Prediction of Electric Fields Induced by Transcranial Magnetic Stimulation in the Brain using a Deep Encoder-Decoder Convolutional Neural Network

Abstract

Transcranial magnetic stimulation (TMS) is a non-invasive, effective, and safe neuromodulation technique to diagnose and treat neurological and psychiatric disorders. However, the complexity and heterogeneity of the brain composition and structure pose a challenge in accurately determining whether critical brain regions have received the right level of induced electric field. Numerical computation methods, like finite element analysis (FEA), can be used to estimate electric field distribution. However, these methods need exceedingly high computational resources and are time-consuming. In this work, we developed a deep convolutional neural network (DCNN) encoder-decoder model to predict induced electric fields, in real-time, from T1-weighted and T2-weighted magnetic resonance imaging (MRI) based anatomical slices. We recruited 11 healthy subjects and applied TMS to the primary motor cortex to measure resting motor thresholds. Head models were developed from MRIs of the subjects using the SimNIBS pipeline. Head model overall size was scaled to 20 new size scales for each subject to form a total of 231 head models. Scaling was done to increase the number of input data representing different head model sizes. Sim4Life, a FEA software, was used to compute the induced electric fields, which served as the DCNN training data. For the trained network, the peak signal to noise ratios of the training and testing data were 32.83dB and 28.01dB, respectively. The key contribution of our model is the ability to predict the induced electric fields in real-time and thereby accurately and efficiently predict the TMS strength needed in targeted brain regions.

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

“…This analysis also ignores crucial output factors such as focality and V-half for prediction. In the meantime, Tashil et al [17] proposed a deep convention neural network (DCNN) to predict induced electric fields from T1 and T2 weighted MRI of 11 healthy person. 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.…”

“…Most of the earlier deep learning works on TMS [13][14][15][16][17][18] have focused on the prediction of the electric field using the input parameters of coil type, coil position, and MRI image. However, these existing works ignore vital factors such as the conductivities of the brain tissues, the distance of the coil from the skin, and the rate of current change.…”

“…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).…”

“…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.…”