Bayesian Spatial Point Process Modeling of Neuroimage Data

神经图像数据的贝叶斯空间点过程建模

• 批准号: 8296951
• 负责人: Timothy D Johnson
• 金额: $ 30.85万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-04-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8296951
• 关键词: 3-Dimensional; Accounting; Address; Brain; Clinical; Code; Complex; Computer software; Data; Data Set; Development; Diagnosis; Disease; Disease Progression; Explosion; Functional Magnetic Resonance Imaging; Heterogeneity; Human; Image; Imaging Techniques; Individual; Knowledge; Linear Models; Location; Meta-Analysis; Methods; Modeling; Multiple Sclerosis; Multiple Sclerosis Lesions; Neurodegenerative Disorders; Neurosciences; Pattern; Population; Process; Research; Research Design; Research Personnel; Signal Transduction; Statistical Methods; Statistical Models; Structure; Study Subject; Techniques; Time; Variant; Work; base; design; longitudinal analysis; neuroimaging; neuropsychiatry; platform-independent; research study; statistics; tool

DESCRIPTION (provided by applicant): Functional neuroimaging has become an essential tool for non-invasively studying the brain of normal and clinical populations. The volume of research using neuroimaging methods has been growing dramatically in the last 20 years. This explosion of research has been supported by a simple and computationally efficient method known as the mass univariate approach (MUA). Despite its common use, however, there are several limitation to the MUA: 1) the inability to infer on the exact location of an effect; 2) the inability to properly account for spatial heterogeneity amongst subjects and the spatial structure of the effect; and 3) it is not designed for point pattern data, such as that from a neuroimaging meta analysis study, nor the binary valued data from multiple sclerosis lesions. To overcome these limitation of the MUA, we are proposing the development of Bayesian statistical models that explicitly address these issues. Specially, we will develop hierarchical Bayesian spatial point process models to analyze neuroimaging coordinate-level data (e.g. when only the peak location of the activation centers are available such as is the case in neuroimaging meta analysis data), binary imaging data (such as that obtained from multiple sclerosis lesions) and hierarchical Bayesian spatial process/spatial point process models for neuroimage voxel-level data (e.g. when the entire contrast or t-statistic image is available on a group of subjects). More recently, neuroscientists have been collecting longitudinal data, as well as cross-sectional data, with the intent of studying progression of disease. There is little work done on the analysis of longitudinal neuroimaging data, so we further propose to extend our modeling to incorporate the longitudinal aspect in the data as well as the cross-sectional aspect. We will implement and optimize our methods and make the software available to the public. One notable feature of this work is that the models can be used to help predict/diagnose neuropsychiatric/neurodegenerative diseases and disorders. Thus our models will assist in understanding the development of neuropsychiatric and neurodegenerative disorders, as well as normal brain development, that cannot be answered by current methods/models. This, in turn, will aid in our understanding of the human brain in normal and diseased states. PUBLIC HEALTH RELEVANCE: Over the past few decades our knowledge of the brain and its associated diseases and disorders has dramatically increased due in part to high-tech imaging techniques. However, the standard practice is to use very basic statistical methods to analyze the large and complex data sets produced by these techniques. In this project we will develop advanced statistical models and associated software that will allow neuroscience researchers to answer questions that cannot be addressed with the basic methods in current use, and this should advance our understanding neuropsychiatric and neurodegenerative disorders.

描述（由申请人提供）：功能性神经影像学已成为非侵入性研究正常和临床种群大脑的重要工具。在过去的20年中，使用神经成像方法的研究量一直在显着增长。这项研究的爆炸得到了一种简单且计算上有效的方法，称为质量单变量方法（MUA）。但是，尽管有一些常见的用途，但MUA有几个限制：1）无法推断效果的确切位置； 2） 无法正确说明受试者之间的空间异质性和效果的空间结构； 3）它不是为点模式数据而设计的，例如来自神经成像元分析研究，也不是从多发性硬化病变中的二元估值数据。为了克服对MUA的这些局限性，我们提出了明确解决这些问题的贝叶斯统计模型的发展。 Specially, we will develop hierarchical Bayesian spatial point process models to analyze neuroimaging coordinate-level data (e.g. when only the peak location of the activation centers are available such as is the case in neuroimaging meta analysis data), binary imaging data (such as that obtained from multiple sclerosis lesions) and hierarchical Bayesian spatial process/spatial point process models for neuroimage voxel-level数据（例如，一组受试者都可以使用整个对比度或T统计图像时）。更多的 最近，神经科学家一直在收集纵向数据以及横截面数据，目的是研究疾病的进展。在分析纵向神经影像数据方面，几乎没有完成的工作，因此我们进一步建议扩展建模，以将纵向方面纳入数据以及横截面方面。我们将实施和优化我们的方法，并向公众提供该软件。这项工作的一个值得注意的特征是，这些模型可用于帮助预测/诊断神经精神/神经退行性疾病和疾病。因此，我们的模型将有助于理解神经精神病学和神经退行性疾病的发展，以及正常的大脑发育，这些疾病无法通过当前的方法/模型来回答。反过来，这将有助于我们在正常和患病状态下对人脑的理解。 公共卫生相关性：在过去的几十年中，我们对大脑及其相关疾病和疾病的了解部分归因于高科技成像技术。但是，标准实践是使用非常基本的统计方法来分析这些技术产生的大型且复杂的数据集。在这个项目中，我们将开发高级统计模型和相关软件，使神经科学研究人员能够回答无法用当前使用中基本方法解决的问题，这应该可以促进我们的理解神经精神病学和神经退行性疾病。