Hierarchical Bayesian Analysis of Retinotopic Maps of the Human Visual Cortex with Conformal Geometry

具有共形几何的人类视觉皮层视网膜专题图的分层贝叶斯分析

基本信息

批准号：
10701881
负责人：
ZHONG-LIN LU
金额：
$ 38.63万
依托单位：
ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10701881
关键词：
Adopted Age related macular degeneration Algorithms Area Atlases Award Bayesian Analysis Bayesian Method Bayesian Modeling Brain Brain imaging Cerebral cortex Cognition Cognitive Collaborations Communities Computing Methodologies Cortical Blindness Data Data Analyses Data Set Development Diagnosis Disease Early Diagnosis Early treatment Economic Burden Ensure Eye Functional Magnetic Resonance Imaging Geometry Glaucoma Human Image Imaging Techniques Individual Individual Differences Knowledge Legal patent Magnetic Resonance Imaging Manuals Maps Mathematics Measurement Measures Mental disorders Methods Modeling National Eye Institute Neurodegenerative Disorders Neurons Noise Patients Perception Persons Play Population Population Statistics Procedures Property Psychophysics Publishing Qualifying Rehabilitation therapy Research Research Personnel Resolution Retina Role Scheme Scientist Series Signal Pathway Signal Transduction Societies Software Tools Source Code Specialist Structure Surface Techniques Treatment Cost Vision Vision Disorders Visual Visual Cortex Visual System Visual impairment Work World Health Organization connectome extrastriate visual cortex geometric methodologies human data improved morphometry nervous system disorder neuroimaging non-invasive imaging novel online resource open source pre-clinical preservation productivity loss programs rehabilitation strategy response retinotopic software development software systems tool visual stimulus

项目摘要

PROJECT SUMMARY / ABSTRACT As of 2015 there were 940 million people with some degree of visual impairment in the world. Visual impairments generate considerable economic burden for the society. The World Health Organization estimates that 80% of visual impairments are either preventable or curable with treatment. Noninvasive imaging techniques have been used extensively by eye specialists for diagnosis and treatment of visual disorders and imaging is one of the priorities in the six core program areas of the National Eye Institute. As a noninvasive high spatial resolution technique for measuring brain activities, functional magnetic resonance imaging (fMRI) has provided a wealth of data on visual cortical organizations. Although numerous studies have been devoted to discovering and validating different retinotopic maps in the human visual system, limited progress has been made in developing software tools that fully consider the intrinsic geometrical features of the underlying cortical structures, enforce diffeomorphic mapping when constructing retinotopic maps and atlases, and integrate both individual and population statistics for more robust data analysis. In preliminary work, we have developed a complete and invertible description of retinotopic maps (U.S. Patent Application Nos. 16/230,284 and 63/004,721, supported by NSF collaborative research awards DMS-1413417 and DMS-1412722). This project will continue developing and applying novel quasiconformal geometry and hierarchical Bayesian modeling (HBM) algorithms to retinotopy data obtained from the Human Connectome Project (HCP), the largest high resolution retinotopy dataset to date. We hypothesize that, by combining Beltrami smoothing, quasiconformal mapping and HBM, the proposed approach will reduce manual annotation work and maximize the statistical power of retinotopic mapping techniques. The project aims to: (1) Develop computational methods to effectively smooth retinotopic maps across multiple visual areas based on Beltrami smoothing. With Beltrami descriptions, the proposed method will simultaneously smooth eccentricity and polar angle retinotopy data in V1, V2 and V3, while preserving the underlying topological continuity; (2) Develop computational methods to effectively register retinotopic maps of multiple visual areas across subjects with quasiconformal mapping. Unlike previous work that relied on either structural MRI (sMRI) or fMRI data only, the proposed method will simultaneously register both sMRI and fMRI data from multiple visual areas across subjects and ensure diffeomorphism; (3) Develop an HBM of the retinotopic maps to capture the hierarchy at both the individual and group levels. The proposed HBM will help overcome measurement noise, reveal both population properties and individual differences, and offer unprecedented accuracy on retinotopic map analysis; (4) Develop and disseminate software tools and atlases of human retinotopic maps. The developed open-source software tools can be extended to analyze data from patients with not only visual impairment but many other neurological and psychiatric disorders.

项目概要/摘要截至2015年，全球有9.4亿人患有某种程度的视力障碍。视觉的损伤给社会带来相当大的经济负担。世界卫生组织估计 80% 的视力障碍可以通过治疗预防或治愈。无创成像眼科专家广泛使用该技术来诊断和治疗视觉障碍成像是国家眼科研究所六个核心项目领域的优先事项之一。作为一种非侵入性用于测量大脑活动的高空间分辨率技术、功能磁共振成像 (fMRI) 提供了有关视觉皮层组织的丰富数据。尽管大量研究致力于在发现和验证人类视觉系统中不同的视网膜专题图方面，进展有限开发充分考虑底层皮质固有几何特征的软件工具结构，在构建视网膜专题图和图集时强制执行微分同态映射，并将两者集成个人和人口统计数据以进行更可靠的数据分析。在前期工作中，我们开发了一个视网膜专题图的完整且可逆的描述（美国专利申请号 16/230,284 和 63/004,721，由 NSF 合作研究奖 DMS-1413417 和 DMS-1412722 支持。这个项目将继续开发和应用新颖的拟共形几何和分层贝叶斯建模 (HBM) 算法对从人类连接组计划 (HCP) 获得的视网膜局部数据进行分析，这是最大的高迄今为止的视网膜局部分辨率数据集。我们假设，通过结合 Beltrami 平滑、拟共形映射和 HBM，所提出的方法将减少手动注释工作并最大化统计数据视网膜专题图技术的力量。该项目旨在： (1) 开发计算方法基于 Beltrami 平滑，有效地平滑跨多个视觉区域的视网膜主题图。和 Beltrami 描述，所提出的方法将同时平滑偏心率和极角视网膜适应 V1、V2 和 V3 中的数据，同时保留底层拓扑连续性； (2) 发展计算能力有效记录受试者多个视觉区域的视网膜专题图的方法拟共形映射。与之前仅依赖结构 MRI (sMRI) 或 fMRI 数据的工作不同，该方法将同时记录来自多个视觉区域的 sMRI 和 fMRI 数据主题并确保微分同态； (3) 开发视网膜专题图的 HBM 以捕获层次结构在个人和团体层面上。所提出的 HBM 将有助于克服测量噪声，揭示群体属性和个体差异，并在视网膜专题图上提供前所未有的准确性分析; (4) 开发和传播人类视网膜专题图的软件工具和图册。这开发的开源软件工具可以扩展到分析来自患者的数据，而不仅仅是视觉损害，但许多其他神经和精神疾病。