scholarly journals Simulation of transcranial magnetic stimulation in head model with morphologically-realistic cortical neurons

2018 ◽  
Author(s):  
Aman Aberra ◽  
Boshuo Wang ◽  
Warren M Grill ◽  
Angel V Peterchev
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Electric Fields ◽  
Cortical Neurons ◽  
Magnetic Stimulation ◽  
Brain Activity ◽  
Pyramidal Cells ◽  
Human Head ◽  
Mechanistic Explanation ◽  
Head Model ◽  
Neural Activation

Transcranial magnetic stimulation (TMS) enables non-invasive modulation of brain activity with both clinical and research applications, but fundamental questions remain about the neural types and elements it activates and how stimulation parameters affect the neural response. We integrated detailed neuronal models with TMS-induced electric fields in the human head to quantify the effects of TMS on cortical neurons. TMS activated with lowest intensity layer 5 pyramidal cells at their intracortical axonal terminations in the superficial gyral crown and lip regions. Layer 2/3 pyramidal cells and inhibitory basket cells may be activated too, whereas direct activation of layers 1 and 6 was unlikely. Neural activation was largely driven by the field magnitude, contrary to theories implicating the field component normal to the cortical surface. Varying the induced current's direction caused a waveform-dependent shift in the activation site and provided a mechanistic explanation for experimentally observed differences in thresholds and latencies of muscle responses. This biophysically-based simulation provides a novel method to elucidate mechanisms and inform parameter selection of TMS and other forms of cortical stimulation.

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 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.

scholarly journals A multi-scale computational model of the effects of TMS on motor cortex

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 1945 ◽  
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

The 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 Deep Transcranial Magnetic Stimulation: Improved Coil Design and Assessment of the Induced Fields Using MIDA Model

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Amine M. Samoudi ◽  
Emmeric Tanghe ◽  
Luc Martens ◽  
Wout Joseph
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Nucleus Accumbens ◽  
Electric Field ◽  
Electric Fields ◽  
Magnetic Stimulation ◽  
Maximum Amplitude ◽  
Human Head ◽  
Brain Structures ◽  
Magnetic Flux Density ◽  
Electric Field Amplitude

Stimulation of deep brain structures by transcranial magnetic stimulation (TMS) is a method for activating deep neurons in the brain and can be beneficial for the treatment of psychiatric and neurological disorders. To numerically investigate the possibility for deeper brain stimulation (electric fields reaching the hippocampus, the nucleus accumbens, and the cerebellum), combined TMS coils using the double-cone coil with the Halo coil (HDA) were modeled and investigated. Numerical simulations were performed using MIDA: a new multimodal imaging-based detailed anatomical model of the human head and neck. The 3D distributions of magnetic flux density and electric field were calculated. The percentage of volume of each tissue that is exposed to electric field amplitude equal or greater than 50% of the maximum amplitude of E in the cortex for each coil was calculated to quantify the electric field spread (V50). Results show that only the HDA coil can spread electric fields to the hippocampus, the nucleus accumbens, and the cerebellum with V50 equal to 0.04%, 1.21%, and 6.2%, respectively.

scholarly journals Simulation of transcranial magnetic stimulation in head model with morphologically-realistic cortical neurons

2020 ◽  
Vol 13 (1) ◽  
pp. 175-189 ◽  
Author(s):  
Aman S. Aberra ◽  
Boshuo Wang ◽  
Warren M. Grill ◽  
Angel V. Peterchev
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Cortical Neurons ◽  
Magnetic Stimulation ◽  
Head Model

scholarly journals Task-Modulated Brain Activity Predicts Antidepressant Responses of Prefrontal Repetitive Transcranial Magnetic Stimulation: A Randomized Sham-Control Study

2021 ◽  
Vol 5 ◽  
pp. 247054702110068
Author(s):  
Cheng-Ta Li ◽  
Chih-Ming Cheng ◽  
Chi-Hung Juan ◽  
Yi-Chun Tsai ◽  
Mu-Hong Chen ◽  
...  
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Magnetic Stimulation ◽  
Antidepressant Treatment ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Brain Activity ◽  
Characteristic Curve ◽  
Depression Score ◽  
Double Blind ◽  
Antidepressant Efficacy ◽  
Group B

Background Prolonged intermittent theta-burst stimulation (piTBS) and repetitive transcranial magnetic stimulation (rTMS) are effective antidepressant interventions for major depressive disorder (MDD). Cognition-modulated frontal theta (frontalθ) activity had been identified to predict the antidepressant response to 10-Hz left prefrontal rTMS. However, whether this marker also predicts that of piTBS needs further investigation. Methods The present double-blind randomized trial recruited 105 patients with MDD who showed no response to at least one adequate antidepressant treatment in the current episode. The recruited patients were randomly assigned to one of three groups: group A received piTBS monotherapy; group B received rTMS monotherapy; and group C received sham stimulation. Before a 2-week acute treatment period, electroencephalopgraphy (EEG) and cognition-modulated frontal theta changes (Δfrontalθ) were measured. Depression scores were evaluated at baseline, 1 week, and 2 weeks after the initiation of treatment. Results The Δfrontalθ at baseline was significantly correlated with depression score changes at week 1 (r = −0.383, p = 0.025) and at week 2 for rTMS group (r = −0.419, p = 0.014), but not for the piTBS and sham groups. The area under the receiver operating characteristic curve for Δfrontalθ was 0.800 for the rTMS group (p = 0.003) and was 0.549 for the piTBS group (p = 0.619). Conclusion The predictive value of higher baseline Δfrontalθ for antidepressant efficacy for rTMS not only replicates previous results but also implies that the antidepressant responses to rTMS could be predicted reliably at baseline and both piTBS and rTMS could be effective through different neurobiological mechanisms.

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