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

项目概要 大脑解剖和功能的非侵入性成像对于研究大脑的发育和功能至关重要。 人脑的运作。它为临床医生提供了有关神经系统疾病的宝贵信息， 在理解神经系统疾病的一般机制以及为神经系统疾病提供指导方面 个别患者的诊断和治疗计划。在所有功能成像模式中， 脑磁图 (MEG) 具有最佳的组合时空分辨率，这使其成为 神经科学和神经病学的优秀工具。为了利用 MEG 潜在的良好空间分辨率，一种 必须解决逆问题，即从空间离散的神经电流中估计潜在的神经电流 磁场的测量。这项任务原则上是非唯一的，是通过拟合来完成的 对获取的多通道数据建立特定的参数化数学模型，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 参数转化为神经电流空间结构的估计，用于 解释各种任务和条件下的大脑功能。 MEG的空间精度可以为 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源，而不是一个，也许 扩展，来源？原则上，随着目标之间距离的增加，这项任务显得越来越困难。 源和测量传感器增加。困难的原因有两个：1）幅度 磁场随着距离的增加而减弱，2) 磁场衰减的空间复杂特征 距离比空间更平滑、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会导致不同的来源合并为一个估计的来源，即使在 假设传感器完全没有噪声的情况。为了提高 MEG 的基本分辨率，我们 将利用我们在磁信号分层分解方面的丰富专业知识，通过这些知识我们可以分离 对应于不同空间复杂度级别的信号特征，表示为空间频率。在 目标 1，我们开发适用于头皮的新的频率相关分层基函数 测量以及优化相应频率分解的数值稳定性 组件，并开发特定频率逆建模的方法，旨在改善空间 借助高频成分来提高分辨率。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检测性 传统的 MEG 系统。我们利用新的传感器技术允许灵活的设计和 还建议针对特定主题的传感器放置优化。在目标 3 中，我们设计模拟、模型 测量和人体测量来验证我们的方法。

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.