Accelerating the computation of the volume of tissue activated during deep brain stimulation using Gaussian processes

Keywords: Deep brain stimulation, volume of tissue activated, multicompartment axon model, emulation, Gaussian process classification


The 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.


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Author Biographies

Iván De La Pava Panche, Universidad Tecnológica de Pereira
Grupo de Automática, Facultad de Ingeniería
Viviana Gómez-Orozco, Universidad Tecnológica de Pereira
Grupo de Automática, Facultad de Ingeniería
Mauricio Alexander Álvarez-López, University of Shefield
Machine Learning group, Department of Computer Science
Óscar Alberto Henao-Gallo, Universidad Tecnológica de Pereira
Grupo de Automática, Facultad de Ingeniería
Genaro Daza-Santacoloma, Instituto de Epilepsia y Parkinson del Eje Cafetero
Academic degree PhD, senior research
Alvaro Ángel Orozco-Gutiérrez, Universidad Tecnológica de Pereira
Grupo de Automática, Facultad de Ingeniería


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