CRII: SCH: Brain-Body Sensor Fusion: Merging Neuroimaging With Full-Body Motion Capture

CRII：SCH：脑体传感器融合：将神经成像与全身运动捕捉相结合

• 批准号: 1565962
• 负责人: Kunal Mankodiya
• 金额: $ 17.5万
• 依托单位: University of Rhode Island
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1565962&HistoricalAwards=false
• 关键词: CRII; SCH; Brain; Body; Sensor

Title: CRII: SCH: Brain-Body Sensor Fusion: Merging Neuroimaging With Full-Body Motion CapturePI: Kunal MankodiyaThis proposal is to establish a cyber infrastructure to detect and visualize the brain's activities while the human body is in motion, performing various limb movements. Any disturbance in the brain-body functional coupling affects one's ability to move efficiently, causing various movement disorders such as Parkinson's disease that is the second most common neurodegenerative disorder, affecting 4 million worldwide. Acquiring deepened understanding of such movement disorders demands to study brain activity and body movements simultaneously. Due to technical constraints, traditional brain scanners such as functional magnetic resonance imaging (fMRI) require the body to remain horizontal and motionless during scanning periods. Moreover, fMRI is bulky and immobile, providing one image per second, while the brain's activity occurs at a much faster rate. Hence, there is a limited knowledge of brain dynamics that are coupled tightly to body's mobility behaviors. In recent years, "functional near infrared spectroscopy (fNIRS)" is emerging as a portable, optical neuroimaging device that provide greater benefits compared to the bulky counterparts such as fMRI. The PI takes the fNIRS technology one step further by integrating it with the maturing technology of body sensor networks (BSN) to quantify brain-body connectivity. The project aims to establish a medical cyber-physical system (mCPS) delivering the system integration of fNIRS and BSN. Specifically, the project mainly explores the following directions: System Integration: The mCPS is a system integration aggregating two complex subsystems (20-channel fNIRS system and 17-sensor body motion suit) to produce a unique interface delivering the overarching functionality of motion-tagged neuroimaging. Challenges in this project emerge from the heterogeneity of system components and interactions among them. In this work, system integration takes place into three forms: 1) "hardware integration" for centralizing the multimodal sensor data, 2) "data integration/fusion" for conditioning and fusing multi-dimensional task-level signals, and 3) "presentation integration" for combined visualization of brain and body behaviors. Motion-Tolerant Neuroimaging: It is expected that fNIRS neuroimaging data will face challenges of motion artifacts. Brain activity data would largely be superimposed by the mechanical vibrations traversed to the scalp due to the body movements. The PI explores harmonic sum models to rectify motion-affected brain activities to extract the more accurate hemodynamic response of the brain in mobile settings. A Clinical Screening Interface for Neurologists: The PI collaborates with a neurologist specialized in treating Parkinson's disease. The end result will be a clinical tool for physicians to screen the motor exams of patient with Parkinson's disease. The clinical tool is aimed at providing quantified data of motion and brain's cortical activities displayed side-by-side for the improved diagnostic screening of Parkinson's disease.

标题：CRII：SCH：Brain-Body传感器融合：将神经成像与全身运动捕捉相结合这项提议是为了建立一个网络基础设施，在人体运动时检测和可视化大脑的活动，执行各种肢体运动。大脑-身体功能耦合的任何障碍都会影响一个人的有效行动能力，导致各种运动障碍，如帕金森氏症，这是第二常见的神经退行性疾病，影响全球400万人。要加深对这种运动障碍的理解，就需要同时研究大脑活动和身体运动。由于技术限制，传统的脑扫描仪，如功能磁共振成像(FMRI)，要求身体在扫描期间保持水平和不动。此外，功能磁共振成像体积庞大，不能移动，每秒提供一张图像，而大脑的活动速度要快得多。因此，对大脑动力学的了解有限，这些知识与身体的运动行为密切相关。近年来，“功能性近红外光谱(FNIRS)”作为一种便携式的光学神经成像设备正在兴起，与体积庞大的同类设备(如功能磁共振成像)相比，它提供了更大的好处。PI将fNIRS技术进一步发展，将其与日益成熟的身体传感器网络(BSN)技术相结合，以量化大脑与身体的连接。该项目旨在建立一个医疗网络物理系统(MCPS)，提供fNIRS和BSN的系统集成。具体地说，该项目主要探索以下方向：系统集成：MCPS是一个系统集成，聚合了两个复杂的子系统(20通道fNIRS系统和17传感器人体运动服)，以产生一个独特的界面，提供运动标记神经成像的主要功能。该项目中的挑战来自系统组件的异构性以及它们之间的交互。在这项工作中，系统集成分为三种形式：1)用于集中多模式传感器数据的“硬件集成”，2)用于调节和融合多维任务级信号的“数据集成/融合”，以及3)用于大脑和身体行为的组合可视化的“呈现集成”。运动耐受性神经成像：预计fNIRS神经成像数据将面临运动伪影的挑战。大脑活动数据在很大程度上会被身体运动传递到头皮的机械振动叠加在一起。PI探索调和和模型来校正受运动影响的大脑活动，以提取在移动环境中更准确的大脑血流动力学响应。神经科医生的临床筛查界面：PI与专门治疗帕金森氏症的神经科医生合作。最终结果将成为医生筛查帕金森氏症患者运动检查的临床工具。该临床工具旨在提供并行显示的运动和大脑皮质活动的量化数据，以改进帕金森病的诊断筛查。