Comparison of fMRI Analytic tools

fMRI 分析工具比较

• 批准号: 6778393
• 负责人: TERRENCE R OAKES
• 金额: $ 20.69万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-25 至 2006-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6778393
• 关键词: adult human (21+); bioimaging /biomedical imaging; brain imaging /visualization /scanning; clinical research; computer data analysis; computer human interaction; computer program /software; computer system design /evaluation; functional magnetic resonance imaging; human subject; image processing; mathematical model

DESCRIPTION (provided by applicant): There are a variety of software packages available for analysis of functional MRI (fMRI) data. Widespread use of a thoroughly tested package is advantageous not only because of the ready availability to research labs without extensive computer programming resources, but also is a vehicle to facilitate comparisons and reproducibility among different research sites. We have experience with the following packages in our lab: AFNI, BrainVoyager, MEDx, SPM99, and VoxBo. Each of these packages is used by several laboratories around the world, and is generally accepted by the research community. Furthermore, each of these packages has a similar general approach to fMRI analysis, consisting of pre-processing (motion correction, coregistration to a template), parametric voxelwise comparison with a time-varying activation paradigm, definition of a statistical model, and interrogation and display of statistically meaningful results. However, the implementation of these basic steps varies considerably from one package to another, and many users find it difficult to select the best package for a given application based on criteria other than simply having started out using one package and becoming familiar with it. Each package has a different approach to preprocessing, specifying and estimating a statistical model, corrections for multiple comparisons, and to interrogation of intermediate and final results. Other considerations include the ease of learning and using a package, the availability and complexity of a Graphical User Interface (GUI), the ability to employ scripts to batch-process large analysis efforts, and the computational time required to perform the various analytic steps. Of course, the most important criteria is the accuracy, sensitivity, and specificity of the results with respect to a range of BOLD signal changes; while each of the above packages has an acceptable level of accuracy and sensitivity, how these levels compare across the packages has not been thoroughly investigated. We propose to perform a series of well-characterized fMRI scans, representative of research efforts in a variety of neuroimaging applications. We will use these scans to investigate the utility and effectiveness of five common fMRI analysis packages. We will also construct several series of synthetic data with simulated activations of varying magnitude and extent, which we will use to quantify the accuracy of motion correction and spatial normalization, and the ability of each package to detect small activations. Given the large amount of energy expended on neuroimaging experiments and the importance of knowledge to be gained, it is vital for researchers to select the best tools to reduce and analyze these complex data.

描述（由申请人提供）：有多种软件包可用于分析功能性MRI（fMRI）数据。广泛使用经过全面测试的软件包不仅是有利的，因为它可以在没有大量计算机编程资源的情况下随时提供给研究实验室，而且是促进不同研究地点之间的比较和再现性的工具。我们在实验室中使用过以下软件包：AFNI、BrainVoyager、MEDx、SPM 99和VoxBo。这些软件包中的每一个都被世界各地的几个实验室使用，并被研究界普遍接受。此外，这些软件包中的每一个都有一个类似的功能磁共振成像分析的一般方法，包括预处理（运动校正，配准到模板），参数voxelwise比较与随时间变化的激活范例，定义的统计模型，并询问和显示有统计意义的结果。然而，这些基本步骤的实现从一个软件包到另一个软件包有很大的不同，许多用户发现很难根据标准为给定的应用选择最好的软件包，而不是简单地开始使用一个软件包并熟悉它。每个软件包都有不同的预处理方法，指定和估计统计模型，多重比较的校正，以及中间和最终结果的询问。其他考虑因素包括易于学习和使用软件包，图形用户界面（GUI）的可用性和复杂性，采用脚本批量处理大型分析工作的能力，以及执行各种分析步骤所需的计算时间。当然，最重要的标准是准确性，灵敏度和特异性的结果相对于一系列的BOLD信号的变化;虽然上述每一个软件包都有一个可接受的准确性和灵敏度水平，这些水平如何在软件包之间进行比较尚未得到彻底的研究。我们建议进行一系列的功能磁共振成像扫描，在各种神经影像学应用的研究工作的代表。我们将使用这些扫描来研究五种常见的功能磁共振成像分析软件包的实用性和有效性。我们还将构建几个具有不同幅度和范围的模拟激活的合成数据系列，我们将使用这些数据来量化运动校正和空间归一化的准确性，以及每个包检测小激活的能力。考虑到神经成像实验所花费的大量精力以及获得知识的重要性，研究人员选择最佳工具来减少和分析这些复杂数据至关重要。

项目成果

Involvement of tumor macrophage HIFs in chemotherapy effectiveness: mathematical modeling of oxygen, pH, and glutathione.

• DOI: 10.1371/journal.pone.0107511
• 发表时间: 2014
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Chen D;Bobko AA;Gross AC;Evans R;Marsh CB;Khramtsov VV;Eubank TD;Friedman A
• 通讯作者: Friedman A

Dynamic Causal Modeling applied to fMRI data shows high reliability.

• DOI: 10.1016/j.neuroimage.2009.07.015
• 发表时间: 2010-01-01
• 期刊: NEUROIMAGE
• 影响因子: 5.7
• 作者: Schuyler, Brianna;Ollinger, John M.;Oakes, Terrence R.;Johnstone, Tom;Davidson, Richard J.
• 通讯作者: Davidson, Richard J.