Bayesian methods for cortical surface neuroimaging data

用于皮质表面神经影像数据的贝叶斯方法

• 批准号: 10066355
• 负责人: Amanda Mejia
• 金额: $ 35.56万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2022-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10066355
• 关键词: ALS patients; Address; Aging; Amyotrophic Lateral Sclerosis; Area; Astronomy; Bayesian Analysis; Bayesian Method; Bayesian Modeling; Big Data; Brain; Complex; Computational Technique; Computer Analysis; Data; Data Set; Dependence; Development; Disease; Euclidean Space; Exhibits; Functional Magnetic Resonance Imaging; Genomics; Individual; Joints; Linear Models; Location; Measures; Methods; Mind; Modeling; Neurologic; Neurons; Noise; Operative Surgical Procedures; Population; Prevention; Research; Rest; Sample Size; Scanning; Series; Statistical Methods; Stimulus; Structure; Surface; Techniques; Time; Tissues; Work; autism spectrum disorder; base; behavioral study; brain behavior; clinical care; flexibility; functional group; gray matter; imaging study; improved; independent component analysis; insight; model design; multidimensional data; nervous system disorder; neuroimaging; personalized medicine; response; simulation; statistics; task analysis; two-dimensional

PROJECT SUMMARY: Over the past several decades, non-invasive functional magnetic resonance imaging (fMRI) has revolutionized the study of brain function and organization, enhancing scientific understanding of normal brain function, development, aging and disease. Yet leveraging the full potential of fMRI data remains challenging due to its massive size, complex dependence structure and noise. Analysis of individual subjects, which is needed for clinical care and the study of brain-behavior relationships, is particularly difficult due to high noise levels and typical short scan durations. Traditional analysis techniques were originally developed with computational feasibility in mind, rather than optimal efficiency and power. Today, statistical, computational and data advances provide opportunities for development of statistical methods with substantially improved accuracy for group and individual fMRI analysis. In particular, cortical-surface fMRI (csfMRI) data, an increasingly popular format in which the cortical gray matter is projected to a 2-dimensional manifold, offers two important benefits. First, geodesic distances along the cortical surface are a meaningful measure of dissimilarity in neuronal activation, unlike Euclidean distances in traditional volumetric fMRI data, making csfMRI optimal for use in spatial models. Second, csfMRI data achieves more accurate alignment of subjects' cortical areas, thus improving the precision of group studies and providing an opportunity to borrow strength across subjects. This project focuses on the development of computationally efficient Bayesian statistical methods for csfMRI data. We address two specific scientific objectives: (1) estimation of activation in the brain in response to a task or stimulus, and (2) identification of functional areas of the brain, which tend to activate together in the absence of a particular task. For (1), we propose a spatial Bayesian model that addresses the limitations of previously proposed models by (a) utilizing csfMRI data rather than volumetric fMRI, (b) employing recent developments in spatial statistics and Bayesian computation for accurate and efficient model estimation, (c) utilizing an efficient excursions set method to identify areas of activation based on the joint (rather than the marginal) posterior distribution, and (d) proposing an efficient and principled multi-subject analysis approach. We also propose several extensions to allow for spatial dependencies that are not stationary and isotropic. For (2), we propose a hierarchical Bayesian independent component analysis (ICA) model that borrows strength from the population through empirical priors, which are estimated from large, publicly available fMRI datasets. The use of empirical priors is very computationally advantageous. Finally, we combine this model with the proposed spatial Bayesian approach to task activation developed for Aim 1 by incorporating a spatial prior appropriate for csfMRI data into the hierarchical ICA model. We conduct simulation and reliability studies to validate the proposed methods and compare them with traditional approaches. We also apply the proposed methods to studies of autism spectrum disorder and amyotrophic lateral sclerosis or Lou Gehrig's disease.

项目概述：在过去的几十年里，非侵入性功能磁共振成像 功能磁共振成像（fMRI）彻底改变了大脑功能和组织的研究，提高了对大脑功能的科学理解。 正常的大脑功能、发育、衰老和疾病。然而，利用功能磁共振成像数据的全部潜力仍然是 由于其巨大的尺寸、复杂的依赖结构和噪声，具有挑战性。对个别受试者的分析， 这是临床护理和大脑行为关系的研究所需要的，由于高血压， 噪声水平和典型的短扫描持续时间。传统的分析技术最初是由 计算可行性，而不是最佳效率和功率。今天，统计，计算和 数据的进步为发展准确性大大提高的统计方法提供了机会 进行群体和个体的功能磁共振成像分析特别是，皮质表面功能磁共振成像（csfMRI）数据，一个越来越受欢迎的 将皮质灰质投影到二维流形的格式提供了两个重要的好处。 首先，沿着皮层表面的测地线距离沿着是神经元中的相异性的有意义的度量。 与传统体积fMRI数据中的欧几里得距离不同，csfMRI最适合用于空间 模型其次，csfMRI数据实现了受试者皮层区域的更准确对齐，从而改善了受试者的大脑皮层功能。 小组研究的精确性，并提供了一个机会，借用跨学科的力量。该项目重点 关于csfMRI数据的计算效率贝叶斯统计方法的发展。我们解决两个 具体的科学目标：（1）估计大脑对任务或刺激的反应，以及（2） 识别大脑的功能区域，这些区域在没有特定任务的情况下往往会一起激活。 对于（1），我们提出了一个空间贝叶斯模型，通过以下方式解决了先前提出的模型的局限性： (a)利用csfMRI数据而不是体积fMRI，（B）利用空间统计学的最新发展 贝叶斯计算，用于准确和有效的模型估计，（c）利用有效的偏移集方法 基于联合（而不是边缘）后验分布来识别激活区域，以及（d）提出 一种高效、有原则的多学科分析方法。我们还提出了几个扩展，以允许 空间依赖性不是静止的和各向同性的。对于（2），我们提出了一个分层贝叶斯 独立成分分析（伊卡）模型，通过经验先验从总体中借用力量， 这是从大型的公开可用的功能磁共振成像数据集估计的。经验先验的使用非常 计算上的优势。最后，我们结合联合收割机这个模型与建议的空间贝叶斯方法， 通过将适用于csfMRI数据的空间先验纳入目标1的任务激活 层次伊卡模型我们进行仿真和可靠性研究，以验证所提出的方法， 将其与传统方法进行比较。我们还将所提出的方法应用于自闭症谱系的研究 疾病和肌萎缩侧索硬化症或Lou Gehrig病。