DOI

https://doi.org/10.25772/ZE34-D432

Author ORCID Identifier

0000-0001-8219-5101

Defense Date

2023

Document Type

Thesis

Degree Name

Master of Science

Department

Mechanical and Nuclear Engineering

First Advisor

Ravi L. Hadimani

Second Advisor

Radhika Barua

Third Advisor

Karla Mossi

Abstract

Transcranial magnetic stimulation (TMS) is a safe, effective and non-invasive treatment for several mental and psychiatric disorders. TMS is an FDA approved treatment and is commonly applied to patients who do not respond to medications for the treatment of clinical depression, smoking cessation, obsessive-compulsive disorder and migraine. Recently, there has been an increase in the development of electromagnetic neuromodulation techniques targeted at enhancing the effectiveness of TMS devices for the treatment of mental diseases. In TMS stimulation, focality is an important factor which determines the specificity of the pulses induced in different brain tissues. The electromagnetic pulses must be confined to specific regions of the brain and avoid stimulation of the surrounding areas.

The first part of this research focuses on developing focal TMS coils for small animals’ application, which are critical to enhancing the efficacy of TMS therapy and its applicability to neurological disorders which necessitates precise focal stimulation. To activate specific brain regions in small animals, TMS stimulation coils must be highly advanced to accommodate their small head sizes. Current investigations are limited in that most researchers are utilizing commercially available coils designed for human use. These coils result in unwanted stimulation well beyond the intended target with unavoidable side effects. This work, systematically compares innovative coil designs and suitable soft magnetic materials for the development of TMS coils for small animal applications. This can be achieved by altering the properties of the magnetic core material used for the TMS system. Finite element analysis of a rat head model is carried out using Sim4life and ANSYS Maxwell finite element packages while investigating variations associated with changing the coil configuration. The objective of this study is to attain precise stimulation in a specific focal area while minimizing unintended stimulation of the surrounding regions to mitigate potential side effects.

The second project of this research involves predicting the induced electric field (E-field) distribution in brain models using simulated transcranial magnetic stimulation (TMS) and machine learning algorithms. This method aims to overcome the difficulties caused by the brain's complex and heterogeneous composition, which makes it challenging to determine with precision if important brain areas have gotten the right amount of e-field stimulation. While numerical computation methods like finite element analysis (FEA) can estimate e-field distribution, they are computationally intensive and time-consuming. To overcome these limitations, we developed a deep convolutional neural network (DCNN) encoder-decoder model capable of real-time prediction of induced electric fields from T1-weighted and T2-weighted magnetic resonance imaging (MRI) anatomical slices. Utilizing this approach, we conducted TMS on the primary motor cortex of 11 healthy subjects. Head models were generated from the subjects' MRIs using the SimNIBS pipeline and scaled to 20 different sizes for each subject, resulting in a total of 231 head models. Sim4Life, a finite element analysis (FEA) software, was employed to compute induced electric fields, serving as the training data for the DCNN. The key contribution of our model lies in its ability to predict induced electric fields in real-time, enabling accurate and efficient determination of the required TMS strength in targeted brain regions.

Rights

© 2023 Mohannad S. Tashli

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

12-14-2023

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Available for download on Friday, December 13, 2024

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