Bayesian methods for cortical surface neuroimaging data

项目概述：在过去的几十年里，无创功能磁共振成像 (功能磁共振成像)使大脑功能和组织的研究发生了革命性的变化，提高了对 正常的大脑功能、发育、衰老和疾病。然而，充分利用功能磁共振数据的潜力仍然存在 由于其庞大的体积、复杂的依赖结构和噪声，具有挑战性。对个别主题的分析， 这是临床护理和研究大脑-行为关系所需要的，由于高 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用 考虑计算的可行性，而不是最优的效率和功率。今天，统计、计算和 数据的进步为统计方法的发展提供了机会，大大提高了准确性 用于群体和个体的fMRI分析。尤其是皮质表面功能磁共振成像(CsfMRI)数据，越来越受欢迎 皮质灰质被投射到2维流形的格式提供了两个重要的好处。 首先，沿皮质表面的测地线距离是神经元差异的一个有意义的量度。 激活，与传统体积fMRI数据中的欧几里得距离不同，使csfMRI成为空间应用的最佳选择 模特们。其次，csfMRI数据实现了受试者大脑皮层区域更精确的对准，从而改善了 小组研究的精确度，并提供了跨学科借力的机会。这个项目的重点是 关于csfmri数据计算高效的贝叶斯统计方法的发展。我们解决了两个问题 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，这些功能区往往在没有特定任务的情况下一起激活。 对于(1)，我们提出了一个空间贝叶斯模型，它通过以下方式解决了以前提出的模型的局限性 (A)利用csfMRI数据而不是体积功能磁共振数据，(B)利用空间统计学的最新发展和 用于准确和有效的模型估计的贝叶斯计算，(C)使用有效的偏移集方法 根据联合(而不是边缘)后验分布确定激活区域，以及(D)建议 高效和原则性的多主题分析方法。我们还提出了几个延期方案，以允许 空间依赖关系不是固定的和各向同性的。对于(2)，我们提出了一种分层贝叶斯 独立成分分析(ICA)模型通过经验先验从总体中借用力量， 这些数据是从公开可用的大型fMRI数据集中估计出来的。经验先验的使用是非常重要的 在计算上是有利的。最后，我们将该模型与所提出的空间贝叶斯方法相结合。 通过将适用于csfMRI数据的空间先验合并到 分层ICA模型。我们进行了模拟和可靠性研究，以验证所提出的方法和 将它们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卢格里克病。

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.