Algorithms for cross-scale integration and analysis

摘要：神经科学所获得的关于神经系统的绝大多数信息， 人类大脑的微观结构-认知能力的基质和 神经病理过程的特定位置-已经通过离体分析获得 组织.从历史上看，这涉及几十年（如果不是几百年）的切割，染色， 在显微镜下固定和成像。在过去的二十年里， 对人脑进行成像和分析。这包括微观方面的进展（例如， 介观的（例如，偏振光成像，PLI），光学相干断层扫描（OCT）， RNA-seq和宏观成像（例如，MRI）。虽然这些技术已经产生了巨大的 大量关于结构、分子、连接组、遗传和 尽管它们与脑的转录性质无关，但迄今为止它们对体内分析几乎没有影响。在 同样，虽然我们在定位重要大脑区域的能力方面取得了很大进展， 在活体实验中，这些能力在显微镜和神经病理学中几乎没有影响。在这 项目我们寻求使用我们的介观成像和分析工具，以消除这些障碍， 促进信息从显微镜到体内人体研究的流动，以及相反的流动 方向这些新能力的影响的例子将是：使用静息态功能磁共振成像 网络（rsFMRI），以指导在尸检过程中提取神经病理块， 基于网络的各种神经退行性疾病的理论或使用预测的血管 分布和密度，以提高fMRI的层特异性。

Abstract - The vast majority of information that neuroscience has obtained about the microscopic structure of the human brain – the substrate for cognitive competencies and the specific locations of neuropathological processes – has been obtained by the analysis of ex vivo tissue. Historically this involves the decades (if not centuries) old procedure of cutting, staining, mounting and imaging under a microscope. The last two decades have seen stunning advances in imaging and analysis of the human brain. This include advances in microscopic (e.g. CLARITY1, SWITCH), mesoscopic (e.g., polarized light imaging, PLI), optical coherence tomography (OCT), RNA-seq and macroscopic imaging (e.g., MRI). While these techniques have generated huge amounts of new information regarding the structural, molecular, connectomic, genetic and transcriptional nature of the brain, they have thus far had little impact on in vivo analysis. In the same way, while we have made great progress in our ability to localize important brain regions in living subjects, these capabilities have had little impact in microscopy and neuropathology. In this project we seek to use our mesoscopic imaging and analysis tools to remove these barriers and facilitate the flow of information from microscopy to in vivo human studies, as well as in the reverse direction. Examples of the impact of these new abilities would be: using resting-state fMRI networks (rsFMRI) to guide the extraction of neuropathological blocks during autopsy to test network-based theories of various neurodegenerative disease or using predicted vascular distributions and densities to improve the laminar specificity of fMRI.