A Unified Framework for Flexible Brain Image Analysis

描述（由申请人提供）：数据驱动的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光谱上，一端是基于模型的方法（如SPM软件中实现的一般线性模型（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。在这两者之间有一个空白，我们和其他人一直在努力填补这个空白。特别是，诸如ICA之类的方法对于将多变量fMRI问题简化为既易于处理又能够结合先验信息的问题特别有用。在这一竞争更新的第一阶段，我们将精力集中在开发功能磁共振成像方法的ICA上，这将适用于进行群体推断，并允许合并先验信息，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。在这个竞争性的更新中，我们建议继续并显著扩展这项工作。首先，我们将扩展我们的半盲ICA （sbICA）框架，为整合来自多个时空源的先验信息提供一个通用框架。在第二个目标中，我们将侧重于统计推断，并开发一个框架来整合相关的功能组件。在第三个目标中，我们将验证目标1和2中的算法，包括使用从各种范式中收集的多日功能磁共振成像数据。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，我们将把我们的方法应用于在健康对照和精神分裂症患者中收集的四个经过充分研究的范例中的数据。我们的最终目标包括继续开发GIFT工具箱，并将上述算法、约束选择机制和可视化界面合并到软件中。这项研究的成功完成将为研究界提供一套强大的工具，通过利用基于模型和数据驱动的方法的优势，提高BOLD分析方法的敏感性和特异性。这些工具还将以灵活的方式提供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，也适用于许多其他疾病，如阿尔茨海默氏症、注意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.