A Unified Framework for Flexible Brain Image Analysis

描述(申请人提供)：数据驱动的方法正越来越多地被用于分析脑成像数据。FMRI分析可以放在分析谱上，一端是基于大量模型的方法(如SPM软件中实现的通用线性模型(GLM))，另一端是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。在两者之间有一个缺口，我们和其他国家一直在努力填补这一缺口。特别是，像独立成分分析这样的方法对于将多变量功能磁共振成像问题降低到既容易处理又能够合并先验信息的问题特别有用。在这一竞争更新的第一阶段，我们将重点放在开发适合于进行群体推理的fMRI方法的ICA上，并允许纳入先前的信息，从而从“盲”的ICA方法转变为半盲的ICA方法。尽管我们已经取得了进展，但在使用ICA分析fMRI数据方面仍有相当大的工作要做。在这一竞争性的更新中，我们建议继续并显著扩大这项工作。首先，我们将扩展我们的半盲ICA(Sbica)框架，以提供一个综合来自多个空间和时间来源的先验信息的通用框架。在第二个目标中，我们将重点放在统计推断上，并制定一个框架，以整合相关的功能组成部分。在第三个目标中，我们将验证AIMS 1和2中的算法，包括使用从各种范例收集的多天功能磁共振数据。在这个目标中，我们开发了一种决策机制，用于选择给定特定问题的最佳方法组合。对于第四个目标，我们将应用我们的方法来收集在健康对照组和精神分裂症患者的四个研究范例中收集的数据。我们的最终目标包括继续开发我们的GIFT工具箱，并将上述算法、约束选择机制和可视化界面整合到软件中。这项研究的成功完成将为研究界提供一套强大的工具，通过利用基于模型的方法和数据驱动的方法的优点，提高大胆分析方法的敏感性和特异性。这些工具还将提供一种灵活的方式来研究功能网络之间的相互关系。这不仅适用于精神分裂症，也适用于许多其他疾病，如阿尔茨海默氏症、注意力缺陷多动症和精神病。

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.