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

项目摘要/摘要 截至2015年，全球有9.4亿人存在不同程度的视力障碍。视觉 减值给社会带来了相当大的经济负担。世界卫生组织估计 80%的视力障碍要么可以预防，要么可以通过治疗治愈。无创成像 眼科专家广泛使用这些技术来诊断和治疗视力障碍和 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 用于测量脑活动的高空间分辨率技术--功能磁共振成像 提供了大量关于视觉皮质组织的数据。尽管已有大量研究致力于 在人类视觉系统中发现和验证不同的视网膜定位图方面，进展有限 在开发软件工具时制作，该软件工具充分考虑底层皮质的固有几何特征 结构，在构造视网膜地图和地图集时强制执行微分同胚映射，并将两者结合起来 个人和人口统计，以便进行更可靠的数据分析。在前期工作中，我们开发了一种 视网膜定位图的完整和可逆描述(美国专利申请号16/230,284和 63/004,721，由美国国家科学基金会合作研究奖dms-1413417和dms-1412722支持)。这个项目 将继续开发和应用新的准共形几何和分层贝叶斯建模 从人类连接组计划(HCP)获得的视网膜复制数据的(HBM)算法，最大的高 到目前为止的分辨率视网膜复制数据集。我们假设，通过结合Beltrami光滑化、准共形 映射和HBM，所提出的方法将减少人工标注工作，并最大化统计 视网膜定位技术的力量。该项目的目标是：(1)开发计算方法以 基于Beltrami平滑，有效地平滑跨多个视觉区域的视网膜定位图。使用 Beltrami描述，提出的方法可以同时平滑偏心和极角视网膜复制 V1、V2和V3中的数据，同时保持底层拓扑连续性；(2)开发计算 有效配准受试者多个视觉区域的视网膜定位图的方法 拟共形映射。与以前仅依赖结构磁共振(SMRI)或功能磁共振数据的工作不同， 所提出的方法将同时配准来自多个视觉区域的sMRI和fMRI数据 (3)建立视网膜定位图的HBM，以捕捉层次结构 无论是个人层面还是群体层面。据透露，拟议的HBM将有助于克服测量噪声 群体属性和个体差异，并在视网膜定位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定位图的软件工具和地图集。这个 开发的开源软件工具可以扩展到分析来自患者的数据，不仅可视化 损害，但许多其他神经和精神障碍。

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.