# Near real-time system for high-resolution computationalTMS navigation > **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2022 · $790,364 ## Abstract Project Summary/Abstract: Transcranial Magnetic Stimulation (TMS) is a widely used technology for non-invasive modulation of human brain activity. TMS induces electric fields (E-fields) in the intracranial tissue by means of time-varying magnetic fields (electromagnetic induction) that results in the possibility of obtaining suprathreshold stimulation intensities safely and with little discomfort to the subjects. Clinical applications of TMS include major depressive disorder (MDD) and treatment resistant depression (TRD) in which repetitive TMS (rTMS) is administered to Dorsolateral Prefrontal Cortex (DLPFC) with well-demonstrated efficacy. Both in clinical and basic neuroscience research applications, it is important that the stimulation is accurately targeted to the desired region(s). The E-field distribution that is induced by the TMS pulse in the intracranial tissue is the key physical quantity that can be used to delineate which areas are stimulated and which are not. This is especially important for non-motor regions such as the DLPFC because a direct peripheral measure (e.g., electromyographic response) cannot be used to guide the stimulation. To date, computationally estimated E- field distributions have been used in “online” commercial neuronavigation systems to guide the TMS coil positioning, but the currently available systems offer only spherically symmetric head models that cannot properly take into account the individual differences in tissue geometries and may result in substantial targeting and dosing errors. On the other hand, the most accurate computational methods for E-field estimation are too slow to enable near real time operation. Therefore, no technique exists that has the computational efficiency to enable neuronavigation applications while at the same time incorporating high level of anatomical detail and numerical accuracy. To remove this efficiency vs. accuracy dilemma that is currently posing a critical barrier for development of more quantitative TMS approaches, we propose to use our recently developed Boundary Element Method (BEM) based computational strategy accelerated by the Fast Multipole Method (FMM) that is suitable for both online and offline application scenarios. Our approach starts with developing an automatic segmentation and surface generation pipeline to obtain accurate representations of the tissue conductivity boundaries using individual MRI data. We will subsequently develop and experimental TMS neuronavigation system that utilizes the fast BEM-FMM method. The purpose of this system is to render the E-field distributions on top of the 3D brain anatomy and to guide the operator to position the TMS coil and associated E-field “hot spot” to the desired location. We will interface the computational engine with a commercial TMS navigator to demonstrate translational potential for clinical research and ultimately to therapeutic/clinical applications. Finally, we will validate the computational neuronavigat... ## Key facts - **NIH application ID:** 10345482 - **Project number:** 1R01MH128421-01 - **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL - **Principal Investigator:** Aapo Nummenmaa - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $790,364 - **Award type:** 1 - **Project period:** 2022-02-01 → 2026-11-30 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10345482 ## Citation > US National Institutes of Health, RePORTER application 10345482, Near real-time system for high-resolution computationalTMS navigation (1R01MH128421-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10345482. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*