Design of TMS coils with reduced Lorentz forces: application to concurrent TMS-fMRI

Abstract

Objective. Interleaving TMS (transcranial magnetic stimulation) with fMRI (functional Magnetic Resonance Imaging) is a promising technique to study functional connectivity in the human brain, but its development is being restricted by technical limitations, such as that due to the interaction of the TMS current pulses with the magnetic fields of an MRI scanner. In this work, a TMS coil design method capable of controlling Lorentz forces experienced by the coil in the presence of static magnetic fields is presented. Approach. The suggested approach is based on an existing inverse boundary element method (IBEM) for TMS coil design, in which new electromagnetic computational models of the Lorentz forces have been included to be controlled in the design process. Main results. To demonstrate the validity of this technique, it has been used for the design and simulation of TMS coils wound on rectangular flat, spherical and hemispherical surfaces with improved mechanical stability. The obtained results confirm that TMS coils with reduced Lorentz forces inside the static main field of an MRI scanner can be produced, which is achieved to the detriment of other coil performance parameters. Significance. The proposed approach provides an efficient tool to design TMS stimulators of a wide range of coil geometries with improved mechanical stability, which can be extremely useful to overcome current limitations for interleaved TMS-fMRI.

TMS Does Not Increase BOLD Activity at the Site of Stimulation: A Review of All Concurrent TMS-fMRI Studies

“…To do so, we started with the 69 studies reported by Bergmann et al (2021) and then, using equivalent search criteria, looked for additional studies published in 2020 and 2021, which resulted in five additional studies (Cobos Sánchez et al, 2020;Navarro de Lara et al, 2020;Jackson et al, 2021;Rafiei et al, 2021;Scrivener et al, 2021). Second, we excluded two sets of articles: (1) papers that primarily focused on methodological issues and only reported data from a single subject (Bestmann et al, 2003a(Bestmann et al, , 2006Peters et al, 2013;Navarro de Lara et al, 2015;Oh et al, 2019), and (2) papers that did not provide information that allows the determination of whether or not TMS led to an increase in local BOLD activation because relevant analyses were not reported (Ruff et al, 2006(Ruff et al, , 2008Guller et al, 2012;Chen et al, 2013;Hanlon et al, 2016;Fonzo et al, 2017;Cobos Sánchez et al, 2020;Eshel et al, 2020;Hermiller et al, 2020;Navarro de Lara et al, 2020;Jackson et al, 2021;Oathes et al, 2021;Scrivener et al, 2021). Among the remaining studies, two pairs of studies shared the same underlying dataset (Leitão et al, 2013and Shitara et al, 2011 and, therefore, we kept only the ones that explicitly mention results related to the presence or absence of activation at the site of stimulation (Shitara et al, 2011;Leitão et al, 2015).…”

Devices and Technology in Transcranial Magnetic Stimulation: A Systematic Review

“…Regarding optimization, other parameters have improved the field intensity and focality. The coils could be symmetrically designed (concentric), with the turns equally spaced from the center, or spaced asymmetrically, with its center displaced from the circumference (eccentric), or with variations in the winding shape to reduce the Lorentz forces [ 59 ]. Applying this variable to the FoE coil, it is possible to obtain significant differences among measurements of the two mentioned configurations [ 104 ], the latter being more efficient.…”

“…Publications about modeling and optimization in TMS can be grossly divided into three main branches, (1) modeling with the finite element method (FEM) [ 43 , 52 , 82 , 92 , 98 , 99 , 102 , 115 , 131 , 132 ], (2) modeling with the boundary element method (BEM) [ 133 ], and (3) modeling using the finite differences method (FDM) [ 42 , 58 ]. Adaptations of these procedures for fast computing and optimization, such as the improvement of BEM with the fast multipole method (BEM-FMM) [ 38 , 55 ] or the use of the inverse boundary element method for coil calculation [ 40 , 59 , 134 ], were presented. Algorithms adopting alternate programming languages for field optimization [ 42 , 56 ], fast field calculation with neural networks [ 63 ], or other analytical methods [ 107 , 135 ] are scarcely present.…”

Implementation of a TMS-fMRI system: A primer

“…One of these challenges is that the MRI scanner generates a stable and homogenous magnetic field, while the TMS apparatus uses an unstable and non-constant magnetic field. Therefore, in order to achieve proper concurrent TMS-fMRI, specialized equipment (e.g., MR-compatible TMS machine, and customized TMS and MR coils for concurrent TMS-fMRI) (Bohning et al, 2003b; Moisa et al, 2008; Navarro De Lara et al, 2015; Cobos Sánchez et al, 2020) and synchronization between MR sequences and TMS pulses (Bohning et al, 1999; Jung et al, 2016) is required. Some of the other technical challenges include static and dynamic artifacts.…”