Fast computational optimization of TMS coil placement for individualized electric field targeting

Gomez, Luis J.; Dannhauer, Moritz; Peterchev, Angel V.

doi:10.1016/j.neuroimage.2020.117696

Cited by 83 publications

(81 citation statements)

References 55 publications

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“…Moreover, the prediction speed is still slower than the fast quadrature method [22] and the DNN based method [21] though the predicted E-field by these methods is only in a smaller region of the brain. More recently, a fast computational algorithm was introduced in [45] to estimate Efield in a selected ROI so that the E-fields generated by coils placed at 5900 different scalp positions and 360 orientation per position can be computed under 15 minutes. In [46], a rapid algorithm was introduced to compute E-field in~100 ms on a cortical surface mesh with 120k facets and with about 5 hours of preparation time.…”

Section: On Prediction Speedmentioning

confidence: 99%

Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning

Rathi

Camprodon

et al. 2021

PLoS ONE

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Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulation technique that is increasingly used in the treatment of neuropsychiatric disorders and neuroscience research. Due to the complex structure of the brain and the electrical conductivity variation across subjects, identification of subject-specific brain regions for TMS is important to improve the treatment efficacy and understand the mechanism of treatment response. Numerical computations have been used to estimate the stimulated electric field (E-field) by TMS in brain tissue. But the relative long computation time limits the application of this approach. In this paper, we propose a deep-neural-network based approach to expedite the estimation of whole-brain E-field by using a neural network architecture, named 3D-MSResUnet and multimodal imaging data. The 3D-MSResUnet network integrates the 3D U-net architecture, residual modules and a mechanism to combine multi-scale feature maps. It is trained using a large dataset with finite element method (FEM) based E-field and diffusion magnetic resonance imaging (MRI) based anisotropic volume conductivity or anatomical images. The performance of 3D-MSResUnet is evaluated using several evaluation metrics and different combinations of imaging modalities and coils. The experimental results show that the output E-field of 3D-MSResUnet provides reliable estimation of the E-field estimated by the state-of-the-art FEM method with significant reduction in prediction time to about 0.24 second. Thus, this study demonstrates that neural networks are potentially useful tools to accelerate the prediction of E-field for TMS targeting.

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Section: On Prediction Speedmentioning

confidence: 99%

Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning

Rathi

Camprodon

et al. 2021

PLoS ONE

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“…Powerful mathematical tools have recently been developed and implemented ( Gomez et al, 2021 , Daneshzand et al, 2021 ) for fast computations of the TMS-IP solutions via the auxiliary dipole method (ADM) or the magnetic stimulation profile approach, for determining the optimum coil position and orientation. The goal of the present study is not to compete with these tools but rather to evaluate the usefulness and degree of improvement of the TMS-IP solution itself.…”

Section: Discussionmentioning

confidence: 99%

“…From the practical point of view, the solution of a particular TMS-IP will likely be best accomplished by using specialized highly efficient algorithms such as ( Gomez et al, 2021 , Daneshzand et al, 2021 ) instead of the straightforward yet slow approach of this study.…”

Section: Discussionmentioning

confidence: 99%

Degree of improving TMS focality through a geometrically stable solution of an inverse TMS problem

et al. 2021

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The Transcranial Magnetic Stimulation (TMS) inverse problem (TMS-IP) investigated in this study aims to focus the TMS induced electric field close to a specified target point defined on the gray matter interface in the M 1 HAND area while otherwise minimizing it. The goal of the study is to numerically evaluate the degree of improvement of the TMS-IP solutions relative to the well-known sulcus-aligned mapping (a projection approach with the 90° local sulcal angle). In total, 1536 individual TMS-IP solutions have been analyzed for multiple target points and multiple subjects using the boundary element fast multipole method (BEM-FMM) as the forward solver. Our results show that the optimal TMS inverse-problem solutions improve the focality – reduce the size of the field “hot spot ” and its deviation from the target – by approximately 21–33% on average for all considered subjects, all observation points, two distinct coil types, two segmentation types, two intracortical observation surfaces under study, and three tested values of the field threshold. The inverse-problem solutions with the maximized focality simultaneously improve the TMS mapping resolution (differentiation between neighbor targets separated by approximately 10 mm) although this improvement is quite modest. Coil position/orientation and conductivity uncertainties have been included into consideration as the corresponding de-focalization factors. The present results will change when the levels of uncertainties change. Our results also indicate that the accuracy of the head segmentation critically influences the expected TMS-IP performance.

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“…This provides insight into structure-function relationships at both group and individual level (e.g. [1,2]). However, the precise cortical location at which an induced electric field initiates the behavioral effect remains unclear.…”

Section: Introductionmentioning

confidence: 99%

“…However, the precise cortical location at which an induced electric field initiates the behavioral effect remains unclear. Estimating the induced fields is key to address this [1,2] but complex field distributions and interindividual variations hamper precise cortical localization. For example, the regions which are initially activated for TMS-elicited motor evoked potentials (MEP) are still debated.…”

Section: Introductionmentioning

confidence: 99%

Efficient high-resolution TMS mapping of the human motor cortex by nonlinear regression

Numssen

Zier

Thielscher

et al. 2021

Preprint

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Background: The precise cortical origins of the electrophysiological and behavioral effects of transcranial magnetic stimulation (TMS) remain largely unclear. Addressing this question is further impeded by substantial inter-individual response variability to TMS. Objective: We present a novel method to reliably and user-independently determine the effectively stimulated cortical site at the individual subject level. This generic approach combines physiological measurements with electric field simulations and leverages information from random coil positions, electric field estimations, and electromyography. Methods: We applied ~1000 single biphasic TMS pulses with standard TMS hardware to 13 subjects with random coil positions & orientations over the primary motor hand area. Motor evoked potentials (MEPs) of three finger muscles were recorded concurrently. We calculated the corresponding electric fields for all TMS pulses and regressed them against the elicited MEPs in each cortical element. This yields a cortical map of congruency between induced field strength and generated response. Results: We observed high congruence between the electric fields and the elicited MEPs in hotspots located primarily on the crowns and rims of the precentral gyrus. The three cortical digit representations could be distinguished at the individual subject level with a high spatial resolution. A post-hoc convergence analysis revealed a possible lower bound of only 180 pulses to obtain qualitatively identical results. Conclusions: Leveraging information from many different TMS pulses significantly reduces the number of necessary stimulations and mapping time. The protocol is easy to implement due to the realization of arbitrary coil positions & orientations and is suitable for practical and clinical use such as preoperative mapping.

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Fast computational optimization of TMS coil placement for individualized electric field targeting

Cited by 83 publications

References 55 publications

Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning

Rapid whole-brain electric field mapping in transcranial magnetic stimulation using deep learning

Degree of improving TMS focality through a geometrically stable solution of an inverse TMS problem

Efficient high-resolution TMS mapping of the human motor cortex by nonlinear regression

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