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：在过去的几十年中，非侵入性功能磁共振成像 （fMRI）彻底改变了对大脑功能和组织的研究，增强了对 正常的大脑功能，发育，衰老和疾病。然而，利用fMRI数据的全部潜力仍然存在 由于其大小，复杂的依赖性结构和噪声而挑战。分析个体主题， 临床护理和研究脑行为关系所需的所需，由于高 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 计算可行性是考虑到最佳效率和功率。今天，统计，计算和 数据进步为开发统计方法的开发提供了大大提高准确性的机会 用于小组和个人功能磁共振成像分析。特别是皮质表面fMRI（CSFMRI）数据，这是一个越来越流行的 将皮质灰质投影到二维歧管的格式提供了两个重要的好处。 首先，沿皮质表面的大地距离是神经元不同的有意义的衡量标准 激活与传统体积fMRI数据中的欧几里得距离不同，使CSFMRI最佳用于空间 型号。其次，CSFMRI数据可实现受试者皮质区域的更准确对齐，从而改善 小组研究的精确度，并提供了跨科目借用力量的机会。这个项目集中在 关于CSFMRI数据的计算高效贝叶斯统计方法的开发。我们解决两个 特定的科学目标：（1）响应任务或刺激的大脑中激活的估计，以及（2） 识别大脑的功能区域，在没有特定任务的情况下倾向于一起激活。 对于（1），我们提出了一个空间贝叶斯模型，该模型解决了先前提出模型的局限性 （a）利用CSFMRI数据而不是体积fMRI，（b）采用空间统计和 贝叶斯计算以进行准确有效的模型估计，（c）利用有效的偏移方法 根据关节（而不是边缘）后部分布确定激活区域，以及（d）提出 一种有效且原则的多主体分析方法。我们还提出了几个扩展，以允许 空间依赖性不是固定和各向同性的。对于（2），我们提出了一个分层贝叶斯 独立的组件分析（ICA）模型，通过经验先验从人口借用强度， 这是从大型的fMRI数据集中估算的。经验先验的使用非常 计算有利。最后，我们将该模型与建议的空间贝叶斯方法相结合 为AIM 1开发的任务激活是通过将适用于CSFMRI数据的空间合并到 分层ICA模型。我们进行仿真和可靠性研究以验证提出的方法，并 将它们与传统方法进行比较。我们还将提出的方法应用于自闭症谱系的研究 疾病和肌萎缩性侧面硬化症或lou gehrig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.