Modeling TMS-induced Cortical Network Activity

模拟 TMS 诱导的皮质网络活动

• 批准号: 9348648
• 负责人: Aapo Nummenmaa
• 金额: $ 24.9万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-04 至 2020-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9348648
• 关键词: Anatomy; Animals; Area; Boundary Elements; Brain; Brain region; Career Choice; Clinical; Complex; Computational Technique; Computer software; Computing Methodologies; Consultations; Data; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Distant; Education; Electroencephalography; Electromagnetic Fields; Electromyography; Electrophysiology (science); Elements; Emerging Technologies; Engineering; Evaluation; FDA approved; Functional Magnetic Resonance Imaging; Goals; Grant; Human; Image; Institution; Investigation; Knowledge; Length; Link; Location; Magnetic Resonance Imaging; Magnetoencephalography; Mathematics; Measurement; Measures; Mental Depression; Mentors; Methodology; Methods; Modeling; Motor; Motor Cortex; Multimodal Imaging; Muscle; Neurosciences; Outcome; Pathway interactions; Performance; Phase; Physics; Physiological; Positioning Attribute; Research; Research Personnel; Resolution; Secondary to; Side; Source; System; Techniques; Testing; Therapeutic; Time; Training; Transcranial magnetic stimulation; Validation; Work; base; bioimaging; career; career development; clinical application; data acquisition; electric field; experimental study; improved; in vivo; instrumentation; millisecond; multidisciplinary; neurophysiology; non-invasive imaging; novel; operation; predictive modeling; public health relevance; relating to nervous system; skills; temporal measurement; tool; tractography

DESCRIPTION (provided by applicant): This project aims at developing novel methods for predicting the large-scale cortical network that is activated by spatially focused transcranial magnetic stimulation (TMS). Presently there is no integrated framework that would allow predicting how the activation will spread from the primary target area to secondary locations. The proposal will combine accurate and efficient electromagnetic field computation methods for calculating the primary activations with data from diffusion weighted magnetic resonance imaging (MRI) tractography to predict the effects of TMS on an anatomically connected cortical network. We will evaluate the modeling framework by measuring the effects of TMS with simultaneous electroencephalography EEG (TMS-EEG) and functional MRI (TMS-fMRI) in humans, and also using TMS adapted to small animals. During the mentored phase, I will utilize my training in theoretical physics, applied mathematics, and computational engineering to build a set of tools to model the TMS-induced network activity. Anatomical MRI data acquisitions and functional TMS-EEG and TMS-fMRI measurements will be carried out for verifying the proposed model. During the second phase, the software will be optimized for real-time operation and more extensive experiments for evaluation of the system performance and prediction accuracy will be carried out. This project optimally fits my long-term career goal of becoming a multi-disciplinary researcher capable of both methodological development and carrying out neuroscientific studies combining TMS with non-invasive imaging. The outcome of the proposed project will be an entirely novel framework for understanding the effects of TMS on cortical networks for scientific studies and therapeutic applications. The grant will allow me to reach the immediate goal of strengthening my skills in the experimental side of imaging neuroscience and broadening my knowledge of the neurophysiology of TMS. The mentored phase will be carried out at MGH-Harvard-MIT Martinos Center for Biomedical Imaging. The affiliated institutions offer first-rate education pertinent to my career development. Specifically, in addition to building my experimental expertise, I will audit courses on neural engineering and complex networks at Harvard and MIT. The collaborative effort for validating the models with a small animal TMS experiment will also provide hands-on training in electrophysiology. The Martinos Center has cutting edge imaging facilities for carrying out the experimental work and world-class experts available for mentoring and consultation.

描述（由申请人提供）：该项目旨在开发新的方法来预测由空间聚焦经颅磁刺激（TMS）激活的大规模皮层网络。目前还没有一个综合框架，可以预测激活将如何从主要目标区域蔓延到次要位置。该提案将结合联合收割机精确和有效的电磁场计算方法，用于计算初级激活与扩散加权磁共振成像（MRI）纤维束成像的数据，以预测TMS对解剖学连接的皮层网络的影响。我们将评估建模框架，通过测量TMS与同步脑电图脑电图（TMS-EEG）和功能性MRI（TMS-fMRI）在人类中的影响，也使用TMS适应于小动物。在指导阶段，我将利用我在理论物理，应用数学和计算工程方面的培训来构建一套工具来模拟TMS诱导的网络活动。解剖MRI数据采集和功能TMS-EEG和TMS-fMRI测量将进行验证所提出的模型。在第二阶段，将对软件进行实时操作优化，并将进行更广泛的实验，以评估系统性能和预测准确性。这个项目最适合我的长期职业目标，成为一个多学科的研究人员，能够同时开发方法和进行神经科学研究相结合的TMS与非侵入性成像。该项目的成果将是一个全新的框架，用于理解TMS对大脑皮层网络的影响，以用于科学研究和治疗应用。该补助金将使我能够达到加强我在成像神经科学实验方面的技能和扩大我对TMS神经生理学的知识的直接目标。指导阶段将在MGH-Harvard-MIT Martinos生物医学成像中心进行。附属机构提供与我的职业发展相关的一流教育。具体来说，除了培养我的实验专业知识外，我还将旁听哈佛和麻省理工学院的神经工程和复杂网络课程。通过小动物TMS实验验证模型的合作努力也将提供电生理学方面的实践培训。Martinos中心拥有先进的成像设备，可用于开展实验工作，并拥有世界一流的专家进行指导和咨询。

项目成果

Incorporating and Compensating Cerebrospinal Fluid in Surface-Based Forward Models of Magneto- and Electroencephalography.

在基于表面的磁磁图和脑电图正向模型中合并和补偿脑脊液。

• DOI: 10.1371/journal.pone.0159595
• 发表时间: 2016
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Stenroos,Matti;Nummenmaa,Aapo
• 通讯作者: Nummenmaa,Aapo

A 3-axis coil design for multichannel TMS arrays.

• DOI: 10.1016/j.neuroimage.2020.117355
• 发表时间: 2021-01-01
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Navarro de Lara LI;Daneshzand M;Mascarenas A;Paulson D;Pratt K;Okada Y;Raij T;Makarov SN;Nummenmaa A
• 通讯作者: Nummenmaa A