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 脑解剖和功能的非侵入性成像对于研究脑的发育和功能至关重要。 人类大脑的运作。它为临床医生提供了宝贵的信息，神经系统的条件， 在理解神经系统疾病的机制方面， 个体患者的诊断和治疗计划。在所有功能成像模式中， 脑磁图（MEG）具有最佳的时空分辨率，这使其成为一种 神经科学和神经病学的绝佳工具。为了利用MEG潜在的良好空间分辨率， 必须解决逆问题，即，估计潜在的神经电流从空间离散 测量磁场。这个任务原则上是不唯一的， 将特定的参数化数学模型映射到所采集的多通道数据，并确定一组 根据特定的优化标准提供最佳拟合的参数。因此，这些 参数转换为神经电流的空间结构的估计，该估计用于 在各种任务和条件下解释大脑功能。脑磁图的空间精度可以 通过考虑以下问题来确定：两个相邻空间之间的最小距离是多少？ 神经电流的集中，可以区分为两个独立的来源，而不是一个，也许 延伸，源？从原则上讲，这项任务似乎越来越困难， 源和测量传感器增加。困难的原因是两方面的：1） 磁场随距离减小，以及2）磁场衰减的空间复杂特征 其中距离比空间上更平滑、信息量更少的特征更快。在传统的逆建模中， 第二种类型的困难可能导致不同的源被合并为一个估计源 假设传感器完全没有噪声。为了提高脑磁图的基本分辨率，我们 我们将利用我们在磁信号分层分解方面的广泛专业知识， 信号特征对应于不同的空间复杂度水平，表示为空间频率。在 目的1，我们发展了适用于头皮的新的频率相关的分层基函数 测量，优化相应频率分解的数值稳定性 组件，并开发特定频率的逆建模方法，旨在提高空间 在高频元件的帮助下提高分辨率。在目标2中，我们开发新传感器的方法 阵列设计，以最大限度地提高比可实现的更宽频谱的可探测性， 常规MEG系统。我们利用新传感器技术允许灵活设计的事实， 还建议了对象特定传感器放置优化。在Aim 3中，我们设计模拟， 测量和人类测量来验证我们的方法。