Study of the Electromagnetic Field of Transcranial Magnetic Stimulation Based on the Real Head Model

2009 ◽  
Author(s):  
WeiZhong He ◽  
Peng Zhou ◽  
DongDong Lin ◽  
Xin Zhao ◽  
MingShi Wang
Keyword(s):  
Electromagnetic Field ◽  
Transcranial Magnetic Stimulation ◽  
Magnetic Stimulation ◽  
Head Model ◽  
The Real

scholarly journals Theoretical Analysis and Design of an Innovative Coil Structure for Transcranial Magnetic Stimulation

2021 ◽  
Vol 11 (4) ◽  
pp. 1960
Author(s):  
Naming Zhang ◽  
Ziang Wang ◽  
Jinhua Shi ◽  
Shuya Ning ◽  
Yukuo Zhang ◽  
...  
Keyword(s):  
Electromagnetic Field ◽  
Transcranial Magnetic Stimulation ◽  
Theoretical Analysis ◽  
Numerical Simulations ◽  
Magnetic Stimulation ◽  
Influential Factors ◽  
Intersection Angle ◽  
Electromagnetic Field Distribution ◽  
Analysis And Design ◽  
Coil Structure

Previous research showed that pulsed functional magnetic stimulation can activate brain tissue with optimum intensity and frequency. Conventional stimulation coils are always set as a figure-8 type or Helmholtz. However, the magnetic fields generated by these coils are uniform around the target, and their magnetic stimulation performance still needs improvement. In this paper, a novel type of stimulation coil is proposed to shrink the irritative zone and strengthen the stimulation intensity. Furthermore, the electromagnetic field distribution is calculated and measured. Based on numerical simulations, the proposed coil is compared to traditional coil types. Moreover, the influential factors, such as the diameter and the intersection angle, are also analyzed. It was demonstrated that the proposed coil has a better performance in comparison with the figure-8 coil. Thus, this work suggests a new way to design stimulation coils for transcranial magnetic stimulation.

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.

Repetitive transcranial magnetic stimulation reduces cortisol concentrations in bulimic disorders

2010 ◽  
Vol 41 (6) ◽  
pp. 1329-1336 ◽  
Author(s):  
A. M. Claudino ◽  
F. Van den Eynde ◽  
D. Stahl ◽  
T. Dew ◽  
M. Andiappan ◽  
...  
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Salivary Cortisol ◽  
Magnetic Stimulation ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Controlled Trial ◽  
Food Craving ◽  
Double Blind ◽  
The Real ◽  
Left Dlpfc ◽  
Dorsolateral Prefrontal

BackgroundIn people with bulimic eating disorders, exposure to high-calorie foods can result in increases in food craving, raised subjective stress and salivary cortisol concentrations. This cue-induced food craving can be reduced by repetitive transcranial magnetic stimulation (rTMS). We investigated whether rTMS has a similar effect on salivary cortisol concentrations, a measure of hypothalamic–pituitary–adrenal axis (HPAA) activity.MethodWe enrolled twenty-two female participants who took part in a double-blind randomized sham-controlled trial on the effects of rTMS on food craving. Per group, eleven participants were randomized to the real or sham rTMS condition. The intervention consisted of one session of high-frequency rTMS delivered to the left dorsolateral prefrontal cortex (DLPFC). Salivary cortisol concentrations were assessed at four time points throughout the 90-min trial. To investigate differences in post-rTMS concentrations between the real and sham rTMS groups, a random-effects model including the pre-rTMS cortisol concentrations as covariates was used.ResultsSalivary cortisol concentrations following real rTMS were significantly lower compared with those following sham rTMS. In this sample, there was also a trend for real rTMS to reduce food craving more than sham rTMS.ConclusionsThese results suggest that rTMS applied to the left DLPFC alters HPAA activity in people with a bulimic disorder.

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