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

项目总结/摘要-总体 我们试图了解软膜神经血管回路的性质，其动力学的特点是： 在0.1 Hz附近的超低频振荡，包裹成跨皮层的独立相干区域。我们将 使用该知识来形成光学中观察到的血液动力学模式之间的数学关系， 以及功能性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体内光学显微镜方面的实验专长， 小鼠和人的fMRI。这些主要的数据采集方式与 行为训练、电生理学和数据分析。我们的实验努力与两个理论上的平行 努力一个混合的分析/计算工作是在耦合振荡器动力学上制定模型， 软膜神经血管回路的复杂程度不同。第二个纯粹的计算工作涉及 通过软脑膜神经血管回路的局部振荡来调节氧气的运输。 软脑膜神经血管回路由软脑膜小动脉的二维网络组成， ~ 0.1 Hz血管收缩带的节律性振荡。回路中的每一个元素-一段小动脉 其直径由平滑肌的收缩/扩张调节，包含内在节律 发生器，很像中枢模式发生器中的内在爆发神经元。软脑膜小动脉整合 来自邻近小动脉、下层神经元、皮层下神经元和神经调节神经元的神经元活动 中心，以产生跨皮质外套的小动脉直径的相干振荡的动态模式。 这些图案包含以略微不同的频率振荡的区域，即，它们被分成了 地区有趣的问题是，这种划分只部分反映了直接底层的输入 神经元输入我们试图理解，模型，并利用这种分割。 PI多年来一直在神经科学和神经血管科学领域的问题上进行合作。这 一个建议是他们的发现和在结构化的合作努力中的共同兴趣的结果。项目1将 形成对软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，它们携带并相位锁定 而不是被动过滤。项目1、2和4将从实验和理论上探索四个 竞争性相互作用，即，来自邻近小动脉的输入，（ii）来自底层神经元的输入，（iii）来自 参与稳态的皮层下区域;和（iv）来自脑神经调节中心的输入，导致 观察软膜神经血管活动的模式。项目2和项目4将探讨和模拟 氧气在皮下血管，而项目3将扩大分辨率的磁共振成像在人类观察 单血管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 特别感兴趣的是将血管运动的时空模式转换为内部脑状态的预测。

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.