to model selection,” Neurocomputing , vol. 48, no. 1-4, pp. 155–173, 2002.
M. Okun and P. Zeilman, “Parkinson’s disease: Guide to deep brain stimulation therapy. Hagerstown, MD: National Parkinson Foundation,” 2017.
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