Algorithms for cross-scale integration and analysis

跨尺度集成和分析算法

基本信息

批准号：
10224850
负责人：
Bruce Fischl
金额：
$ 25.68万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10224850
关键词：
3-Dimensional Achievement Algorithms Anatomic Models Anatomy Architecture Atlases Autopsy Blood Vessels Brain Brain region Cognitive Collaborations Competence Cortical Dysplasia Data Databases Detection Diagnosis Diffusion Fiber Functional Magnetic Resonance Imaging Genetic Transcription Geometry Goals Histologic Human Image Label Learning Light Location Magnetic Resonance Imaging Manuals Maps Measures Microscope Microscopic Microscopy Modeling Molecular Motor Cortex Nature Network-based Neurodegenerative Disorders Neurosciences Noise Optical Coherence Tomography Optics Population Study Procedures Process Rest Scanning Side Signal Transduction Slice Source Specificity Stains Structure Supervision Surface Techniques Technology Testing Three-dimensional analysis Tissues Training Work attenuation automated segmentation base brain volume convolutional neural network deep learning density feature extraction imaging modality improved in vivo network architecture neuropathology novel polarized light theories three-dimensional modeling tool transcriptome sequencing

项目摘要

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.

摘要 - 神经科学获得的绝大多数信息是关于人脑的微观结构——认知能力和认知能力的基础神经病理过程的特定位置——通过离体分析获得从历史上看，这涉及数十年（如果不是数百年）历史的切割、染色、显微镜下的安装和成像在过去的二十年中取得了惊人的进步。人脑成像和分析方面的进步，包括显微技术的进步（例如 CLARITY1、 SWITCH）、介观（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）已经产生了巨大的影响。大量关于结构、分子、连接组、遗传和由于大脑的转录性质，它们迄今为止对体内分析影响不大。同样地，虽然我们在定位重要大脑区域的能力方面取得了巨大进步在活体受试者中，这些能力对显微镜和神经病理学影响不大。我们的项目寻求使用我们的介观成像和分析工具来消除这些障碍促进从显微镜到人体体内研究以及相反的信息流动这些新能力的影响的例子是：使用静息态功能磁共振成像。网络（rsFMRI）指导尸检过程中神经病理块的提取以进行测试各种神经退行性疾病的基于网络的理论或使用预测的血管分布和密度以提高功能磁共振成像的层流特异性。