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Algorithms for cross-scale integration and analysis

跨尺度集成和分析算法

基本信息

批准号：
10038179
负责人：
Bruce Fischl
金额：
$ 26.21万
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/10038179
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：神经科学已经获得的绝大多数关于神经系统的信息