A Unified Framework for Flexible Brain Image Analysis

灵活脑图像分析的统一框架

• 批准号: 7764786
• 负责人: VINCE D CALHOUN
• 金额: $ 47.27万
• 依托单位: THE MIND RESEARCH NETWORK
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-04-01 至 2012-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7764786
• 关键词: Algorithms; Alzheimer' s Disease; Attention; Auditory; Automobile Driving; Behavior; Bipolar Disorder; Brain; Brain imaging; Classification; Clinical; Cognitive; Commit; Communities; Complex; Computer software; Data; Data Analyses; Data Set; Databases; Dependency; Development; Disease; Educational process of instructing; Event; Exhibits; Functional Magnetic Resonance Imaging; Funding; Goals; Hand; Hyperactive behavior; Image Analysis; Imagery; Individual; Institutes; Internet; Knowledge; Lead; Life; Linear Models; Link; Measures; Methods; Mind; Modeling; Monitor; Motion; Motivation; Motor; Noise; Patients; Pattern; Performance; Play; Principal Component Analysis; Property; Publications; Relative (related person); Research; Rest; Role; Scanning; Schizophrenia; Sensitivity and Specificity; Short-Term Memory; Signal Transduction; Sorting - Cell Movement; Source; Time; Validation; Visual; Work; base; behavior measurement; blind; design; flexibility; improved; independent component analysis; interest; operation; psychopathic personality; response; simulation; spatiotemporal; tool

DESCRIPTION (provided by applicant): Data driven methods are being increasingly used to analyze brain imaging data. FMRI analyses can be put on an analytic spectrum with heavily model-based approaches (like the general linear model (GLM) implemented in the SPM software) on one end and flexible data-driven approaches like independent component analysis (ICA), principal component analysis (PCA), or clustering on the other end. In between there is a gap, which we and others have been trying to fill. In particular, methods such as ICA are particularly useful for reducing the multivariate fMRI problem down to one that is both tractable and also enables the incorporation of prior information. In the first period of this competing renewal, we focused our efforts upon developing ICA of fMRI methods which would be suitable for making group inferences, and which would allow the incorporation of prior information, hence moving from a 'blind' ICA approach to a semi-blind ICA approach. Despite the progress we have made, there is still considerable work to be done in the analysis of fMRI data with ICA. In this competing renewal, we propose to continue and significantly expand this work. First, we will extend our semi-blind ICA (sbICA) framework to provide a general framework for incorporating prior information from multiple spatial and temporal sources. In the second aim we will focus upon statistical inference and develop a framework for integrating the relevant functional components. In the third aim, we will validate the algorithms in aims 1 and 2, including using fMRI data collected on multiple days from a variety of paradigms. In this aim we develop a decision mechanism for selecting the best combination of methods given a particular problem. For the fourth aim, we will apply our methods to data collected during four well-studied paradigms in healthy controls and patients with schizophrenia. Our final aim involves the continuing development of our GIFT toolbox, and incorporation of the above algorithms, constraint selection mechanisms, and visual interfaces into the software. The successful completion of this research will provide a powerful set of tools for the research community to increase the sensitivity and specificity of BOLD analysis methods by drawing upon the strengths of both model-based and data-driven approaches. These tools will also provide a way to study the inter-relationship among functional networks in a flexible manner. This has application not only in schizophrenia but in many other diseases such as Alzheimer's, attention deficit hyperactivity, and psychopathy.

描述（由申请人提供）：数据驱动方法越来越多地用于分析脑成像数据。 FMRI 分析可以放在一个分析范围内，一方面采用大量基于模型的方法（如 SPM 软件中实现的一般线性模型 (GLM)），另一方面采用灵活的数据驱动方法，如独立成分分析 (ICA)、主成分分析 (PCA) 或聚类。两者之间存在着一条鸿沟，我们和其他人一直在努力填补这一鸿沟。特别是，诸如 ICA 之类的方法对于将多变量 fMRI 问题减少到既易于处理又能够合并先验信息的问题特别有用。在这一竞争性更新的第一阶段，我们集中精力开发 fMRI 方法的 ICA，该方法适合进行群体推理，并且允许合并先验信息，从而从“盲”ICA 方法转向半盲 ICA 方法。尽管我们已经取得了进展，但在使用 ICA 分析 fMRI 数据方面仍有大量工作要做。在这次竞争更新中，我们建议继续并大幅扩展这项工作。首先，我们将扩展我们的半盲 ICA (sbICA) 框架，以提供一个整合来自多个空间和时间源的先验信息的通用框架。在第二个目标中，我们将专注于统计推断并开发一个用于集成相关功能组件的框架。在第三个目标中，我们将验证目标 1 和 2 中的算法，包括使用从各种范例中多天收集的 fMRI 数据。为此，我们开发了一种决策机制，用于在给定特定问题的情况下选择最佳方法组合。对于第四个目标，我们将把我们的方法应用于健康对照组和精神分裂症患者的四个经过充分研究的范式中收集的数据。我们的最终目标是持续开发 GIFT 工具箱，并将上述算法、约束选择机制和可视化界面纳入软件中。这项研究的成功完成将为研究界提供一套强大的工具，通过利用基于模型和数据驱动方法的优势来提高 BOLD 分析方法的敏感性和特异性。这些工具还将提供一种以灵活的方式研究功能网络之间相互关系的方法。这不仅适用于精神分裂症，还适用于许多其他疾病，如阿尔茨海默病、注意力缺陷多动症和精神病。