Local neuronal drive and neuromodulatory control of activity in the pial neurovascular circuit

项目摘要/摘要--总体 我们试图了解软脑膜神经血管回路的本质，其动力学特征是 0.1赫兹附近的超低频振荡，在大脑皮层上分割成独立的相干区域。我们会 利用这一知识在光学系统中观察到的血流动力学模式之间形成数学关系 以及功能磁共振成像实验和潜在的大脑状态。 我们提出的研究建议利用我们在活体光学显微镜方面的实验专业知识 小鼠和人的功能磁共振成像。这些用于数据采集的主要模式与 行为训练、电生理学和数据分析。我们的实验工作与两个理论的工作是平行的 努力。一项混合的分析/计算工作是关于耦合振子动力学，以建立模型，在 软脑膜神经血管回路的复杂程度各不相同。第二个纯粹的计算工作涉及到 通过软脑膜神经血管回路的区域性振荡来调节氧气的运输。 软脑膜神经血管回路是由软脑膜小动脉组成的二维网络，它经历了 ~0.1赫兹血管运动频段的节律性振荡。这个环路中的每个元素--一段小动脉 其直径受平滑肌收缩/扩张的调节，包含一种内在的节律 生成器，很像中央模式生成器中的固有爆发性神经元。软脑膜小动脉整合 邻近小动脉、下层神经元、皮质下神经元的神经元活动和神经调节 中心，以产生跨皮质地幔的小动脉直径的相干振荡的动态模式。 这些图案包含以略有不同的频率振荡的区域，即它们被分割成独立的 地区。令人着迷的问题是，这种分割只部分反映了来自直接底层的输入 神经元输入。我们试图理解、建模和利用这一划分。 多年来，PI一直在神经科学和神经血管科学问题上进行合作。这 提案是他们发现的结果，并在结构化的协作工作中汇聚了兴趣。项目1将 形成对软脑膜神经血管回路的基本生理学的理解。这包括 确定脑小动脉是否真的充当相互作用的非线性振荡器，即它们是否缠绕和锁相 而不是被动地过滤。项目1、2和4将从实验和理论上探索4 竞争性相互作用，即，来自相邻小动脉的输入，(Ii)来自底层神经元的输入，(Iii)来自 参与动态平衡的皮质下区域；以及(Iv)来自脑神经调节中心的输入，导致 观察到软脑膜神经血管活动的模式。项目2和4将探索和模拟监管 地下血管中的氧气，而项目3将扩大人体磁共振成像的分辨率，以观察 单个血管的CBV变化，从而以无与伦比的分辨率测量软脑膜神经血管动力学。一个 尤其令人感兴趣的是将血管运动的时空模式转化为对大脑内部状态的预测。

PROJECT SUMMARY/ABSTRACT – OVERALL We seek to understand the nature of the pial neurovascular circuit, whose dynamics is characterized by ultralow frequency oscillations near 0.1 Hz that parcellate into separate coherent regions across cortex. We will use this knowledge to form a mathematical relation between the hemodynamic patterns observed in optical and functional magnetic resonance imaging experiments and the underlying brain state. Our proposed studies propose to leverage our experimental expertise in in vivo optical microscopy in mouse and fMRI in mouse and human. These primary modalities for data acquisition are combined with behavioral training, electrophysiology, and data analysis. Our experimental effort is parallel by two theoretical efforts. One mixed analytical/computational effort is on coupled oscillator dynamics to formulate models, at varying levels of complexity, of the pial neurovascular circuit. A second solely computational effort concerns the modulation of the transport of oxygen, by regional oscillations of the pial neurovascular circuit. The pial neurovascular circuit is composed of a two-dimensional network of pial arterioles that undergo rhythmic oscillations in the ~ 0.1 Hz vasomotor band. Each element in this circuit - a segment of arteriole whose diameter is modulated by the constriction/dilation of smooth muscle, contains an intrinsic rhythm generator, much like intrinsic bursting neurons in central pattern generators. The pial arterioles integrate neuronal activity from neighboring arterioles, underlying neurons, subcortical neurons, and neuromodulatory centers to produce dynamic patterns of coherent oscillations in arteriolar diameter across the cortical mantle. These patterns contain regions that oscillate at slightly different frequencies, i.e., they parcellate into separate regions. The fascinating issue is that the parcellation only partially reflects input from the directly underlying neuronal input. We seek to understand, model, and exploit this parcellation. The PIs have collaborated on issues in neuroscience and neurovascular science for many years. This proposal is a result of their discoveries and converging interest in a structured collaborative effort. Project 1 will formulate an understanding of fundamental physiology of the pial neurovascular circuit. This includes determining if brain arterioles truly act as interacting non-linear oscillators, i.e., that they entrain and phase-lock rather than passively filter. Projects 1, 2, and 4 will explore experimentally and theoretically how four competitive interactions, viz, input from neighboring arterioles, (ii) input from underlying neurons, (iii) input from subcortical areas involved in homeostasis; and (iv) input from brain neuromodulatory centers, lead to the observed patterns of pial neurovascular activity. Projects 2 and 4 will explore and model the regulation of oxygen in subsurface vessels, while Project 3 will expand the resolution of MR imaging in humans to observe single vessels CBV changes and thus measure pial neurovascular dynamics with unparalleled resolution. A particular interest is to transform spatiotemporal patterns of vasomotion into predictions of internal brain state.