Spatial-frequency decompositions for enhancement of source reconstruction resolution in MEG

用于增强 MEG 中源重建分辨率的空间频率分解

• 批准号: 10661098
• 负责人: Samu Taulu
• 金额: $ 19.44万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-06 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10661098
• 关键词: Algorithms; Alzheimer' s Disease; Anatomy; Area; Brain; Brain imaging; Brain region; Clinical; Clinical Research; Compensation; Complex; Data; Derivation procedure; Detection; Development; Devices; Diagnosis; Diagnostic; Electroencephalography; Epilepsy; Etiology; Frequencies; Functional Imaging; Geometry; Head; Helmet; Human; Individual; Lead; Location; Magnetism; Magnetoencephalography; Measurement; Measures; Methodology; Methods; Modality; Modeling; Movement; Neurologic; Neurology; Neurosciences; Noise; Optics; Parkinson Disease; Positioning Attribute; Pump; Research; Resolution; Scalp structure; Shapes; Signal Transduction; Source; Structure; System; Techniques; Testing; Time; Translating; Validation; autism spectrum disorder; design; electric field; experimental study; flexibility; human data; human subject; imaging modality; improved; individual patient; interest; magnetic field; mathematical methods; mathematical model; nervous system disorder; neural; non-invasive imaging; novel; operation; reconstruction; sensor; sensor technology; signal processing; simulation; spatiotemporal; superconducting quantum interference device; tool; treatment planning; vector

Project Summary Non-invasive imaging of brain anatomy and function is essential for the study of the development and operation of the human brain. It provides clinicians with invaluable information on neurological conditions, both in terms of understanding mechanisms of neurological diseases in general as well as providing guidance for diagnostics and treatment planning of individual patients. Among all functional imaging modalities, magnetoencephalography (MEG) has the best combined spatiotemporal resolution, which makes it an excellent tool for neuroscience and neurology. To exploit the potentially good spatial resolution of MEG, one must solve the inverse problem, i.e., estimate the underlying neural currents from the spatially discretized measurement of the magnetic field. This task, which is non-unique in principle, is accomplished by fitting specific parametrized mathematical models to the acquired multi-channel data and determining a set of parameters that provides the best fit according to a particular optimization criterion. Consequently, these parameters translate to an estimate of the spatial structure of the neural current, which is used in the interpretation of brain function under various tasks and conditions. The spatial precision of MEG can be determined by considering the following question: What is the minimum distance between two nearby spatial concentrations of neural current that can be distinguished as two separate sources instead of one, perhaps extended, source? In principle, this task appears increasingly more difficult as the distance between the sources and the measurement sensors increases. The reason for the difficulty is two-fold: 1) the amplitude of the magnetic field decreases with distance and 2) the spatially complex features of the magnetic field decay with distance faster than the spatially smoother, less informative, features. In conventional inverse modeling, the second type of difficulty may cause distinct sources to become merged as one estimated source even in the hypothetical situation that the sensors have no noise at all. To improve fundamental resolution of MEG, we will utilize our extensive expertise in hierarchical decompositions of magnetic signals by which we can separate signal features corresponding to different levels of spatial complexity, represented as spatial frequencies. In Aim 1, we develop new frequency-dependent hierarchical basis functions applicable to on-scalp measurements as well, optimize the numerical stability of the decomposition of the corresponding frequency components, and develop methodology for frequency-specific inverse modeling that aims at improving spatial resolution with the help of high-frequency components. In Aim 2, we develop methodology for new sensor array design in order to maximize the detectability of a wider frequency spectrum than what is achievable with conventional MEG systems. We exploit the fact that new sensor technologies allow for flexible designs and suggest subject-specific sensor placement optimization as well. In Aim 3, we design simulations, phantom measurements, and human measurements to validate our methods.

项目摘要 脑解剖学和功能的非侵入性成像对于研究发展至关重要 人脑的操作。它为临床医生提供了有关神经系统状况的宝贵信息 在理解神经疾病的机制方面，以及为指导提供指导 个别患者的诊断和治疗计划。在所有功能成像方式中， 磁脑摄影（MEG）具有最好的组合时空分辨率，这使其成为 神经科学和神经病学的出色工具。为了利用MEG的潜在良好空间分辨率，一个 必须解决反问题，即，从空间离散的 磁场的测量。原则上这项任务是非唯一的，是通过安装来完成的 针对获得的多通道数据的特定参数化数学模型，并确定一组 根据特定优化标准提供最佳拟合的参数。因此，这些 参数转化为对神经电流的空间结构的估计，用于 在各种任务和条件下对大脑功能的解释。 MEG的空间精度可以是 通过考虑以下问题来确定：附近两个空间之间的最小距离是多少 可以区分为两个独立来源的神经电流的浓度，而不是一个。 扩展，来源？原则上，这项任务似乎越来越困难，因为 来源和测量传感器增加。难度的原因是两个方面：1） 磁场随距离而降低，2）磁场衰减的空间复杂特征 距离的距离比空间更光滑，内容较少的功能更快。在常规的反向建模中 第二种难度可能导致不同的来源合并为一个估计来源，即使在 传感器根本没有噪音的假设情况。为了改善MEG的基本解决方案，我们 将利用我们广泛的专业知识在磁信号的层次分解中，我们可以分开 信号特征对应于不同级别的空间复杂性，表示为空间频率。在 AIM 1，我们开发了适用于SCALP的新的频率层次基础函数 测量也可以优化相应频率分解的数值稳定性 组件，并开发用于频率特异性逆建模的方法，旨在改善空间 借助高频组件解决。在AIM 2中，我们为新传感器开发方法 阵列设计是为了最大程度地提高更广泛的频谱的可检测性 传统的MEG系统。我们利用了一个新的传感器技术允许灵活设计和 也建议特定于特定的传感器放置优化。在AIM 3中，我们设计模拟，幻影 测量和人体测量以验证我们的方法。