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
- 来源:
- 关键词:3-DimensionalAnatomyApplications GrantsAreaBenchmarkingBoundary ElementsBrainClinicalClinical ResearchComputing MethodologiesDataDevelopmentDoseElectromagneticsFunctional Magnetic Resonance ImagingGenerationsGeometryGoalsGoldHeadHot SpotHumanHuman VolunteersIndividualIndividual DifferencesLocationMagnetic Resonance ImagingMajor Depressive DisorderManufacturer NameMeasuresMemoryMethodsModelingMotorNational Institute of Mental HealthNeuronavigationNeurosciences ResearchOperative Surgical ProceduresOpticsPatient SchedulesPatientsPerformancePeripheralPhysiologic pulsePositioning AttributePrefrontal CortexPublishingReal-Time SystemsResearchResolutionScalp structureSkinSourceSpeedSurfaceSystemTechniquesTechnologyTestingTherapeuticTimeTissuesTranscranial magnetic stimulationautomated segmentationbaseclinical applicationclinical effectcommercial applicationcortex mappingcraniumelectric fieldgray matterhealthy volunteerimprovedinnovationmagnetic fieldnoveloperationphysical modelprototyperepetitive transcranial magnetic stimulationresponsetranslational potentialtreatment-resistant depressionvolunteerwhite matter
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)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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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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