TY - JOUR T1 - Prediction of Electric Fields Induced by Transcranial Magnetic Stimulation in the Brain using a Deep Encoder-Decoder Convolutional Neural Network JF - bioRxiv DO - 10.1101/2022.10.27.513583 SP - 2022.10.27.513583 AU - Mohannad Tashli AU - Muhammad Sabbir Alam AU - Jiaying Gong AU - Connor Lewis AU - Carrie L. Peterson AU - Hoda Eldardiry AU - Jayasimha Atulasimha AU - Ravi L. Hadimani Y1 - 2022/01/01 UR - http://biorxiv.org/content/early/2022/10/28/2022.10.27.513583.abstract N2 - 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.Competing Interest StatementDr. Hadimani has one granted patent on a TMS coil (US10792508B2), one granted patent on brain phantoms for neuromodulation (US11373552B2), two patents published and pending on TMS coils US20170120065A1 and US20220241605A1 and one patent pending on transcranial magnetic stimulator (63/334,767) ER -