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: 10318145
• 负责人: Amanda Mejia
• 金额: $ 35.3万
• 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 2024-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10318145
• 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.

项目摘要：过去几十年来，非侵入性功能磁共振成像 （功能磁共振成像）彻底改变了大脑功能和组织的研究，增强了对大脑功能和组织的科学理解 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成像数据的潜力仍然存在 由于其巨大的尺寸、复杂的依赖结构和噪声而具有挑战性。个别科目分析， 这是临床护理和大脑行为关系研究所需的，由于高度 噪声水平和典型的短扫描持续时间。传统分析技术最初是由 考虑的是计算可行性，而不是最佳效率和功率。今天，统计、计算和 数据进步为统计方法的发展提供了机会，大大提高了准确性 用于团体和个人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，一种日益流行的数据 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，沿着皮质表面的测地距离是神经元差异的有意义的度量。 激活，与传统体积 fMRI 数据中的欧几里得距离不同，使得 csfMRI 最适合在空间中使用 模型。其次，csfMRI 数据实现了受试者皮质区域更准确的对齐，从而提高了 小组研究的准确性并提供跨学科借用力量的机会。该项目重点 开发计算高效的 csfMRI 数据贝叶斯统计方法。我们解决两个 具体的科学目标：（1）估计大脑响应任务或刺激的激活，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），我们提出了一种空间贝叶斯模型，该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) 采用空间统计的最新发展和 用于准确有效的模型估计的贝叶斯计算，(c) 利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，并且（d）提出 一种高效且有原则的多主题分析方法。我们还提出了一些扩展，以允许 非平稳且各向同性的空间依赖性。对于（2），我们提出了分层贝叶斯 独立成分分析（ICA）模型，通过经验先验从总体中借用力量， 这是根据大型、公开的功能磁共振成像数据集估计的。经验先验的使用非常广泛 计算上有利。最后，我们将该模型与提出的空间贝叶斯方法结合起来 为目标 1 开发的任务激活，通过将适合 csfMRI 数据的空间先验纳入 分层 ICA 模型。我们进行模拟和可靠性研究来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伽雷氏病。

项目成果

Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference.

• DOI: 10.1073/pnas.2203020119
• 发表时间: 2022-08-09
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1

Psilocybin induces spatially constrained alterations in thalamic functional organizaton and connectivity.

• DOI: 10.1016/j.neuroimage.2022.119434
• 发表时间: 2022-10-15
• 期刊: NEUROIMAGE
• 影响因子: 5.7
• 作者: Gaddis, Andrew;Lidstone, Daniel E.;Nebel, Mary Beth;Griffiths, Roland R.;Mostofsky, Stewart H.;Mejia, Amanda F.;Barrett, Frederick S.
• 通讯作者: Barrett, Frederick S.