VoNA The Visualisation of Neuronal Activation

To observe and shape electrode-generated outputs for electrical stimulation in the brain using a Finite Element Model

Master Thesis (2024)
Authors

N.F. van Lith (TU Delft - Mechanical Engineering)

Supervisors

Tiago Lopes Marta da Costa (TU Delft - Bio-Electronics)

Faculty
Mechanical Engineering, Mechanical Engineering
Copyright
© 2024 Niels van Lith
More Info
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Publication Year
2024
Language
English
Copyright
© 2024 Niels van Lith
Graduation Date
30-01-2024
Awarding Institution
Delft University of Technology
Programme
Biomedical Engineering
Faculty
Mechanical Engineering, Mechanical Engineering
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Abstract

The development of neural prostheses, especially those directly targeting the brain, requires extensive research and modelling before clinical trials can be performed. Currently, the resolution of artificial vision is not sufficient for everyday tasks. By studying the expected spatial extent of stimulation, we aim to provide insights to researchers that can be used to improve the resolution of artificial vision. The goal of this MSc thesis was to visualise the shape and intensity of electric fields in the cortex as a response to intracortical microelectrode stimulation to observe the expected regions of neuronal activation considering electrode design parameters. To do this, parameters that can influence the generated electric field and the regions of activated tissue have been defined. Implementing these parameters in a Finite Element Model (FEM) allows the computation of the generated electric field in 3D of a stimulating electrode to observe the spatial extent of activated tissue. The spatial extent of activated tissue can be estimated using simplified methods such as the activating function (AF) or current density threshold. The result is a parameterised framework that creates a Visualisation of Neuronal Activation (VoNA) that can be used to assess activated tissue regions for varying scenarios by defining material properties and dimensions of the model, and allows for the adjustment of the stimulation configuration, electrode contact spacing, customisation of electrode size and modulation of stimulation current. It enables the user to tune the model settings towards their specific needs and explore the possibilities by visualising the results from different angles by defining subsets of the entire solution. In line with expectations, the presented models show that the model parameters can influence factors such as the generated electric field, the current density and electric potential, which are indicators of neuronal activation. The findings support the hypothesis that these parameters should be considered during electrode design to achieve accurate stimulation.

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