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.

项目摘要 /摘要截至2015年，世界上有9.4亿人有一定程度的视觉障碍。视觉的障碍为社会带来了巨大的经济负担。世界卫生组织估计 80％的视觉障碍是可以预防的，也可以通过治疗治疗。无创成像眼科专家已广泛使用技术来诊断和治疗视觉障碍和成像是国家眼科研究所六个核心计划领域的优先事项之一。作为无创的高空间分辨率技术，用于测量大脑活动，功能磁共振成像（fMRI）已经提供了有关视觉皮质组织的大量数据。尽管大量研究已投入在人类视觉系统中发现和验证不同的视网膜图，有限的进展一直是在开发完全考虑基础皮质的内在几何特征的软件工具中结构，构造视网膜图和地图集时强制执行差异映射，并整合两者个人和人口统计数据，以进行更强大的数据分析。在初步工作中，我们已经开发了视网膜地图的完整和可逆描述（美国专利申请号16/230,284和 63/004,721，由NSF协作研究奖DMS-1413417和DMS-1412722的支持。这个项目将继续开发和应用新颖的准形式几何形状和等级贝叶斯建模（HBM）从人类Connectome项目（HCP）获得的视网膜数据的算法，这是最大的高高分辨率视网膜数据集迄今为止。我们假设，通过结合Beltrami平滑，准信息映射和HBM，提出的方法将减少手动注释工作并最大化统计视网膜映射技术的力量。该项目的目的是：（1）开发计算方法根据Beltrami平滑，有效地在多个视觉区域上平滑视网膜图。和 Beltrami描述，所提出的方法将同时平滑偏心率和极角度视网膜 V1，V2和V3中的数据，同时保留了潜在的拓扑连续性；（2）开发计算有效地注册跨受试者的多个视觉区域的视网膜图的方法准信息映射。与以前依赖结构MRI（SMRI）或fMRI数据的工作不同，所提出的方法将同时从多个视觉区域中同时注册SMRI和fMRI数据受试者并确保差异性；（3）开发视网膜图的HBM以捕获层次结构在个人和小组级别上。提出的HBM将有助于克服测量噪声，揭示人口属性和个体差异，并在视网膜图上提供前所未有的精度分析; （4）开发和传播人类视网膜图的软件工具和地图集。这开发的开源软件工具可以扩展以分析来自视觉的患者的数据损害，但许多其他神经和精神疾病。