A Cerebral Functional Unit Model for Multimodal Imaging of Neurovascular Coupling

描述（由申请人提供）：大脑的效率是神经、代谢和血管系统共同工作以执行大脑功能的程度的量度。 这些系统共同构成了大脑的一个功能单元，它们之间生理事件的协调被认为是大脑健康的重要标志。 并发多模态血流动力学和电生理测量提供了独特的能力，以量化这些神经血管的关系，从而研究脑功能单位的属性。 在这个项目中，我们建议开发新的多模态实验和基于模型的分析工具来表征大脑功能单元的属性。 我们假设神经、代谢和血管变化之间的关系的多模态表征与单独的自主（单一模态）测量相比将提供对大脑的更稳健和内在的评估。 我们将开发一个分析框架的基础上，自下而上的模型的大脑功能单位，这将使我们能够更好地利用独特的属性，同时多模态测量。 我们的模型将被应用于同时非侵入性，近红外光学成像（NIRS）和脑磁图（MEG）测量，以开发，测试和完善我们的方法的基础上，我们的模型的应用程序的一组体感实验。 该项目的具体目标是：目标1。 集成光学和MEG成像系统，以便同时进行神经血管测量。 我们将改进现有的仪器，硬件和分析框架，这将允许收集和共注册的并发近红外光学（NIRS）和MEG信号。 目标2. 量化神经和血液动力学诱发信号之间的关系。 使用视觉和体感刺激范例与参数输入相结合，我们将实验研究神经和血管诱发反应之间的典型关系。 目标3。 建立脑功能单位模型。 我们将开发和表征一个综合的多模式模型的脑功能单位，将信息从并发神经和血管测量。 公共卫生相关性：在健康的大脑中，神经、代谢和血管系统高度耦合以平衡神经和突触过程对能量的使用以及血管系统对底物的供应和废物的去除。 虽然人们普遍认为这种耦合对大脑的健康很重要，但研究这些影响的分析和解释方法尚未得到充分发展，无法详细描述这些关系。 特别是，多模态神经成像实验的效用可以通过开发新的分析方法来提高，该方法是特定于并发多模态测量的独特特征。 我们建议开发一个状态空间模型的神经，代谢和血管单位的大脑，这将使我们能够统计联合收割机并发测量从不同的神经成像技术，特别是近红外光谱（NIRS）和脑磁图（MEG），到一个统一的估计大脑功能。 该模型将提供一个新的工具，调查和表征神经，代谢和血管生理之间的潜在关系，并将提供一个新的框架融合的实验多模态信息。

DESCRIPTION (provided by applicant): The efficiency of the brain is a measure of the degree to which the neural, metabolic, and vascular systems work together collectively to perform cerebral function. The coordination of physiological events between these systems, which collectively comprise a functional unit of the brain, is believed to be an important marker of brain fitness. Concurrent multimodal hemodynamic and electrophysiological measurements offer the unique ability to quantify these neurovascular relationships and thereby investigate the properties of the cerebral functional unit. In this project, we propose to develop novel multimodal experimental and model-based analysis tools to characterize the properties of the cerebral functional unit. We hypothesize that multimodal characterization of the relationships between neural, metabolic, and vascular changes will provide more robust and intrinsic assessments of the brain in comparison to autonomous (single- modality) measurements alone. We will develop an analysis framework based on a bottom-up model of the cerebral functional unit that will allow us to better utilize the unique attributes of concurrent multimodal measurements. Our model will be applied to simultaneous non-invasive, near-infrared optical imaging (NIRS) and magnetoencephalography (MEG) measurements in order to develop, test, and refine our methods based on the application of our model to a set of somatosensory experiments. The specific aims of this project are: Aim 1. Integrate optical and MEG imaging systems to allow for concurrent neurovascular measurements. We will improve existing instrumentation, hardware, and analysis framework, which will allow for collection and coregistration of concurrent near-infrared optical (NIRS) and MEG signals. Aim 2. Quantify the relationships between neural and hemodynamic evoked signals. Using a combination of visual and somatosensory stimulation paradigms with parametric inputs, we will experimentally investigate the canonical relationships between neural and vascular evoked responses. Aim 3. Develop the cerebral functional unit model. We will develop and characterize an integrated multimodal model of the cerebral functional unit to incorporate information from concurrent neural and vascular measurements. PUBLIC HEALTH RELEVANCE: Within a healthy brain, the neural, metabolic, and vascular systems are highly coupled to balance the use of energy by neural and synaptic processes and the supply of substrates and removal of waste products by the vascular system. While it is generally accepted that such coupling is important to the health of the brain, analysis and interpretation methods to investigate these effects have not been adequately developed to allow detailed characterization of these relationships. In particular, the utility of multimodal neuroimaging experiments can be improved by developing new analysis methodologies that are specific to the unique characteristics of concurrent multimodal measurements. We propose to develop a state-space model of the neural, metabolic, and vascular units of the brain that will allow us to statistically combine concurrent measurements from differing neuroimaging techniques, specifically near-infrared spectroscopy (NIRS) and magnetoencephalography (MEG), into a unified estimate of brain function. This model will provide a new tool to investigate and characterize the underlying relationships between neural, metabolic, and vascular physiology and will offer a novel framework for fusion of experimental multimodal information.