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（由申请人提供）：神经影像学广泛用于研究神经退行性，神经发育，神经精神病学和脑认知功能。 在基于神经影像学的研究中，研究重点通常集中于阐明大脑结构或功能测量和临床测量之间的关联。 我们将其称为神经影像学标记的检测，因为其目标着重于识别表征脑疾病或认知过程的神经影像学标记。 统计分析方法在推断大脑结构或功能测量和临床变量之间的关联方面起着至关重要的作用。 神经成像标记检测的主要方法是基于一般线性模型（GLM）和群众。 这种方法的局限性在于它的重点是功能隔离，无法建模大脑区域之间的多元相互作用，正态性的假设，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），用于神经成像记录。 基于伽马和GLM的质量 - 单变量方法之间的差异为三倍。 首先，伽玛是非参数：它不依赖统计假设，例如正态性。 其次，伽玛是多元的，而一般线性模型的方法是质量 - 单变量。 伽马有可能检测大脑区域结构或功能测量的非线性多元关联以及临床变量的状态，而单变量方法无法检测到。 第三，伽玛是完全自动化的：它需要最少的用户输入，例如利息区域（ROI）规范或预定义的显着性阈值。 伽马的其他特征包括对体素和模型稳定机制之间空间相关性的明确建模。 我们已经应用了伽玛进行结构磁共振（MR）图像分析，功能MR分析和病变缺陷分析。 由于缺乏提高可用性和互操作性的资源，伽玛被释放得不足。 本提案的目的是开发开源且文献良好的贝叶斯神经影像学数据分析软件，具有增强的可用性，互操作性和可访问性。 具体目的是：特定目标1：增强软件的报告，项目管理和可视化模块。 该特定目的重点是设计图形用户界面（GUI）。 该GUI可以以易于理解和可解释的方式为其他研究人员提供伽马产生的结果。 该GUI还支持项目管理和可视化。 特定目标2：增强软件支持各种图像和数据文件格式的能力。 这个具体目的包括为分析，NIFTI和DICOM图像格式提供支持；添加支持模块以读取CSV（逗号分离值）格式和XML（可扩展标记语言）中的临床变量；并以数据挖掘社区广泛使用的格式输出生成的统计模型。 特定目标3：确保伽马与广泛使用的神经影像学数据分析工具之间的互操作性。 我们将开发一个SPM5-gamma工具箱；编写用户手册部分，描述如何使用伽玛分析AFNI或FSL生成的结果，以及如何使用AFNI或FSL可视化伽马的输出；并开发伽马应用程序编程界面（API），以促进用户将伽马纳入其软件。 特定目标4：完全记录应用程序，提供用户和开发人员的手册。 我们将提供源代码，工具箱，开发人员工具包，示例测试数据，用于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交换所（NITRC）中的文档，以便与科学界共享。 公共卫生相关性：该项目以增强开源的贝叶斯数据挖掘工具为中心，以分析神经成像数据。 该工具可以确定在神经退行性，神经发育，神经精神病学和脑认知功能研究中表征临床状况的神经影像学标记。