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

职务名称：中环填海第二期：SCH：脑体传感器融合：将神经成像与全身运动捕捉相结合PI：Kunal Mankodiya该提案旨在建立一个网络基础设施，以检测和可视化人体运动时大脑的活动，执行各种肢体运动。脑-体功能耦合的任何干扰都会影响一个人有效移动的能力，导致各种运动障碍，例如帕金森病，这是第二种最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，需要同时研究大脑活动和身体运动。由于技术限制，传统的大脑扫描仪，如功能性磁共振成像（fMRI），要求身体在扫描期间保持水平和静止。此外，功能性磁共振成像体积庞大，固定不动，每秒只能提供一幅图像，而大脑活动的速度要快得多。因此，有一个有限的知识，大脑动力学是紧密耦合到身体的移动行为。近年来，“功能性近红外光谱（fNIRS）”作为一种便携式光学神经成像设备正在兴起，与功能性磁共振成像（fMRI）等笨重的设备相比，它提供了更大的好处。PI通过将fNIRS技术与成熟的身体传感器网络（BSN）技术相结合，将fNIRS技术向前推进了一步，以量化脑-体连接。该项目旨在建立一个医疗信息物理系统（mCPS），提供fNIRS和BSN的系统集成。具体而言，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传感器身体运动服），以产生一个独特的界面，提供运动标记神经成像的总体功能。这个项目的挑战来自于系统组件的异质性和它们之间的交互。在这项工作中，系统集成分为三种形式：1）“硬件集成”集中的多模态传感器数据，2）“数据集成/融合”的调节和融合多维任务级信号，和3）“呈现集成”的大脑和身体行为的组合可视化。运动耐受神经成像：预计fNIRS神经成像数据将面临运动伪影的挑战。大脑活动数据将在很大程度上被由于身体运动而穿过头皮的机械振动叠加。PI探索谐波和模型，以纠正受运动影响的大脑活动，从而在移动的设置中提取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与专门治疗帕金森病的神经科医生合作。最终的结果将是一个临床工具，医生筛选运动检查的帕金森氏病患者。该临床工具旨在提供并排显示的运动和大脑皮层活动的量化数据，以改进帕金森病的诊断筛查。

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.