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

Li, Hongming; Deng, Zhi‐De; Oathes, Desmond J.; Fan, Yong

doi:10.1016/j.neuroimage.2022.119705

Cited by 13 publications

(7 citation statements)

References 51 publications

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“…The DCNN model could predict induced electric fields accurately but the main drawback of this model is that it could predict induced electric fields for a fixed coil position parameter. At the same time, Hongming et al [18] developed a self-supervised deep-learning model which can accurately predict induced electric fields based on different coil positions of a single coil-type parameter. After-while, Guoping et al [16] also proposed a DNN model based on T1 weighted isotropic and anisotropic MRI images, different types of coil, different coil positions, and variation of rate change of current parameters.…”

Section: Related Workmentioning

confidence: 99%

“…The first step is to create a three-dimensional model of the human head through the MRI images. The creation of a three-dimensional human head model by segmenting MRI images takes about 8 to 10 hours [13][14][15][16][17][18]. After that, the electric field intensity is predicted using finite element analysis (FEA) of the volume conductor model (VCM).…”

Section: Introductionmentioning

confidence: 99%

“…Since the estimation of the electric field using the VCM requires a significant amount of computation time, an alternative deep learning based method can be employed to reduce it. Several works have recently been published that require very less computation time than conventional EM software by incorporating simulation data into deep learning architectures [13][14][15][16][17][18], [21]. However, in the reported works, several parameters including the distance of coil from the scalp, conductivity of the tissue [22][23][24], and the rate of current change [25], [26] are not taken into consideration as input parameters to predict the electric field through the deep learning-based model.…”

Section: Introductionmentioning

confidence: 99%

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ViTab Transformer Framework for Predicting Induced Electric Field and Focality in Transcranial Magnetic Stimulation

Ghosh,

Sathi,

Hosain

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Transcranial magnetic stimulation is an electromagnetic induction-based non-invasive therapeutic technique for neurological diseases. For finding new clinical applications and enhancing the efficacy of TMS in existing neurological disorders, the current study focuses on a deep learning-based prediction model as an alternative to time-consuming electromagnetic (EM) simulation software. The main bottleneck of the existing prediction models is to consider very few input parameters of a standard coil such as coil type and coil position for predicting an output of electric field value. To overcome this limitation, a transformer-based prediction model titled as ViTab transformer is developed in this work to predict electric field (E-max), focality or area of stmulation (S-half), and volume of stimulation (V-half) by considering several input parameters such as sources of MRI images, types of coils, coil position, rate of change of current, brain tissues conductivity, and coil distance from the scalp. The proposed framework consists of a vision and a tab transformer to handle both image and tabulartype data. The prediction performance of the offered model is evaluated in terms of coefficient determination, R 2 score, for E-max, V-half, and S-half in the testing phase. The obtained result in terms of R 2 score for E-max, V-half, and S-half are found 0.97, 0.87, and 0.90 respectively. The results indicate that the suggested ViTab transformer model can predict electric field as well as focality more accurately than the current state-of-the-art methods. The reduced computational time, as well as efficient prediction accuracy, resembles that ViTab transformer can assist the neuroscientist and neurosurgeon prior to providing superior TMS treatment in near future.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

ViTab Transformer Framework for Predicting Induced Electric Field and Focality in Transcranial Magnetic Stimulation

Ghosh,

Sathi,

Hosain

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Recent research indicates that deep learning methods have the potential to simulate the E-field statistically [26][27][28][29]. Using individual MRI as input, deep neural networks (DNNs) can produce corresponding E-field distributions in real time, leveraging their efficient extraction of implicit tissue features and sidestepping the dimensional curse encountered in earlier machine learning methods [30,31].…”

Section: Introductionmentioning

confidence: 99%

In-vivo verified anatomically aware deep learning for real-time electric field simulation

Ma,

Zhong,

Yang

et al. 2023

J. Neural Eng.

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Objective. Transcranial magnetic stimulation (TMS) has emerged as a prominent non-invasive technique for modulating brain function and treating mental disorders. By generating a high-precision magnetically evoked electric field (E-field) using a TMS coil, it enables targeted stimulation of specific brain regions. However, current computational methods employed for E-field simulations necessitate extensive preprocessing and simulation time, limiting their fast applications in the determining the optimal coil placement. Approach. We present an attentional deep learning network to simulate E-fields. This network takes individual magnetic resonance images and coil configurations as inputs, firstly transforming the images into explicit brain tissues and subsequently generating the local E-field distribution near the target brain region. Main results. Relative to the previous deep-learning simulation method, the presented method reduced the mean relative error in simulated E-field strength of gray matter by 21.1%, and increased the correlation between regional E-field strengths and corresponding electrophysiological responses by 35.0% when applied into another dataset. In-vivo TMS experiments further revealed that the optimal coil placements derived from presented method exhibit comparable stimulation performance on motor evoked potentials to those obtained using computational methods. The simplified preprocessing and increased simulation efficiency result in a significant reduction in the overall time cost of traditional TMS coil placement optimization, from several hours to mere minutes. Significance. The precision and efficiency of presented simulation method hold promise for its application in determining individualized coil placements in clinical practice, paving the way for personalized TMS treatments.

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“…This fact has brought forward questions such as: how much detail of the coil geometry is necessary to estimate the actual electric field induced by the real coil [ 58 ]? The development of new numerical methods such as neural networks [ 59 ] has also been motivated in order to increase the computation speed of TMS fields. Nevertheless, the most common approach has been the use of specific formulations of quasistatic approximation of Maxwell equations depending on the TMS applications, which is likely to remain the dominant approach for future TMS numerical computation, which, due to the microscopic size of neurons and nerves, requires increasing resolution and precision.…”

Section: Introductionmentioning

confidence: 99%

A Review of Formulations, Boundary Value Problems and Solutions for Numerical Computation of Transcranial Magnetic Stimulation Fields

Pérez-Benítez,

Martínez-Ortiz,

Aguila-Muñoz

2023

Brain Sciences

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Since the inception of the transcranial magnetic stimulation (TMS) technique, it has become imperative to numerically compute the distribution of the electric field induced in the brain. Various models of the coil-brain system have been proposed for this purpose. These models yield a set of formulations and boundary conditions that can be employed to calculate the induced electric field. However, the literature on TMS simulation presents several of these formulations, leading to potential confusion regarding the interpretation and contribution of each source of electric field. The present study undertakes an extensive compilation of widely utilized formulations, boundary value problems and numerical solutions employed in TMS fields simulations, analyzing the advantages and disadvantages associated with each used formulation and numerical method. Additionally, it explores the implementation strategies employed for their numerical computation. Furthermore, this work provides numerical expressions that can be utilized for the numerical computation of TMS fields using the finite difference and finite element methods. Notably, some of these expressions are deduced within the present study. Finally, an overview of some of the most significant results obtained from numerical computation of TMS fields is presented. The aim of this work is to serve as a guide for future research endeavors concerning the numerical simulation of TMS.

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

Cited by 13 publications

References 51 publications

ViTab Transformer Framework for Predicting Induced Electric Field and Focality in Transcranial Magnetic Stimulation

ViTab Transformer Framework for Predicting Induced Electric Field and Focality in Transcranial Magnetic Stimulation

In-vivo verified anatomically aware deep learning for real-time electric field simulation

A Review of Formulations, Boundary Value Problems and Solutions for Numerical Computation of Transcranial Magnetic Stimulation Fields

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