fMRI Research via Database Mining, Management

描述（由申请人提供）：来自同行评审文献的完整fMRI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，以获得有关人脑功能的新知识。此外，他们还提供了广泛的实验方案，设计和参数的范例，用于开发前沿技术解决方案，从而能够探索和检查这些丰富的神经科学数据集。这个多中心项目旨在利用存档的fMRI研究数据来指导新的，假设驱动的，fMRI实验到认知功能的神经生理学相关性。根据这一提议，功能磁共振成像数据中心（fMRIDC），作为核心中心，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库中挖掘数据，以检查受试者在多个认知领域中群体水平和“默认状态”BOLD活动特征方面的个体差异; ii）使用多变量和研究聚类方法设计fMRI研究档案的当前内容的持续更新表示，以可视化fMRI实验中出现的趋势，并绘制BOLD激活中的研究、主题和域特异性变化; 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时间过程中的相似性; iv）第四条研究和部署软件来动态分析已发表的fMRI文献，以指导构建fMRI研究本体框架，与fMRI研究如何进行和报告相关的信息; iv）与加州大学伯克利分校的合作者一起，开发和提供用于fMRI研究数据管理的可免费分发的软件工具，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fMRI处理流程，以确定最能平衡偏差-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fMRI数据做出的推断。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。与来自加州大学圣巴巴拉分校的合作者，利用从这些项目中获得的知识和工具，我们将进行人类认知过程的新功能磁共振成像研究，特别是，测试特定的假设，个体之间的差异，在情节记忆功能。功能磁共振成像数据管理的软件工具将是必不可少的准确封装研究元数据和处理优化方法将是准确的统计建模图像时间过程的关键。该项目的长期成果将是从先前发表的fMRI研究数据中提取有关脑功能的新知识，并将其用于指导原始的、假设驱动的神经影像学研究。此外，该项目的宝贵成果将是：i）通过直接连接到达特茅斯因特网2连接，改善合作者和其他用户对档案的远程访问，以实现资源共享和数据挖掘; ii）利用强大的网格技术实现更大的远程处理能力，以协助档案的分布式使用; iii）增强和扩展数据库功能，通过与其他在线神经成像数据资源的交互，为网站用户增加更大的在线功能磁共振成像数据可视化;以及，iv）支持开放的神经科学数据共享和管理。总的来说，这个项目代表了一个独特的协同努力，在与人脑项目的使命保持一致，将共享计算和数据库资源的目的，推进计算机发起的神经科学研究和方法的发展。

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.