fMRI Research via Database Mining, Management

描述(由申请人提供)：来自同行评议文献的完整功能磁共振研究数据的大规模数据库为创新研究项目提供了宝贵的机会，以获得关于人脑功能的新知识。此外，它们还提供了广泛的实验方案、设计和参数范例，用于开发能够探索和检查这些丰富的神经科学数据集的尖端技术解决方案。这个多中心项目寻求利用存档的功能磁共振研究数据来引导新颖的、假设驱动的功能磁共振实验进入认知功能的神经生理学关联。根据这一提议，作为核心中心的功能磁共振成像数据中心(FMRIDC)及其在伯克利、圣巴巴拉和多伦多大学的合作者将：i)从这个独特的神经成像数据储存库中挖掘数据，以检查受试者之间在表征组水平和跨多个认知域的默认状态的大胆活动方面的个体差异；ii)使用多变量和研究聚类方法设计持续更新的fMRI研究档案的当前内容的表示，以可视化在BOLD激活中的fMRI实验和MAP研究、对象和特定领域的变化的新趋势；Iii)识别和验证BOLD活动的汇总统计测量，其可用于快速评估大量fMRI时间进程中的相似性；iv)研究和部署软件以动态分析已发表的fMRI文献，以指导构建包含与如何执行和报告fMRI研究相关的信息的fMRI研究本体论框架；iv)与加州大学伯克利分校的合作者合作，开发和提供用于fMRI研究数据管理的免费分发软件工具，以简化数据交换过程；V)与罗特曼研究所的合作者一起，检查功能磁共振成像处理流水线，以确定那些最好地平衡偏差-方差权衡的策略，从而优化可以从给定扫描仪、实验和其他协议的功能磁共振数据中做出的推断。将获得功能成像数据集，以验证优化数据处理和汇总统计计算的方法。我们将与加州大学圣巴巴拉分校的合作者一起，利用从这些项目中获得的知识和工具，对人类认知过程进行新颖的功能磁共振研究，特别是测试关于受试者之间在情景记忆功能上的个体差异的具体假设。FMRI数据管理的软件工具将是准确封装研究元数据的关键，处理优化方法将是准确的统计建模图像时间进程的关键。该项目的长期成果将是从以前发表的功能磁共振研究数据中提取有关大脑功能的新知识，并将其用于指导原始的、假设驱动的神经成像研究。该项目的其他有价值的成果将是：1)通过直接连接到达特茅斯因特网2，改进合作者和其他用户对档案的远程访问，以便进行资源共享和数据挖掘；2)利用强大的网格技术增强远程处理能力，以协助档案的分散使用；3)增强和扩展数据库能力，通过与其他在线神经成像数据资源的互动，为网站用户增加更多的在线功能磁共振数据可视化；以及4)支持开放的神经科学数据共享和管理。总体而言，该项目代表着一项独特的协同努力，它将与人脑项目的使命保持一致，共享计算和数据库资源，以推动研究人员发起的神经科学研究和方法开发。

DESCRIPTION (provided by applicant): Large-scale databases of complete fMRI study data from the peer-reviewed literature offer valuable opportunities for innovative research projects to obtain new knowledge about human brain function. In addition, they provide broad ranging exemplars of experimental protocols, designs, and parameters for developing leading edge technological solutions enabling exploration and examination of these rich neuroscientific data sets. This multi-center project seeks to utilize archived fMRI study data to guide novel, hypothesis-driven, fMRI experimentation into the neurophysiological correlates of cognitive function. Under this proposal, the fMRI Data Center (fMRIDC), serving as the core center, and its collaborators at Berkeley, Santa Barbara, and the University of Toronto will: i) mine data from this unique repository of neuroimaging data to examine individual differences between subjects in the characterization of group-level and 'default state' BOLD activity across multiple cognitive domains; ii) devise continuously updating representations of the current content of the fMRI study archive using multivariate and study clustering methods to visualize emerging trends in fMRI experimentation and map study, subject, and domain-specific variation in BOLD activation; iii) identify and validate summary statistical measures of BOLD activity that may be used to rapidly assess similarity across a large number of fMRI time courses; iv) research and deploy software to dynamically analyze the published fMRI literature to guide the construction of an fMRI study ontological framework for containing information relevant to how fMRI studies are performed and reported; iv) with collaborators from UC Berkeley, develop and deliver freely-distributable software tools for fMRI study data management that simplify the process of data exchange; v) with collaborators from the Rotman Institute, examine fMRI processing pipelines in order to identify those strategies best balancing the bias-variance tradeoff, thereby optimizing inferences that can be made from fMRI data given scanner, experimental, and other protocols. Functional imaging data sets will be obtained to validate methods for optimized data processing and for summary statistical calculations. With collaborators from UC Santa Barbara, using knowledge and tools obtained from these projects, we will conduct novel fMRI studies of human cognitive processes, in particular, testing specific hypotheses regarding individual differences between subjects in episodic memory function. The software tools for fMRI data management will be essential for accurately encapsulating study meta-data and processing optimization methods will be critical for accurate statistical modeling image time courses. The long-term outcomes of this project will be the extraction of new knowledge about brain function from previously published fMRI study data and its usefulness in guiding original, hypothesis-driven, neuroimaging research. Additional, valuable outcomes of the project will be i) improving collaborator and other user remote access to the archive for resource sharing and data mining by tying directly into the Dartmouth Internet2 connection; ii) enable greater remote processing capability using powerful Grid technology to assist distributed uses of the archive; iii) enhancement and extension of data base capabilities to add greater online fMRI data visualization for website users through interaction with other online neuroimaging data resources; and, iv) supporting open neuroscientific data sharing and curation. Overall, this project represents a unique synergistic effort which, in keeping with the mission of the Human Brain Project, will share computational and database resources for the purposes of advancing investigator-initiated neuroscience research and methods development.