Aceleración del cálculo del volumen de tejido activo durante estimulación cerebral profunda usando procesos gaussianos

Autores/as

DOI:

https://doi.org/10.17533/udea.redin.n84a03

Palabras clave:

estimulación cerebral profunda, volumen de tejido activo, modelo axonal de múltiples compartimientos, emulación, clasificación de procesos gaussianos

Resumen

 El volumen de tejido activo (VTA) es un enfoque bien establecido para modelar los efectos directos de la estimulación cerebral profunda en el tejido neuronal. Estudios previos han señalado sus posibles aplicaciones clínicas. Sin embargo, el elevado costo computacional requerido para estimar el VTA con las técnicas estándar utilizadas en el modelado neuronal biológico limita su usabilidad a nivel práctico. El objetivo de este estudio fue desarrollar una metodología novedosa para reducir el tiempo de cálculo en la estimación del VTA. Con ese fin, se construyó un emulador basado en procesos gaussianos. Este combina modelos axonales de múltiples compartimientos acoplados al campo de estimulación eléctrica con un sistema de clasificación basado en procesos gaussianos, siguiendo la premisa de que calcular el VTA a partir de un campo axonal es en esencia un problema de clasificación binaria. Se logró una reducción considerable en el tiempo promedio requerido para estimar el VTA, tanto bajo condiciones de conductividad isotrópica idealizada como bajo condiciones realistas de conductividad anisotrópica, limitando la perdida de precisión y superando otros inconvenientes presentes en métodos alternativos.

|Resumen
= 347 veces | PDF (ENGLISH)
= 232 veces|

Descargas

Los datos de descargas todavía no están disponibles.

Biografía del autor/a

Iván De La Pava Panche, Universidad Tecnológica de Pereira

Grupo de Automática, Facultad de Ingenierías.

Viviana Gómez-Orozco, Universidad Tecnológica de Pereira

Grupo de Automática, Facultad de Ingenierías.

Mauricio Alexander Álvarez-López, Universidad de Shefield

Grupo de Machine Learning, Departamento de Ciencias de la Computación.

Óscar Alberto Henao-Gallo, Universidad Tecnológica de Pereira

Grupo de Automática, Facultad de Ingenierías.

Alvaro Ángel Orozco-Gutiérrez, Universidad Tecnológica de Pereira

Grupo de Automática, Facultad de Ingenierías.

Citas

C. McIntyre, W. Grill, D. Sherman, and N. Thakor, “Cellular effects of deep brain stimulation: modelbased 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, W. Tang, J. Dostrovsky, W. Hutchison, and W. Grill, “Response of human thalamic neurons to high-frequency stimulation,” PLoS One, vol. 9, no. 5, pp. 1-10, 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, 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, pp. 2252-2259, 2012.

M. Åström, E. Diczfalusy, H. Martens, and K. Wårdell, “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. 1-8, 2013.

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. 10-11, pp. 1290-1300, 2006.

I. De La 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, 066009 pp. 1-11, 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, V. Wedeen, A. Dale, J. George and J. Belliveau, “Conductivity tensor mapping of the human brain using diffusion tensor MRI,” Proceedings of the National Academy of Sciences of the USA, vol. 98, no. 20, pp. 11697-11701, 2001.

A. Chaturvedi, C. Butson, S. Lempka, S. Cooper, and 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. 65–67, 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, B. Graham, A. Gillies, and D. Willshaw, Principles of computational modelling in neuroscience, 1st ed. Cambridge, UK: Cambridge University Press, 2011.

C. McIntyre and 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, vol. 23, no. 4, pp. 329–337, 1976.

F. Rattay, “Analysis of models for external stimulation of axons,” IEEE Transactions on Biomedical Engineering, vol. 33, no. 10, pp. 974–977, 1986.

C. McIntyre, A. Richardson, and W. 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.

G. Walckiers, “Bio-electromagnetic model of deep brain stimulation,” Ph.D. dissertation, École polytechnique fédérale de Lausanne, Lausanne, Switzerland, 2009.

M. Hines, A. Davison, and E. Muller, “Neuron and python,” Frontiers in Neuroinformatics, vol. 3, no. 1, pp. 1-12, 2009.

M. Hines and N. Carnevale, “The neuron simulation environment,” Neural Computation, vol. 9, no. 6, pp. 1179–1209, 1997.

C. Bishop, Pattern recognition and machine learning, 1st ed. New York, USA: Springer, 2006.

C. Rasmussen and C. Williams, Gaussian Processes for Machine Learning, 1st ed. Cambridge, USA: MIT Press, 2006.

M. Kuss and C. Rasmussen, “Assessing approximate inference for binary gaussian process classification,” The Journal of Machine Learning Research, vol. 6, pp. 1679–1704, 2005.

H. Nickisch and C. Rasmussen, “Approximations for binary gaussian process classification,” Journal of Machine Learning Research, vol. 9, pp. 2035–2078, 2008.

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. 14, pp. 284-289, 2006.

M. Hines and N. Carnevale, “Translating network models to parallel hardware in neuron,” Journal of neuroscience methods, vol. 169, no. 2, pp. 425–455, 2008.

Descargas

Publicado

2017-09-25

Cómo citar

De La Pava Panche, I., Gómez-Orozco, V., Álvarez-López, M. A., Henao-Gallo, Óscar A., Daza-Santacoloma, G., & Orozco-Gutiérrez, A. Ángel. (2017). Aceleración del cálculo del volumen de tejido activo durante estimulación cerebral profunda usando procesos gaussianos. Revista Facultad De Ingeniería Universidad De Antioquia, (84), 17–26. https://doi.org/10.17533/udea.redin.n84a03