Paper Info
Title
Investigating the Effects of Anatomical Structures on the Induced Electric Field in the Brain in Transcranial Magnetic Stimulation.
Abstract
Transcranial magnetic stimulation (TMS) is capable of stimulating neurons in the brain non-invasively and provides numerous possibilities for the treatment of various neurological disorders such as major depressive disorder, Parkinson's disease, obsessive compulsive disorder. TMS coils can affect the distribution of induced electric fields significantly, thus the design of TMS coils is always a popular topic in TMS studies. Yet the importance of the role of anatomical structures in the induced electric field has not been thoroughly investigated. Therefore, this work has compared the strength of electric fields induced from fifty realistic head models with twelve commercial or novel TMS coils to explore how anatomical structures affect the electric field. It has been found that the electric field strengths among the fifty head models showed highly correlated patterns. The coils were placed at two positions, where all the twelve coils were placed at the vertex and eight of them were placed at the dorsolateral prefrontal cortex of the head due to the coil geometry. Notably, fifty heterogeneous head models that are derived from MRI data were used in the simulations for examining the difference on the performance of TMS coils caused by different anatomical structures. A total of one thousand simulations have been conducted, providing a large amount of data for analysis. Clinical Relevance- This provides a basis to make treatment protocols or predictions in TMS clinical trials considering the different anatomical structures among subjects.
Year
DOI
Venue
2022
10.1109/EMBC48229.2022.9871810
Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
DocType
Volume
ISSN
Conference
2022
2694-0604
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Xiaojing Zhong100.34
Hanjun Jiang220743.12
David C Jiles300.34
Zhihua Wang402.37
Jingyi Li544.00
Bing Song600.68