Campus Units

Electrical and Computer Engineering, Materials Science and Engineering, Ames Laboratory

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

6-7-2021

Journal or Book Title

IEEE Transactions on Magnetics

DOI

10.1109/TMAG.2021.3086761

Abstract

Transcranial Magnetic Stimulation (TMS) is a non-invasive neuromodulation technique with applications in brain mapping and effective neuronal connectivity. In its repetitive mode, it is used for the treatment of neurological and psychiatric disorders. It functions on the principle of electromagnetic induction, where generated magnetic fields (H-field) induce electric field (E-field) that stimulates the brain’s neurons. With TMS studies, accurate estimation of the induced E-field is usually necessary. However, this requires a lot of processes, including the three-dimensional head model generation from magnetic resonance images (MRI) using the SimNIBS software and finite element analysis to calculate the induced E-field. These processes are time-consuming and computationally expensive. Additionally, with each head model’s uniqueness, outcomes cannot be generalized across a particular population as the intensity of E-field is subject-specific. In this research, the authors develop deep convolutional neural network (deep CNN) models to determine the intensity of the induced E-field directly from the patient’s MRI scan and across different coil types. We trained CNN models from anatomically realistic head models and across 16 coil types to predict the induced E-field in the brain and scalp (E-Max brain and scalp), and the volume of stimulation of the brain and scalp (V-half brain and scalp) from T1-weighted MRI scans. Using a deep CNN model, the processing time for estimating the induced E-field is significantly reduced, which is helpful both to clinicians and researchers as the need to create subject-specific anatomical head structures is eliminated. Also, there will be no need for additional stimulation sessions with the different coil types for TMS patients as the deep CNN model can predict the outcome from each coil type. The other advantage of the deep CNN model is that the E-field from the different coil types can be compared simultaneously.

Comments

This is a manuscript of an article published as Afuwape, Oluwaponmile F., Olumide O. Olafasakin, and David C. Jiles. "Neural Network Model for Estimation of the Induced Electric Field during Transcranial Magnetic Stimulation." IEEE Transactions on Magnetics (2021). DOI: 10.1109/TMAG.2021.3086761. Posted with permission.

Rights

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Copyright Owner

IEEE

Language

en

File Format

application/pdf

Published Version

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