Project 3

项目摘要/摘要--项目3 我们建议将我们最先进的功能磁共振成像(FMRI)工具与脑电相结合 (EEG)来研究人类的软脑膜神经血管回路。这个电路由一个pial网络组成。 将神经元活动与内部小动脉血管运动相结合的小动脉，产生动态模式 小动脉直径上的相干振荡，有效地分离了皮质外套膜。 今天，功能磁共振成像是最广泛使用的非侵入性测量全人类神经活动的工具 大脑。所有fmri信号都起源于血管，因此正确解释这些血流动力学信号是 了解潜在的神经活动。我们的团队已经证明了动脉中的自发振荡 大脑皮层的血管直径或血管运动受局部神经活动的影响，而小动脉 通过沿着血管的主动信号，表现为与其他连接的小动脉的耦合振荡器 内皮细胞。这激发了U19的中心假说--局部神经元驱动和神经调节 具有超低频成分的输入与小动脉的固有振荡特性相竞争。这 允许不同的皮质区域以不同的频率振荡，并导致 血管动力学和时间相关区域的不同星座的形成。的耦合性 动脉振荡会引起下游静脉血氧的耦合，这是血液循环的基础。 氧合水平依赖(BOLD)对比，最常用的fMRI信号。 在目标1中，我们将采用我们的非侵入性成像工具来成像人体软脑膜的解剖和动力学。 动脉血管网。然后我们将开发新的工具来测量软脑膜小动脉的直径变化，以 直接跟踪人类的血管运动，并将这些动力学与标准的fMRI测量联系起来。我们的目标2是一个人类 项目1的对应者；我们将通过软脑膜神经血管研究多种感觉驱动的血管整合 电路及其在大尺度血流动力学中的反映。我们的目标3是项目2的人类对应；我们将 在觉醒状态的自发波动期间，同时记录BOLD fMRI和EEG，并确定如何 大脑内部状态与我们成像读数的空间模式有关。类似于项目2，我们将测试 这些血流动力学模式是否可以单独或结合脑电信号用来预测认知能力 性能。最后，我们将通过项目4来设计一个现象学的数学模型 这从血管积分器的角度捕捉到了大脑状态的本质， 连贯的血管/血流动力学波动模式。影响：项目3将提供强大的生理学 为解释人类大规模功能磁共振信号和更好地理解 将自发神经血管活动与认知表现联系起来的机制。总体而言，我们的计划将 在人类功能磁共振可观察性和潜在的大脑内部状态之间建立反向联系，潜在地 包括从非侵入性测量推断神经调节动力学。

PROJECT SUMMARY/ABSTRACT – PROJECT 3 We propose to leverage our state-of-the-art functional MRI (fMRI) tools combined with electroencephalography (EEG) to investigate the pial neurovascular circuit in humans. This circuit is composed of a network of pial arterioles that integrate neuronal activity with the intrinsic arteriolar vasomotion, producing dynamic patterns of coherent oscillations in arteriolar diameter that effectively parcellate the cortical mantle. Today fMRI is the most widespread tool for measuring neural activity noninvasively across the entire human brain. All fMRI signals are vascular in origin, thus proper interpretation of these hemodynamic signals is key to understanding the underlying neural activity. Our team has demonstrated that spontaneous oscillations in arterial vascular diameter, or vasomotion, in the cerebral cortex is entrained by local neural activity, and that arterioles behave as coupled oscillators with other connected arterioles via active signaling along the vascular endothelium. This motivates the central hypothesis of this U19—that local neuronal drive and neuromodulatory inputs with ultralow-frequency components compete with the intrinsic oscillatory properties of arterioles. This allows different cortical regions to oscillate at different frequencies and results in spatial parcellation of vasodynamics and the formation of different constellations of temporally coherent regions. The coupling of arterial oscillations will induce coupling of the downstream venous blood oxygenation that is the basis of Blood- Oxygenation Level Dependent (BOLD) contrast, the most commonly used fMRI signal. In Aim 1, we will adapt our noninvasive imaging tools to image the anatomy and dynamics of the human pial arterial vascular network. We will then develop novel tools to measure diameter changes of pial arterioles to directly track vasomotion in humans, and link these dynamics to standard fMRI measures. Our Aim 2 is a human counterpart of Project 1; we will study vascular integration of multiple sensory drives by the pial neurovascular circuit and its reflection in large-scale hemodynamics. Our Aim 3 is the human counterpart of Project 2; we will record BOLD fMRI and EEG simultaneously during spontaneous fluctuations in arousal state, and identify how internal brain states are linked to spatial patterns of our imaging readouts. Similar to Project 2, we will test whether these hemodynamic patterns, alone or in combination with EEG signals, can be used to predict cognitive performance. Finally, we will work throughout with Project 4 to devise a phenomenological mathematical model that captures the essence of a brain state from the standpoint of the vascular integrator producing large-scale patterns of coherent vascular/hemodynamic fluctuations. Impact: Project 3 will offer a strong physiological foundation for the interpretation of large-scale fMRI signals in humans and better understanding of the mechanisms linking spontaneous neurovascular activity to cognitive performance. Overall, our program will establish an inverse link between human fMRI observables and the underlying internal brain state, potentially including inference of neuromodulatory dynamics from noninvasive measurements.