Imaging and Analysis Techniques to Construct a Cell Census Atlas of the Human Brain Admin Supplement

摘要 形成人类大脑的约860亿个神经元是在多个尺度上组织的，从精细的 单个神经元树突分支的细节，到嵌入大规模神经元的局部电路， 大脑的各个系统。在这个项目中，我们将在这个巨大的规模范围内创建一个多尺度图像， 类似于谷歌地球的地图集，可以可视化大脑半球范围的网络， 放大以看到单个的，标记的细胞在前颞叶的微米分辨率。这一戏剧性 通过使用一系列成像技术，包括光片成像技术， 显微镜检查（LSM）、组织清除、免疫组织化学（IMH）、磁共振成像（MRI）和 光学相干层析成像（OCT）的新技术。特别是OCT， 变革性的技术，因为它提供了微米分辨率的大体积的组织，图像的所有组织 (as与荧光相反），不需要封片和染色，因此可以自动化， 当其在切割之前对组织成像时基本上无失真。基于LSM的IMH将提供分子， 细胞的形态和空间特性，使我们能够开发细胞分类系统， 相同组织的OCT图像将使我们能够消除切割和清理引起的失真， 将信息传输到全半球MRI，用于图谱绘制和体内推断。这种信息传递 关键取决于在各种分辨率和对比度类型上配准图像的能力。 为此，我们建议使用脑血管系统提供的内源性基准标志。到 使用深度学习充分利用脉管系统需要每个系统中的标记血管的训练集。 of our imaging成像modalities模态across横过a array阵列of examples例子.本补充的目标是提供这些 标签，包括内部和评价者之间的可靠性评估。

Abstract The ~86 billion neurons that form the human brain are organized at multiple scales, ranging from the fine details of an individual neuron’s dendritic arborization, to local circuits that are embedded within large-scale systems spanning the brain. In this project, we will image across this vast range of scales to create a multiscale atlas akin to Google Earth for the human brain that can visualize hemisphere-wide networks and then zoom in to see individual, labeled cells at micron resolution in the frontal temporal lobe. This dramatic advance will be made possible through the use of an array of imaging technologies, including light-sheet microscopy (LSM), tissue clearing, immunohistochemistry (IMH), magnetic resonance imaging (MRI) and newly developed techniques in Optical Coherence Tomography (OCT). OCT in particular is a potentially transformative technology as it provides micron resolution over large volumes of tissue, images all of the tissue (as opposed to fluorescence), does not require mounting and staining and hence can be automated, and is essentially distortion free as it images the tissue prior to cutting. LSM-based IMH will provide molecular, morphological and spatial properties of cells that will enable us to develop cellular classification systems, while OCT images of the same tissue will enable us to remove the distortions induced by cutting and clearing, and transfer information to whole-hemisphere MRI for atlasing and in vivo inference. This transfer of information depends critically on the ability to register images across a huge range of resolutions and contrast types. For this we propose to use the endogenous fiducial landmarks provided by the cerebral vasculature. To take full advantage of the vasculature using deep learning requires a training set of labeled vessels in each of our imaging modalities across an array of examples. The goal of this supplement is to provide these labelings including the assessment of intra- and inter-rater reliability.