Collaborative Proposal: Understanding Motor Cortical Organization through Engineering Innovation to TMS-Based Brain Mapping

这个项目解决了一个困扰科学家一个多世纪的问题：运动皮层，即大脑中神经冲动引发随意肌肉活动的部分，是如何代表和协调多个肌肉以产生大量运动的？为了回答这个问题，该项目将利用无创、导航、经颅磁刺激（TMS）测绘的独特优势来建立大脑生理和行为之间的因果关系。颅磁刺激是通过在头皮附近放置一圈电线来实现的，当电流激活时，线圈会在头皮和头骨之间产生磁场。颅磁刺激是唯一一种非侵入性的方法，可以像侵入性刺激一样有效地刺激大脑来绘制运动皮层。然而，使用基于tms的运动映射来理解多肌肉生理和控制，迫切需要在三个领域进行创新：1)大幅提高基于tms的运动皮层映射过程的效率、功效和可靠性；2)表征和验证基于tms的映射作为理解多肌肉激活与自主运动之间关系的探针；3)应用神经网络计算方法来提高对运动控制和组织的理解。通过颅磁刺激对运动表征的映射来增强对运动皮层生理学的理解，有可能更好地在手术切除肿瘤、评估脑震荡或中风引起的脑损伤以及识别控制假肢所需的成功脑机交互所需的皮层网络等应用中绘制大脑图谱。参与该项目的学生将接受培训，以解决神经科学、非侵入性脑刺激、软件设计、控制理论、机器学习、统计信号处理、数据降维和可视化等交叉领域的多学科挑战。与波士顿技术行业的领导者合作，将为本科生、研究生和研究生学员提供最先进的培训。通过东北大学的合作教育项目和麻省总医院的实习，将为初高中学生提供基于STEM的学习机会，激励不同群体的学生追求STEM职业。为了促进STEM职业发展并展示影响力，该团队将接触当地的场馆，以提高公众对科学的认识和欣赏，如科学博览会和波士顿科学博物馆。该合作项目的目标是对运动皮层（M1）在控制单个肌肉和产生复杂运动的协同作用中的作用进行更深入的机制理解。这将通过开发一些使用无创经颅磁刺激（TMS）的创新来完成，以绘制协同作用和单个肌肉的空间分布。革命性的计算进步将用于提取关于大脑与运动域以外的其他生理系统相互作用的更准确信息，并增加分析和数据可视化的严谨性，以增强可解释性和可重复性。加强对复杂运动的皮质运动组织的理解将为研究整个生命周期中运动系统的发展、人类表现增强的基础以及神经运动疾病的基础和特征铺平道路。研究计划分为三个目标。AIM 1是通过开发基于肌肉诱发电位（MEPs）作为头皮上二维空间坐标函数的高斯过程模型（GPM）的主动学习过程来加速基于tms的地图获取。开发的Active-GMP学习算法有望通过将花在空数据位点上的时间转移到模型需要更多样本以提高确定性的位点上，从而加快映射过程。将新算法的有效性和准确性与现有的三种替代算法进行比较。AIM 2是测试人类多肌肉经颅磁刺激图谱产生的协同作用的行为相关性，即从生物学角度验证AIM 1中开发的技术方法。具体地说，将从健康参与者的16块手臂肌肉中收集经颅磁刺激和自愿肌电图数据，同时受试者模仿美国手语字母表中静态字母和数字的手部姿势。从VOL数据和TMS数据中提取的非负矩阵分解提取的协同效应将进行比较，以确定TMS引发的协同效应是否与运动生产过程中使用的协同效应相匹配，以及自适应Active-GMP和用户引导的方法是否比其他方法更接近VOL数据产生的协同效应。AIM 3是开发生成和逆地形成像模型，分别对肌肉和协同作用的M1控制进行正演建模，并对M1组织进行反向映射。混合模型将tms诱发的皮层电场的受试者特异性FE模型与训练用于预测诱发肌肉反应的神经网络模型相结合，将用于回答以下关键问题：1)协同作用是运动控制的主要特征吗？Q2) M1运动神经元的直接投射是否增强了协同控制模型？和Q3)肌肉和协同作用是否在M1中分散组织？该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project addresses a question that has vexed scientists for more than a century: how does the motor cortex, i.e. the part of the brain where nerve impulses initiate voluntary muscular activity, represent and coordinate multiple muscles in order to produce a vast range of movements? To answer this question, this project will harness the unique strengths of non-invasive, navigated, transcranial magnetic stimulation (TMS) mapping to establish causal links between brain physiology and behavior. TMS is achieved by placing a coil of wires near the scalp, which when activated with an electrical current will create a magnetic field across the scalp and skull. TMS is the only non-invasive method available to stimulate the brain as effectively as invasive stimulation for mapping the motor cortex. However, innovations in three areas are critically needed to use TMS-based motor mapping to understand multi-muscle physiology and control: 1) drastically improving the efficiency, efficacy and reliability of the TMS-based motor cortex mapping processes, 2) characterizing and validating TMS-based mapping as a probe for understanding the relationship between multi-muscle activation and voluntary movement, and 3) applying a neural network computational method to improve understanding of motor control and organization. Enhanced understanding of motor cortex physiology through TMS mapping of motor representations has the potential to better map the brain in applications such as surgical removal of tumors, assessing brain injury due to concussions or stroke, and identifying cortical networks needed for successful brain-machine interactions for controlling prostheses. Students involved with this project will be trained to address multidisciplinary challenges at the intersection of neuroscience, non-invasive brain stimulation, software design, control theory, machine-learning, statistical signal processing, data dimensionality reduction and visualization. Partnership with Boston-based leaders in the technology industry will provide state-of-the-art training to undergraduate, graduate, and post-graduate trainees. Through cooperative educational programming at Northeastern University and internships with Mass General Hospital, STEM-based learning opportunities will be provided for middle- and high-school students, inspiring a diverse body of students to pursue STEM careers. To promote STEM careers and demonstrate impact, the team will reach out to local venues that promote public awareness and appreciation of science, such as science fairs and the Boston Museum of Science.The goal of this collaborative project is to develop a deeper mechanistic understanding of the role of the motor cortex (M1) in controlling single muscles and synergies in producing complex movements. This will be accomplished by developing several innovations in the use of non-invasive transcranial magnetic stimulation (TMS) to map the spatial distribution of synergies and single muscles. Transformative computational advances will be used to extract more accurate information about brain interaction with other physiological systems outside the motor domain and increase the rigor of analysis and data visualization to enhance interpretability, and repeatability. An enhanced understanding of corticomotor organization of complex movement will pave the way to studying motor system development across the lifespan, the basis of human performance enhancement, and the basis and characterization of neuromotor diseases. The research plan is organized under 3 aims. AIM 1 is to accelerate acquisition of TMS-based maps by developing an active learning process based on a Gaussian Process Model (GPM) of Muscle Evoked Potentials (MEPs) as a function of 2D spatial coordinates on the scalp. The developed Active-GMP learning algorithm is expected to speed up the mapping process by diverting time spent on loci with null data to loci where the model needs more samples to improve certainty. The efficacy and the accuracy of the new algorithm will be compared to three existing alternatives. AIM 2 is to test the behavioral relevance of synergies derived from human multi-muscle TMS mapping, i.e., to biologically validate the technical methods developed in Aim 1. Specifically, TMS and Voluntary (VOL) EMG data will be collected from 16 hand-arm muscles in healthy participants while subjects mimic hand postures for static letters and numbers of the American Sign Language alphabet. Non-negative matrix factorization-extracted synergies from VOL data and TMS data will be compared to determine if the TMS-elicited synergies match those utilized during movement production and if the adaptive Active-GMP and user-guided approaches more closely match synergies derived from VOL data compared to other approaches. AIM 3 is to develop generative and inverse topographic imaging models that allow forward modeling of M1 control and reverse mapping of M1 organization, respectively, of muscles and synergies. Hybrid models combining subject-specific FE modeling of TMS-induced cortical electric fields with neural network models trained to predict evoked muscle responses will be used to answer key questions: Q1) Are synergies dominant features of motor control? Q2) Do direct M1 motorneuron projections augment a synergy model of control? and Q3) Are muscles and synergies discretely organized in M1?This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.