Review on biophysical modelling and simulation studies for transcranial magnetic stimulation

Gómez-Tames, José; Laakso, Ilkka; Hirata, Akimasa

doi:10.1088/1361-6560/aba40d

Cited by 32 publications

(18 citation statements)

References 160 publications

(359 reference statements)

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“…The coils were reproduced in the simulation environment as dimensionless wires placed at the center of the real wires 16,38 . This implied a 2D coil approximation that may slightly underestimate the induced E‐Field, but the overall error was typically below 2% 16,39 …”

Section: Methodsmentioning

confidence: 99%

“…The coils were reproduced in the simulation environment as dimensionless wires placed at the center of the real wires. 16,38 This implied a 2D coil approximation that may slightly underestimate the induced E-Field, but the overall error was typically below 2%. 16,39 To model the presence of the operator, we considered an anatomical human body model, Duke, a standard young adult male (34-year-old, 1.77 m, 70.2 kg), member of the Virtual Population (ViP., v.3.0).…”

Section: 2 Dosimetric Model For Clinician Exposure Assessmentmentioning

confidence: 99%

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Systematic numerical assessment of occupational exposure to electromagnetic fields of transcranial magnetic stimulation

et al. 2022

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Purpose This study aims to perform a classification and rigorous numerical evaluation of the risks of occupational exposure in the health environment related to the administration of transcranial magnetic stimulation (TMS) treatment. The study investigates the numerically estimated induced electric field that occurs in the human tissues of an operator caused by exposure to the variable magnetic field produced by TMS during treatments. This could be a useful starting point for future risk assessment studies and safety indications in this context. Methods We performed a review of the actual positions assumed by clinicians during TMS treatments. Three different TMS coils (two circular and one figure‐of‐eight) were modeled and characterized numerically. Different orientations and positions of each coil with respect to the body of the operator were investigated to evaluate the induced electric (‐E) field in the body tissues. The collected data were processed to allow comparison with the safety standards for occupational exposure, as suggested by the International Commission on Non‐Ionizing Radiation Protection (ICNIRP) 2010 guidelines. Results Under the investigated conditions, exposure to TMS shows some criticalities for the operator performing the treatment. Depending on the model of the TMS coil and its relative position with respect to the operator's body, the numerically estimated E‐field could exceed the limits suggested by the ICNIRP 2010 guidelines. We established that the worst‐case scenario for the three coils occurs when they are placed in correspondence of the abdomen, with the handle oriented parallel to the body (II orientation). Working at a maximum TMS stimulator output (MSO), the induced E‐field is up to 7.32 V/m (circular coil) and up to 1.34 V/m (figure‐of‐eight coil). The induced E‐field can be modulated by the TMS percentage of MSO (%MSO) and by the distance between the source and the operator. At %MSO equal to or below 80%, the figure‐of‐eight coil was compliant with the ICNIRP limit (1.13 V/m). Conversely, the circular coil causes an induced E‐field above the limits, even when powered at a %MSO of 30%. Thus, in the investigated worst‐case conditions, an operator working with a circular coil should keep a distance from its edge to be compliant with the guidelines limit, which depends on the selected %MSO: 38 cm at 100%, 32 cm at 80%, 26.8 cm at 50%, and 19.8 cm at 30%. Furthermore, attention should be paid to the induced E‐field reached in the operator's hand as the operator typically holds the coil by hand. In fact in the hand, we estimated an induced E‐field up to 10 times higher than the limits. Conclusions Our numerical results indicate that coil positions, orientations, and distances with respect to the operator's body can determine the levels of induced E‐field that exceed the ICNIRP limits. The induced E‐field is also modulated by the choice of %MSO, which is related to the TMS application. Even under the best exposure conditions, attention should be paid to the exposure of the ha...

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Section: Methodsmentioning

confidence: 99%

Section: 2 Dosimetric Model For Clinician Exposure Assessmentmentioning

confidence: 99%

Systematic numerical assessment of occupational exposure to electromagnetic fields of transcranial magnetic stimulation

et al. 2022

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“…However, many behavioral manifestations of neurological and psychiatric disease are the result of alterations in distributed brain networks, and neuroimaging technology can be utilized to determine the optimal electrode placements for multichannel tDCS [247]. Computational models have also been built for tDCS [248] and TMS [249] to better estimate current flow patterns in the brain, to design new electrode montages, or to improve dosimetry. The simultaneous acquisition of neuronal and hemodynamic responses along with stimulation can provide an objective form of feedback to guide the stimulation procedure, quantify the stimulation effects, and investigate the underlying neural dynamics [246,[250][251][252].…”

Section: Outlook and Translational Applicationsmentioning

confidence: 99%

Multi-scale neural decoding and analysis

Lorenc

Zhu

et al. 2021

J. Neural Eng.

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Objective. Complex spatiotemporal neural activity encodes rich information related to behavior and cognition. Conventional research has focused on neural activity acquired using one of many different measurement modalities, each of which provides useful but incomplete assessment of the neural code. Multi-modal techniques can overcome tradeoffs in the spatial and temporal resolution of a single modality to reveal deeper and more comprehensive understanding of system-level neural mechanisms. Uncovering multi-scale dynamics is essential for a mechanistic understanding of brain function and for harnessing neuroscientific insights to develop more effective clinical treatment. Approach. We discuss conventional methodologies used for characterizing neural activity at different scales and review contemporary examples of how these approaches have been combined. Then we present our case for integrating activity across multiple scales to benefit from the combined strengths of each approach and elucidate a more holistic understanding of neural processes. Main results. We examine various combinations of neural activity at different scales and analytical techniques that can be used to integrate or illuminate information across scales, as well the technologies that enable such exciting studies. We conclude with challenges facing future multi-scale studies, and a discussion of the power and potential of these approaches. Significance. This roadmap will lead the readers toward a broad range of multi-scale neural decoding techniques and their benefits over single-modality analyses. This Review article highlights the importance of multi-scale analyses for systematically interrogating complex spatiotemporal mechanisms underlying cognition and behavior.

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“…Modeling of electric-fields (E-fields) is now the most widely used method to characterize the localization and spread of electrical current in the brain induced by TMS [18][19][20][21][22][23]. A variety of numerical computational methods have been developed to compute E-fields in conjunction with realistic head models by iteratively solving PDEs governing the E-field induced by TMS coils, including finite element methods (FEMs), boundary element methods (BEMs), and finite-difference methods (FDMs) [21,[24][25][26][27] [28].…”

Section: Introductionmentioning

confidence: 99%

Computation of transcranial magnetic stimulation electric fields using self-supervised deep learning

Li¹,

Deng

Oathes

et al. 2021

Preprint

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BackgroundElectric 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).MethodsWe 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.ResultsIn 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.ConclusionsWe 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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Review on biophysical modelling and simulation studies for transcranial magnetic stimulation

Cited by 32 publications

References 160 publications

Systematic numerical assessment of occupational exposure to electromagnetic fields of transcranial magnetic stimulation

Systematic numerical assessment of occupational exposure to electromagnetic fields of transcranial magnetic stimulation

Multi-scale neural decoding and analysis

Computation of transcranial magnetic stimulation electric fields using self-supervised deep learning

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