An open-source software for Bayesian neuroimaging data analysis

用于贝叶斯神经影像数据分析的开源软件

• 批准号: 7758684
• 负责人: RONG CHEN
• 金额: $ 15.75万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7758684
• 关键词: Address; Algorithms; Alzheimer' s Disease; Anus; Brain; Brain Diseases; Brain imaging; Brain region; Clinical; Clinical dementia rating scale; Cognitive; Communities; Complement; Computer software; Data; Data Analyses; Data Files; Detection; Development; Documentation; Ensure; Extensible Markup Language; Goals; Hippocampus (Brain); Image; Image Analysis; Imagery; Informatics; Lesion; Linear Models; Magnetic Resonance; Magnetic Resonance Imaging; Manuals; Measurement; Measures; Methods; Modeling; Multivariate Analysis; Nerve Degeneration; Neurodegenerative Disorders; Noise; Output; Peer Review; Performance; Play; Process; Reading; Reporting; Research; Research Personnel; Resources; Review Literature; Role; Sample Size; Sampling; Source Code; Statistical Models; Structure; Testing; United States National Institutes of Health; Writing; base; brain volume; cognitive function; data mining; design; file format; graphical user interface; improved; interest; interoperability; morphometry; neurodevelopment; neuroimaging; neuropsychiatry; open source; programs; public health relevance; segregation; tool; usability; web site

DESCRIPTION (provided by applicant): Neuroimaging is widely used to study neurodegeneration, neurodevelopment, neuropsychiatry, and brain cognitive function. In neuroimaging-based studies, the focus of study often centers on elucidating associations between brain structure or function measurements and clinical measurements. We refer to this as neuroimaging marker detection because its goal focuses on identifying neuroimaging markers characterizing a brain disorder or a cognitive process. Statistical analysis methods play a crucial role in inferring the associations among brain structure or function measurements and the clinical variable. The dominant approach for neuroimaging marker detection is general linear model (GLM) based and mass- univariate. The limitations of this approach are its focus on functional segregation, its inability to model multivariate interactions among brain regions, the assumption of normality, the cut-off threshold problem, and the restriction of detecting linear associations. To address these limitations, we have developed a Bayesian statistical analysis method, called Graphical-model-based Multivariate Analysis (GAMMA), for neuroimaging marker detection. The differences between GAMMA and GLM-based mass-univariate methods are three-fold. First, GAMMA is nonparametric: it does not rely on statistical assumptions, such as normality. Second, GAMMA is multivariate, whereas general-linear-model based methods are mass-univariate. GAMMA has the potential to detect nonlinear, multivariate associations among brain regional structure or function measurements and the status of a clinical variable, which would not be detected by univariate approaches. Third, GAMMA is fully automated: it requires minimum user input, such as region-of-interest (ROI) specification or a predefined significance threshold. Other features of GAMMA include the explicitly modeling of spatial correlations among voxels and model stabilization mechanism. We have applied GAMMA for structural magnetic resonance (MR) image analysis, function MR analysis, and lesion-deficit analysis. GAMMA has been under-utilized due to the lack of resources to improve the usability and interoperability. The goal of the present proposal is to develop an open-source and well documented Bayesian neuroimaging data analysis software with enhanced usability, interoperability, and accessibility. The specific aims are: Specific Aim 1: Enhance the software's reporting, project management, and visualization module. This specific aim focuses on designing a graphical user interface (GUI). This GUI can present the results generated by GAMMA in a readily understandable and interpretable fashion for other investigators. This GUI also supports project management and visualization. Specific Aim 2: Enhance the software's ability to support various image and data file formats. This specific aim includes providing support for analyze, NIFTI, and DICOM image format; add support module to read clinical variables in CSV (Comma Separated Values) format and XML (Extensible Markup Language); and output the generated statistical model in a format that is widely used by data mining community. Specific Aim 3: Ensure interoperability between GAMMA and widely used neuroimaging data-analysis tools. We will develop a SPM5-GAMMA toolbox; write user-manual sections that describe how to use GAMMA to analyze the results generated by AFNI or FSL, and how to visualize GAMMA's output using AFNI or FSL; and develop a GAMMA application programming interface (API) to facilitate users to incorporate GAMMA into their software. Specific Aim 4: Completely document the application, providing user and developer's manuals. We will provide source code, toolbox, developer toolkit, sample test data, pre-compiled end user software for different platforms, and documentation in NIH Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC), in order to share it with scientific community. PUBLIC HEALTH RELEVANCE: This project centers on enhancing an open source Bayesian data mining tool to analyze neuroimaging data. This tool can identify neuroimaging markers characterizing a clinical condition in the study of neurodegeneration, neurodevelopment, neuropsychiatry, and brain cognitive function.

描述(申请人提供)：神经成像被广泛用于研究神经退行性变、神经发育、神经精神病学和大脑认知功能。在基于神经成像的研究中，研究的焦点往往集中在阐明脑结构或功能测量与临床测量之间的联系。我们将这称为神经成像标志物检测，因为它的目标是识别表征大脑疾病或认知过程的神经成像标志物。统计分析方法在推断脑结构或功能测量与临床变量之间的关联方面起着至关重要的作用。神经影像标志物检测的主流方法是基于一般线性模型(GLM)和质量单变量的方法。这种方法的局限性在于它关注的是功能分离，它不能对大脑区域之间的多变量交互作用进行建模，正态假设，截止阈值问题，以及检测线性关联的限制。为了解决这些局限性，我们开发了一种贝叶斯统计分析方法，称为基于图形模型的多元分析(GAMMA)，用于神经成像标记物的检测。伽马和基于GLM的质量-单变量方法之间的差异是三倍的。首先，伽马是非参数的：它不依赖于统计假设，如正态分布。其次，伽马是多变量的，而基于一般线性模型的方法是质量单变量的。伽马有可能检测大脑区域结构或功能测量与临床变量状态之间的非线性、多变量关联，这是单变量方法无法检测到的。第三，Gamma是完全自动化的：它需要最少的用户输入，例如感兴趣区域(ROI)规范或预定义的重要性阈值。GAMMA的其他功能包括对体素之间的空间相关性进行显式建模和模型稳定机制。我们已经将伽马应用于结构磁共振(MR)图像分析、功能MR分析和病变缺失分析。由于缺乏资源来提高可用性和互操作性，Gamma一直未得到充分利用。本提案的目标是开发一个开放源码的、文档齐全的贝叶斯神经成像数据分析软件，具有更好的可用性、互操作性和可访问性。具体目标是：具体目标1：增强软件的报表、项目管理和可视化模块。这一特定目标侧重于设计一个图形用户界面(GUI)。该图形用户界面可以以一种易于理解和解释的方式向其他研究人员展示伽马生成的结果。该图形用户界面还支持项目管理和可视化。具体目标2：增强软件支持各种图像和数据文件格式的能力。这一具体目标包括提供对Analyze、Nifti和DICOM图像格式的支持；添加支持模块以读取CSV(逗号分隔值)格式和XML(可扩展标记语言)格式的临床变量；以及以数据挖掘社区广泛使用的格式输出生成的统计模型。具体目标3：确保伽马和广泛使用的神经成像数据分析工具之间的互操作性。我们将开发SPM5-Gamma工具箱；编写用户手册部分，描述如何使用Gamma分析由Afni或FSL生成的结果，以及如何使用Afni或FSL可视化Gamma的输出；并开发Gamma应用编程接口(API)，以帮助用户将Gamma整合到他们的软件中。具体目标4：完整地记录应用程序，提供用户和开发人员手册。我们将提供源代码、工具箱、开发人员工具包、样本测试数据、针对不同平台的预编译最终用户软件，以及NIH神经成像信息学工具和资源信息中心(NITRC)的文档，以便与科学界共享。 公共卫生相关性：这个项目的中心是增强一个开源的贝叶斯数据挖掘工具，以分析神经成像数据。该工具可以在神经退行性变、神经发育、神经精神病学和大脑认知功能的研究中识别表征临床情况的神经成像标记物。