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了科学家们超过世纪的问题：运动皮层（大脑中神经冲动启动自主肌肉活动的部分）是如何代表和协调多块肌肉以产生大范围的运动的？为了回答这个问题，该项目将利用无创、导航、经颅磁刺激（TMS）映射的独特优势，建立大脑生理学和行为之间的因果关系。TMS是通过在头皮附近放置一个线圈来实现的，当用电流激活时，线圈将在头皮和头骨上产生磁场，以刺激大脑。 TMS是唯一可以像侵入性刺激一样刺激大脑的非侵入性方法。然而，要使用基于TMS的运动映射来理解多肌肉生理学和控制，迫切需要三个领域的创新：1）极大地提高了基于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的映射作为用于理解多肌肉激活和随意运动之间的关系的探针，以及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TMS映射运动表征来增强对运动皮层生理学的理解，有可能更好地映射大脑，例如手术切除肿瘤，评估脑震荡或中风引起的脑损伤，以及识别成功控制假肢的脑机交互所需的皮层网络。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，数据降维和可视化交叉的多学科挑战。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。通过在东北大学的合作教育计划和与大众总医院的实习，将为初中和高中学生提供基于STEM的学习机会，激励不同的学生追求STEM职业。为了促进STEM事业并展示影响力，该团队将接触当地促进公众对科学的认识和欣赏的场所，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械理解。这将通过在使用非侵入性经颅磁刺激（TMS）中开发几项创新来实现，以绘制协同作用和单个肌肉的空间分布。变革性的计算进步将用于提取有关大脑与运动领域以外的其他生理系统相互作用的更准确信息，并提高分析和数据可视化的严谨性，以增强可解释性和可重复性。增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的运动系统发展铺平道路，这是人类性能增强的基础，也是神经运动疾病的基础和特征。研究计划分为三个目标。目的1是通过开发基于肌肉诱发电位（MEP）的高斯过程模型（GPM）作为头皮上二维空间坐标的函数的主动学习过程来加速基于TMS的地图的获取。开发的Active-GMP学习算法预计将通过将在具有空数据的位点上花费的时间转移到模型需要更多样本以提高确定性的位点来加快映射过程。新算法的有效性和准确性将与现有的三种替代方案进行比较。目的2是测试来自人类多肌肉TMS映射的协同作用的行为相关性，即，生物学验证目标1中开发的技术方法。具体而言，TMS和自愿（VOL）EMG数据将收集从16个手臂肌肉在健康的参与者，而受试者模仿手姿势的静态字母和数字的美国手语字母表。将比较从VOL数据和TMS数据中提取的非负矩阵因子分解协同效应，以确定TMS引发的协同效应是否与运动生产期间使用的协同效应相匹配，以及自适应Active-GMP和用户指导方法与其他方法相比是否更接近地匹配从VOL数据中获得的协同效应。目的3是开发生成和反向地形成像模型，允许M1控制和M1组织的反向映射，分别为肌肉和协同作用的正向建模。将TMS诱导的皮层电场的受试者特定FE建模与经训练以预测诱发肌肉反应的神经网络模型相结合的混合模型将用于回答关键问题：Q1）协同作用是否是运动控制的主要特征？直接M1运动神经元投射是否增强了控制的协同模型？和Q3）肌肉和协同作用在M1中是离散组织的吗？该奖项反映了NSF的法定使命，并被认为是值得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的支持。

This project addresses a question that has vexed scientists for more than a century: how does the motor cortex (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 to stimulate the brain. TMS is the only non-invasive method available to stimulate the brain like invasive stimulation. However, to use TMS-based motor mapping to understand multi-muscle physiology and control, innovations in three areas are critically needed: 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.