Redesigning existing transcranial magnetic stimulation coils to reduce energy: application to low field magnetic stimulation

Wang, Boshuo; Shen, Michael; Deng, Zhi‐De; Smith, John; Tharayil, Joseph; Gurrey, Clement J; Gomez, Luis J.; Peterchev, Angel V.

doi:10.1088/1741-2552/aaa505

Cited by 44 publications

(42 citation statements)

References 23 publications

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“…For example, the standard Magstim 70mm Double coil consists of a pair of 70 mm diameter, nineturn circular spiral windings of wire with cross-section of 1.9 mm by 6 mm. Because of their small cross-section most TMS modeling frameworks assume that the currents flow uniformly through the wire [11][12][13]. However, the eddy currents cause the currents to redistribute and concentrate toward the wire surface [14].…”

Section: Introductionmentioning

confidence: 99%

Conditions for numerically accurate TMS electric field simulation

Gomez

Dannhauer

Koponen

et al. 2018

Preprint

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HIGHLIGHTS FDM and 1st order FEM with 1.5 mm average mesh edge length have numerical errors above 7%. BEM or 2nd order FEM are most efficient for achieving numerical errors < 2%.  Coil wire cross-section must be accounted to achieve E-field errors below < 2%. Coil eddy currents can account for > 2% of E-field when very brief pulses are used. ABSTRACTBackground: Computational simulations of the E-field induced by transcranial magnetic stimulation (TMS) are increasingly used to understand its mechanisms and to inform its administration. However, characterization of the accuracy of the simulation methods and the factors that affect it is lacking.Objective: To ensure the accuracy of TMS E-field simulations, we systematically quantify their numerical error and provide guidelines for their setup. Method:We benchmark the accuracy of computational approaches that are commonly used for TMS E-field simulations, including the finite element method (FEM), boundary element method (BEM), finite difference method (FDM), and coil modeling methods. Results:To achieve cortical E-field error levels below 2%, the commonly used FDM and 1 st order FEM require meshes with an average edge length below 0.4 mm, whereas BEM and 2 nd (or higher) order FEM require edge lengths below 1.5 mm, which is more practical. Coil models employing magnetic and current dipoles require at least 200 and 3,000 dipoles, respectively. For thick solid-conductor coils and frequencies above 3 kHz, winding eddy currents may have to be modeled.Conclusion: BEM, FDM, and FEM methods converge to the same solution. However, FDM and 1 st order FEM converge slowly with increasing mesh resolution; therefore, the use of BEM or 2 nd (or higher) order FEM is recommended. In some cases, coil eddy currents must be modeled. Both electric current dipole and magnetic dipole models of the coil current can be accurate with sufficiently fine discretization.

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Section: Introductionmentioning

confidence: 99%

Conditions for numerically accurate TMS electric field simulation

Gomez

Dannhauer

Koponen

et al. 2018

Preprint

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“…Using functional neuroimaging to understand the circuit abnormalities that are predictive of treatment response has shown great promise for TMS [32][33][34][35][36][37][38]. Mapping these abnormalities in LFMS responders could eventually help match the treatment to depressed patients most likely to benefit and will guide advancements in LFMS coil design to optimize treatment [39,40].…”

Section: Discussionmentioning

confidence: 99%

A Double-Blind Pilot Dosing Study of Low Field Magnetic Stimulation (LFMS) for Treatment-Resistant Depression (TRD)

Dubin

Ilieva

Deng³

et al. 2018

Preprint

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and I. Ilieva contributed equally to this work. AbstractLow Field Magnetic Stimulation is a potentially rapid-acting treatment for depression with moodenhancing effects in as little as one 20-minute session. The most convincing data for LFMS has come from treating bipolar depression. We examined whether LFMS also has rapid mood-enhancing effects in treatment-resistant major depressive disorder, and whether these effects are dose-dependent. We hypothesized that a single 20-min session of LFMS would reduce depressive symptom severity and that the magnitude of this change would be greater after three 20-min sessions than after a single 20min session. In a double-blind randomized controlled trial, 30 participants (age 21-65) with treatmentresistant depression were randomized to three 20-minute active or sham LFMS treatments with 48 hours between treatments. Response was assessed immediately following LFMS treatment using the 6-item Hamilton Depression Rating Scale (HAMD-6), the Positive and Negative Affect Scale (PANAS) and the Visual Analog Scale. Following the third session of LFMS, the effect of LFMS on VAS and HAMD-6 was superior to sham (F (1, 24) = 7.45, p = 0.03, Holm-Bonferroni corrected; F (1, 22) = 6.92, p = 0.03, Holm-Bonferroni corrected, respectively). There were no differences between sham and LFMS following the initial or second session with the effect not becoming significant until after the third session. Three 20-minute LFMS sessions were required for active LFMS to have a mood-enhancing effect for individuals with treatment-resistant depression. As this effect may be transient, future work should address dosing schedules of longer treatment course as well as biomarker-based targeting of LFMS to optimize patient selection and treatment outcomes.

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“…The BEM-FMM approach has demonstrated faster computational speed (10-1000 times faster) and superior accuracy for high-resolution head models as compared to both the standard boundary element method and the finite element method used in SimNIBS or commercial FEM software ANSYS Maxwell 3D (Htet et al, 2019). These advantages of the BEM-FMM approach have been recently independently and thoroughly confirmed (Gomez et al, 2018).…”

Section: Introductionmentioning

confidence: 94%

Software Toolkit for Fast High-Resolution TMS Modeling

Makarov

Noetscher

Burnham

et al. 2019

Preprint

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Computational modeling of Transcranial Magnetic Stimulation (TMS) is a tradeoff between computational speed vs. spatial precision. In this study, we introduce a software toolkit for highresolution TMS modeling, which may offer the best of both. The toolkit employs the recently developed boundary element fast multipole method (BEM-FMM) with accurate solution computations close to tissue interfaces. It operates within the MATLAB platform and is designed for a broad, medically-oriented computational research community. To enable easy, subjectspecific operation, the package is compatible with human head models generated with automated tools such as SimNIBS and FreeSurfer.Both coil design in free space as well as efficacy and focality of TMS for a specific subject could be modeled by the package, including optional user-defined parametric loops. Detailed and widely used coil models, generated in both CAD and wire formats, may include several hundred thousand elementary current elements and observation spaces with approximately 1 M field points; the corresponding computational times are on the order of 1 sec. Detailed head models with approximately 1 M triangular facets and a mesh resolution of 0.6 points per mm 2 are simulated in approximately 1.5 min which is arguably the fastest time to date. Further reduction of computational times is foreseen. The toolkit is augmented with a population of 16 ready-to-use head models for performing simulations for computational studies that do not involve MRI data collection. The toolkit is also augmented with a coil geometry generator capable of creating accurate coil models.

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Redesigning existing transcranial magnetic stimulation coils to reduce energy: application to low field magnetic stimulation

Cited by 44 publications

References 23 publications

Conditions for numerically accurate TMS electric field simulation

Conditions for numerically accurate TMS electric field simulation

A Double-Blind Pilot Dosing Study of Low Field Magnetic Stimulation (LFMS) for Treatment-Resistant Depression (TRD)

Software Toolkit for Fast High-Resolution TMS Modeling

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