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

项目摘要 脑解剖和功能的非侵入性成像是研究大脑发育和功能的关键。 人脑的运作。它为临床医生提供了关于神经疾病的宝贵信息， 在了解神经系统疾病的一般机制方面，以及为 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)具有最好的时空组合分辨率，这使得它成为 神经科学和神经学的极佳工具。为了开发MEG潜在的良好空间分辨率，一个 必须解决逆问题，即从空间离散的 磁场的测量。这项任务在原则上不是唯一的，它是通过装配来完成的 对采集的多通道数据建立特定的参数化数学模型，并确定一组 根据特定的优化标准提供最佳匹配的参数。因此，这些 参数转化为对神经电流空间结构的估计，这在 解释在不同任务和条件下的大脑功能。脑磁图的空间精度可以达到 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 神经电流的浓度可以被区分为两个独立的来源，而不是一个 延期了，线人？原则上，这项任务似乎越来越困难，因为 信号源和测量传感器增加。造成这一困难的原因有两个：1) 磁场随距离减小；2)磁场衰减的空间复杂特征 与空间上更平滑、信息量更少的特征相比，距离更快。在传统的逆建模中， 第二类困难可能会导致不同的来源合并为一个估计来源，即使在 传感器完全没有噪音的假设情况。为了提高MEG的根本分辨率，我们 将利用我们在磁性信号分层分解方面的广泛专业知识，通过这些分解，我们可以分离出 对应于不同空间复杂程度的信号特征，表示为空间频率。在……里面 目的1.提出了一种新的适用于头皮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，并开发旨在改善空间性能的特定于频率的逆建模方法 在高频分量的帮助下实现分辨率。在目标2中，我们开发了新传感器的方法学 阵列设计，以便最大限度地提高更宽频谱的可探测性 传统的脑磁图系统。我们利用这样一个事实，即新的传感器技术允许灵活的设计和 还可以建议特定主题的传感器位置优化。在目标3中，我们设计了模拟、幻影 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.