Computational Modelling and Optimization of Electric Fields Generated by Transcranial Brain Stimulation
Abstract
A range of neurostimulation technologies seek to modulate brain activity using electric fields. These methods have several research and clinical applications, for example in the treatment of Parkinson’s disease and major depressive disorder. Transcranial brain stimulation (TBS) are types of neurostimulation methods where the electric fields are applied from outside the body, using either scalp electrodes or a magnetic coil positioned over head, and therefore do not require surgical interventions. Over recent years, TBS methods have attracted attention for their potential in human neuroscience research and in the treatment of various neuropsychiatric disorders with minimal side effects. However, as the stimulation sources, located outside the head, are far from the stimulation targets, located in the brain, the electric fields generated by TBS are influenced by individual anatomical features such as skull thickness and brain gyrification patterns. These effects are in part responsible for the large variability observed in the outcomes of TBS interventions. To gain a better understanding of how TBS affects the brain, practitioners are turning to computational methods for simulating electric fields in individualized head models. In the first part of this thesis, we attempt to validate and compare tools for automatically creating head models from magnetic resonance (MR) images using intracranial electric field measurements. We will see that, while there is considerable variability between electric field estimates provided by the different modelling tools, the intracranial measurements did not clearly indicate which tool provides the greatest accuracy. In the following chapter, we develop novel algorithms for optimizing electrode positions for transcranial electric stimulation (TES) in order to obtain focal electric fields around given targets. Our optimization methods proved to be reliable and efficient, and we were able to apply them to map the accessibility of thousands of brain regions to focal TES as well as the effects of stimulation parameters. Finally, we describe our implementation of computational modelling and optimization tools for TBS into free and open source software, making our research accessible for practitioners.
Info
Thesis PhD, 2020
UN SDG Classification
DK Main Research Area
Science/Technology
