Brain-wide Neuronal Circuit Mapping with X-ray Nano-Holography

项目摘要 该提案的目标是开发基于同步加速器的X射线成像技术，以实现高分辨率成像 整个大脑的神经元回路。目前，即使是在很小的范围内，全面绘制全脑回路图也是不可行的 哺乳动物模型系统，因为光学显微镜(LM)缺乏足够的分辨率和电子显微镜(EM) 不能应用于大容量。利用新的第四代同步加速器前所未有的性能 来源：欧洲同步加速器，我们将开发用于大规模X射线纳米全息(XNH)成像的技术 大脑回路的成像。利用信号源相干性和亮度的改进，我们将改进 成像分辨率允许直接可视化神经元之间的突触连接，并开发成像方案 这使得在典型的光束线实验中可以成像厘米级的电路体积。我们将结合非 用EM和LM成像技术严格和定量地验证XNH的准确性 基于电路重构。然后我们将使用这个相关的工作流来研究远程 感觉输入、局部突触微电路和单个神经元活动，研究后部电路如何 顶叶皮质(PPC)支持知觉决策。最后，我们将把XNH电路映射应用于整个 大脑皮层，并利用基于深度学习的机器视觉算法获得大脑皮层的全面图谱 连通性。这一图谱原则上将解决单一轴突皮质区域之间的所有远程连接 分辨率，有助于洞察不同的皮质区域如何实现特定的功能，以及皮质如何分布 网络支持认知，并受到精神障碍的影响。

Project Summary This proposal's objective is to develop synchrotron-based X-ray imaging technologies to enable high-resolution imaging of brain-wide neuronal circuits. Comprehensively mapping brain-wide circuits is not currently feasible, even in small mammalian model systems, because light microscopy (LM) lacks sufficient resolution and electron microscopy (EM) cannot be applied over large volumes. Leveraging the unprecedented qualities of the new 4th generation synchrotron source at the European Synchrotron, we will develop X-ray nano-holography (XNH) imaging techniques for large-scale imaging of brain circuits. Taking advantage of improvements in source coherence and brightness, we will improve imaging resolution to allow direct visualization of synaptic connections between neurons, and develop imaging protocols that allow imaging of centimeter-scale circuit volumes within a typical beamline experiment. We will combine non- destructive XNH with EM and LM imaging techniques to rigorously and quantitatively validate the accuracy of XNH- based circuit reconstruction. We will then use this correlative workflow to study the relationships between long-range sensory inputs, local synaptic micro-circuitry, and single-neuron activity, investigating how circuits in the posterior parietal cortex (PPC) support perceptual decision-making. Lastly, we will apply XNH circuit-mapping over an entire cortical hemisphere, and utilize deep-learning based machine vision algorithms to obtain a comprehensive atlas of cortical connectivity. This atlas will in principle resolve all long-range connections between cortical areas at single-axon resolution, lending insight into how distinct cortical areas achieve specialized function, and how distributed cortical networks support cognition and are affected by psychiatric disorders.