Algorithms for cross-scale integration and analysis

摘要-神经科学获得的关于 人脑的微观结构--认知能力和 神经病理过程的具体位置-已通过体外分析获得 组织。从历史上看，这涉及数十年(如果不是数百年)的切割、染色、 在显微镜下安装和成像。在过去的二十年里，我们看到了惊人的进步 对人脑进行成像和分析。这包括在显微方面的进展(例如CLARITY1， 开关)、介观(例如，偏振光成像，PLI)、光学相干层析成像(OCT)， RNA-SEQ和宏观成像(例如磁共振成像)。虽然这些技术已经产生了巨大的 关于结构、分子、连接性、遗传性和 由于大脑的转录性质，到目前为止，它们对体内分析几乎没有影响。在 同样，尽管我们在定位大脑重要区域的能力上取得了很大进步 在活着的受试者中，这些能力对显微镜和神经病理学几乎没有影响。在这 我们寻求使用我们的介观成像和分析工具来消除这些障碍并 促进信息从显微镜到活体人体研究的流动，以及反过来 方向。这些新能力的影响的例子有：使用静息状态功能磁共振成像 用于指导尸检期间神经病理块提取的网络(RsFMRI)进行测试 基于网络的各种神经退行性疾病理论或使用预测的血管 分布和密度以提高fMRI的层间特异性。

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.