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

• 批准号: 10473754
• 负责人: ZHONG-LIN LU
• 金额: $ 37.35万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 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/10473754
• 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; 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; Pathway Analysis; Patients; Perception; Persons; Play; Population; Population Statistics; Procedures; Property; Psychophysics; Publishing; 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 impairment; Visual system structure; Work; World Health Organization; base; connectome; extrastriate visual cortex; geometric methodologies; human data; imaging study; 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; vision development; visual stimulus

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.

项目总结/摘要 截至2015年，全世界有9.4亿人患有某种程度的视力障碍。视觉 残疾给社会造成了相当大的经济负担。世界卫生组织估计 80%的视力障碍是可以预防或通过治疗治愈的。非侵入性成像 眼科专家已经广泛使用这些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重点之一。作为一种无创 测量大脑活动的高空间分辨率技术，功能性磁共振成像（fMRI） 提供了大量关于视觉皮层组织的数据。尽管有大量的研究 为了发现和验证人类视觉系统中不同的视网膜分布图， 在开发软件工具，充分考虑了内在的几何特征的底层皮层 结构，在构建视网膜定位图和地图集时执行非对称映射，并将两者整合 个人和人口统计，以便进行更有力的数据分析。在初步工作中，我们开发了一个 视网膜定位图的完整和可逆的描述（美国专利申请No. 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 我将继续开发和应用新的准共形几何和分层贝叶斯建模 (HBM)算法retinotopy数据从人类连接组计划（HCP），最大的高 resolution retinotopy dataset to date.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人工注释工作，并最大限度地提高统计 视网膜定位技术的力量。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拓扑图。与 Beltrami描述，所提出的方法将同时平滑偏心率和极角视网膜病变 V1、V2和V3中的数据，同时保留底层拓扑连续性;（2）开发计算 有效地配准受试者的多个视觉区域的视网膜定位图的方法 拟共形映射与以前的工作，无论是依赖于结构磁共振成像（sMRI）或功能磁共振成像数据只， 所提出的方法将同时记录来自多个视觉区域的sMRI和fMRI数据， （3）开发视网膜定位图的HBM，以捕获层次结构 在个人和群体层面上。拟议的HBM将有助于克服测量噪声，揭示 群体特性和个体差异，并提供前所未有的视网膜定位图的准确性 （4）开发和传播人类视网膜定位图的软件工具和地图集。的 开发的开源软件工具可以扩展到分析患者的数据， 损伤，但许多其他神经和精神疾病。