Three-Dimensional Head Model Simulation of Transcranial Magnetic Stimulation

2004 ◽  
Vol 51 (9) ◽  
pp. 1586-1598 ◽  
Author(s):  
T.A. Wagner ◽  
M. Zahn ◽  
A.J. Grodzinsky ◽  
A. Pascual-Leone
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Magnetic Stimulation ◽  
Model Simulation ◽  
Three Dimensional ◽  
Head Model

scholarly journals Comparison of Induced Fields in Virtual Human and Rat Heads by Transcranial Magnetic Stimulation

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Ya-Wen Lu ◽  
Mai Lu
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Magnetic Stimulation ◽  
Animal Studies ◽  
Brain Research ◽  
Three Dimensional ◽  
Virtual Human ◽  
Impedance Method ◽  
Magnetic Flux Density ◽  
Biological Mechanisms ◽  
First Time

Transcranial magnetic stimulation (TMS) shows significant values in both brain research and therapeutic applications of cognitive neuroscience, neurophysiology, and psychiatry. Animal studies of TMS provide a potential way for learning the biological mechanisms of actions of TMS. In this paper, we presented the comparison of human TMS and rat TMS by using the conventional figure-of-eight coil for the first time. Three-dimensional distributions of magnetic flux density and induced electric field in both virtual human and rat heads were obtained through the 3D impedance method. The results indicated that smaller TMS coils are needed for stimulation of the rat brain. A rat-specific figure-of-eight coil was designed by considering the coil radii, number of coil turns, and the injected coil currents. We found that the numerically designed Fo8 coil can be applied to the rat TMS with improved focality while also keeping high stimulation intensities.

scholarly journals Real-time computation of transcranial magnetic stimulation electric fields using self-supervised deep learning

2021 ◽  
Author(s):  
Hongming Li ◽  
Zhi-De Deng ◽  
Desmond Oathes ◽  
Yong Fan
Keyword(s):  
Boundary Condition ◽  
Deep Learning ◽  
Transcranial Magnetic Stimulation ◽  
Real Time ◽  
Electric Fields ◽  
Magnetic Stimulation ◽  
State Of The Art ◽  
Rank Test ◽  
Head Model ◽  
Magnetic Vector Potential

Background: Electric fields (E-fields) induced by transcranial magnetic stimulation (TMS) can be modeled using partial differential equations (PDEs) with boundary conditions. However, existing numerical methods to solve PDEs for computing E-fields are usually computationally expensive. It often takes minutes to compute a high-resolution E-field using state-of-the-art finite-element methods (FEM). Methods: We developed a self-supervised deep learning (DL) method to compute precise TMS E-fields in real-time. Given a head model and the primary E-field generated by TMS coils, a self-supervised DL model was built to generate a E-field by minimizing a loss function that measures how well the generated E-field fits the governing PDE and Neumann boundary condition. The DL model was trained in a self-supervised manner, which does not require any external supervision. We evaluated the DL model using both a simulated sphere head model and realistic head models of 125 individuals and compared the accuracy and computational efficiency of the DL model with a state-of-the-art FEM. Results: In realistic head models, the DL model obtained accurate E-fields with significantly smaller PDE residual and boundary condition residual than the FEM (p<0.002, Wilcoxon signed-rank test). The DL model was computationally efficient, which took about 0.30 seconds on average to compute the E-field for one testing individual. The DL model built for the simulated sphere head model also obtained an accurate E-field whose difference from the analytical E-fields was 0.004, more accurate than the solution obtained using the FEM. Conclusions: We have developed a self-supervised DL model to directly learn a mapping from the magnetic vector potential of a TMS coil and a realistic head model to the TMS induced E-fields, facilitating real-time, precise TMS E-field modeling.

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