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Near real-time system for high-resolution computationalTMS navigation

Award Number: R01MH128421
ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 02/01/2022
PERIOD OF PERFORMANCE END DATE: 11/30/2026

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Near real-time system for high-resolution computationalTMS navigation - 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 neuronavigation accuracy with an anthropomorphic head phantom and evaluate our system in healthy volunteers and in volunteer neurosurgical patients that are scheduled for invasive direct cortical mapping that will be used as a gold standard.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $783,657 )
2026	2026	THE GENERAL HOSPITAL CORPORATION	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Mental Health Research Grants	000	5	12/11/2025	NON-COMPETING CONTINUATION	$783,657
														Subtotal = $783,657

Issue Date FY: 2025 ( Subtotal = $666,109 )
2025	2025	THE GENERAL HOSPITAL CORPORATION	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Mental Health Research Grants	000	4	12/13/2024	NON-COMPETING CONTINUATION	$666,109
														Subtotal = $666,109

Issue Date FY: 2024 ( Subtotal = $762,135 )
2024	2024	THE GENERAL HOSPITAL CORPORATION	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Mental Health Research Grants	001	3	8/15/2024	NON-COMPETING CONTINUATION	$76,213
2024	2024	THE GENERAL HOSPITAL CORPORATION	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Mental Health Research Grants	000	3	12/1/2023	NON-COMPETING CONTINUATION	$685,922
														Subtotal = $762,135

Issue Date FY: 2023 ( Subtotal = $768,508 )
2023	2023	THE GENERAL HOSPITAL CORPORATION	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Mental Health Research Grants	001	2	1/19/2023	NON-COMPETING CONTINUATION	$76,850
2023	2023	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Mental Health Research Grants	000	2	12/20/2022	NON-COMPETING CONTINUATION	$691,658
														Subtotal = $768,508

Issue Date FY: 2022 ( Subtotal = $790,364 )
2022	2022	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Mental Health Research Grants	000	1	1/31/2022	NEW	$713,027
2022	2022	MASSACHUSETTS GENERAL HOSPITAL, THE	55 FRUIT ST	BOSTON	MA	02114	SUFFOLK	USA	Mental Health Research Grants	001	1	6/27/2022	NEW	$77,337
														Subtotal = $790,364

Grand Total All Awards = $3,770,773

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Near real-time system for high-resolution computationalTMS navigation

Award Number: R01MH128421

ORGANIZATION: NATIONAL INSTITUTE OF MENTAL HEALTH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 02/01/2022

PERIOD OF PERFORMANCE END DATE: 11/30/2026

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