Near real-time system for high-resolution computationalTMS navigation

用于高分辨率计算 TMS 导航的近实时系统

基本信息

  • 批准号:
    10345482
  • 负责人:
  • 金额:
    $ 79.04万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-02-01 至 2026-11-30
  • 项目状态:
    未结题

项目摘要

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.
项目总结/文摘:

项目成果

期刊论文数量(0)
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会议论文数量(0)
专利数量(0)

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Aapo Nummenmaa其他文献

Aapo Nummenmaa的其他文献

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{{ truncateString('Aapo Nummenmaa', 18)}}的其他基金

CRCNS: Multifocal causal mapping of brain networks supporting human cognition
CRCNS:支持人类认知的大脑网络的多焦点因果图谱
  • 批准号:
    10612128
  • 财政年份:
    2022
  • 资助金额:
    $ 79.04万
  • 项目类别:
Near real-time system for high-resolution computationalTMS navigation
用于高分辨率计算 TMS 导航的近实时系统
  • 批准号:
    10558627
  • 财政年份:
    2022
  • 资助金额:
    $ 79.04万
  • 项目类别:
CRCNS: Multifocal causal mapping of brain networks supporting human cognition
CRCNS:支持人类认知的大脑网络的多焦点因果图谱
  • 批准号:
    10654871
  • 财政年份:
    2022
  • 资助金额:
    $ 79.04万
  • 项目类别:
Collaborative robot (cobot) controlled system for transcranial magnetic stimulation
协作机器人(cobot)控制的经颅磁刺激系统
  • 批准号:
    10177246
  • 财政年份:
    2021
  • 资助金额:
    $ 79.04万
  • 项目类别:
Modeling TMS-induced Cortical Network Activity
模拟 TMS 诱导的皮质网络活动
  • 批准号:
    9348648
  • 财政年份:
    2015
  • 资助金额:
    $ 79.04万
  • 项目类别:
Modeling TMS-induced Cortical Network Activity
模拟 TMS 诱导的皮质网络活动
  • 批准号:
    9137686
  • 财政年份:
    2015
  • 资助金额:
    $ 79.04万
  • 项目类别:
Modeling TMS-induced Cortical Network Activity
模拟 TMS 诱导的皮质网络活动
  • 批准号:
    8581251
  • 财政年份:
    2013
  • 资助金额:
    $ 79.04万
  • 项目类别:

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