scholarly journals Estimates of Peak Electric Fields Induced by Transcranial Magnetic Stimulation in Pregnant Women as Patients or Operators Using an FEM Full-Body Model

2019 ◽  
pp. 49-73
Author(s):  
Janakinadh Yanamadala ◽  
Raunak Borwankar ◽  
Sergey Makarov ◽  
Alvaro Pascual-Leone
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Pregnant Women ◽  
Electric Fields ◽  
Magnetic Stimulation ◽  
Body Model ◽  
Full Body

scholarly journals Modelling of the Electric Field Distribution in Deep Transcranial Magnetic Stimulation in the Adolescence, in the Adulthood, and in the Old Age

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Serena Fiocchi ◽  
Michela Longhi ◽  
Paolo Ravazzani ◽  
Yiftach Roth ◽  
Abraham Zangen ◽  
...  
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Electric Field ◽  
Electric Fields ◽  
Magnetic Stimulation ◽  
Depressive Disorders ◽  
Brain Structures ◽  
Increasing Demand ◽  
And Gender ◽  
Aging People ◽  
The Brain

In the last few years, deep transcranial magnetic stimulation (dTMS) has been used for the treatment of depressive disorders, which affect a broad category of people, from adolescents to aging people. To facilitate its clinical application, particular shapes of coils, including the so-called Hesed coils, were designed. Given their increasing demand and the lack of studies which accurately characterize their use, this paper aims to provide a picture of the distribution of the induced electric field in four realistic human models of different ages and gender. In detail, the electric field distributions were calculated by using numerical techniques in the brain structures potentially involved in the progression of the disease and were quantified in terms of both amplitude levels and focusing power of the distribution. The results highlight how the chosen Hesed coil (H7 coil) is able to induce the maxima levels ofEmainly in the prefrontal cortex, particularly for the younger model. Moreover, growing levels of induced electric fields with age were found by going in deep in the brain, as well as a major capability to penetrate in the deepest brain structures with an electric field higher than 50%, 70%, and 90% of the peak found in the cortex.

scholarly journals A Multi-Scale Computational Model of the effects of TMS on Motor Cortex

2016 ◽  
Author(s):  
Hyeon Seo ◽  
Natalie Schaworonkow ◽  
Sung Chan Jun ◽  
Jochen Triesch
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Computational Model ◽  
Electric Fields ◽  
Action Potentials ◽  
Magnetic Stimulation ◽  
Pyramidal Neurons ◽  
Pyramidal Cells ◽  
Cell Types ◽  
Cortical Layer ◽  
Multi Scale

AbstractThe detailed biophysical mechanisms through which transcranial magnetic stimulation (TMS) activates cortical circuits are still not fully understood. Here we present a multi-scale computational model to describe and explain the activation of different cell types in motor cortex due to transcranial magnetic stimulation. Our model determines precise electric fields based on an individual head model derived from magnetic resonance imaging and calculates how these electric fields activate morphologically detailed models of different neuron types. We predict detailed neural activation patterns for different coil orientations consistent with experimental findings. Beyond this, our model allows us to predict activation thresholds for individual neurons and precise initiation sites of individual action potentials on the neurons’ complex morphologies. Specifically, our model predicts that cortical layer 3 pyramidal neurons are generally easier to stimulate than layer 5 pyramidal neurons, thereby explaining the lower stimulation thresholds observed for I-waves compared to D-waves. It also predicts differences in the regions of activated cortical layer 5 and layer 3 pyramidal cells depending on coil orientation. Finally, it predicts that under standard stimulation conditions, action potentials are mostly generated at the axon initial segment of corctial pyramidal cells, with a much less important activation site being the part of a layer 5 pyramidal cell axon where it crosses the boundary between grey matter and white matter. In conclusion, our computational model offers a detailed account of the mechanisms through which TMS activates different cortical cell types, paving the way for more targeted application of TMS based on individual brain morphology in clinical and basic research settings.

scholarly journals Real-time estimation of electric fields induced by transcranial magnetic stimulation with deep neural networks

2019 ◽  
Vol 12 (6) ◽  
pp. 1500-1507 ◽  
Author(s):  
Tatsuya Yokota ◽  
Toyohiro Maki ◽  
Tatsuya Nagata ◽  
Takenobu Murakami ◽  
Yoshikazu Ugawa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Transcranial Magnetic Stimulation ◽  
Real Time ◽  
Electric Fields ◽  
Magnetic Stimulation ◽  
Deep Neural Networks ◽  
Time Estimation ◽  
Real Time Estimation

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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