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Influence of Processing Tools on fMRI Metanalyses

处理工具对 fMRI 荟萃分析的影响

基本信息

批准号：
6989048
负责人：
JINHU XIONG
金额：
$ 19.44万
依托单位：
UNIVERSITY OF IOWA
依托单位国家：
美国
项目类别：
财政年份：
2002
资助国家：
美国
起止时间：
2002-12-01 至 2007-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/6989048
关键词：
Influence Processing Tools fMRI Metanalyses

项目摘要

DESCRIPTION (provided by applicant): Quantitative metanalysis is an effective strategy for associating localized neural populations with cognitive operations. By grouping published observations, quantitative metanalysis can assemble converging evidence to create neuroanatomically and cognitively explicit models of elementary cognitive operations and of ensembles of cognitive operations supporting complex tasks. Metanalysis strategies have been developed and validated by the PI and colleagues at the Research Imaging Center (RIC) for the last four years. An electronic environment (BrainMap) for effectively carrying out metanalyses is operational. At present, the metanalysis strategy, however, does not correct for differences among the informatics tools used in image processing and data analysis. Currently, results are confounded by different informatics tools, in particular by spatial normalization, statistical methods and inferences. Assessing and understanding effects of different informatics tools on metanalysis and neuroimaging results are therefore very important. Building upon our current work and an ongoing NIH reward (entitled "Metanalysis in Cognitive Neuroimaging: Methods Validations"), the present application seeks to further refine and extend modeling to account for the effects of informatics tools and to include metanalyses with statistical parametric images. The overall objective of this application is then to advance the use of quantitative metanalysis as a research method for functional human brain mapping by assessing and minimizing the effects of different informatics tools. The effects of different spatial normalization procedures on reported locations of functional activations will be assessed (Aim 1). The effects of different statistical analyses and statistical inference strategies on detection of functional activations will be extended to include analysis of shared statistical parametric images (Aim 4). The strategies, guidelines, statistical models, electronic tools, and data sets developed through this application will be shared with the brain-mapping community through publications, Internet and community presentations. Once thoroughly critiqued and refined, integration of these tools/strategies into an electronic environment (BrainMap) for public distribution is our next objective.

描述（由申请人提供）：定量元分析是将局部神经群体与认知操作相关联的有效策略。通过对已发表的观察结果进行分组，定量元分析可以收集汇聚的证据，以创建基本认知操作和支持复杂任务的认知操作集合的神经解剖学和认知明确模型。在过去的四年里，PI和成像研究中心（RIC）的同事已经开发和验证了荟萃分析策略。有效进行元分析的电子环境（脑图）是可操作的。目前，元分析策略，但是，不正确的图像处理和数据分析中使用的信息学工具之间的差异。目前，不同的信息学工具，特别是空间标准化、统计方法和推断，混淆了结果。因此，评估和理解不同信息学工具对元分析和神经影像结果的影响非常重要。基于我们当前的工作和正在进行的NIH奖励（题为“认知神经成像中的元分析：方法验证”），本申请寻求进一步细化和扩展建模以考虑信息学工具的影响并包括具有统计参数图像的元分析。这个应用程序的总体目标是，通过评估和最大限度地减少不同的信息学工具的影响，促进使用定量元分析作为功能人脑映射的研究方法。将评估不同空间标准化程序对报告的功能激活位置的影响（目标1）。不同的统计分析和统计推断策略对功能激活检测的影响将扩展到包括共享统计参数图像的分析（目标4）。通过这一应用程序开发的战略、指导方针、统计模型、电子工具和数据集将通过出版物、互联网和社区演示与脑图社区共享。一旦彻底的批判和完善，这些工具/战略整合到一个电子环境（脑图），供公众分发是我们的下一个目标。