Neuroinformatics for Prospective Data Management and Sharing

用于前瞻性数据管理和共享的神经信息学

• 批准号: 8607214
• 负责人: Henry Jeremy Bockholt
• 金额: $ 20.52万
• 依托单位: ADVANCED BIOMEDICAL INFORMATICS GROUP
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8607214
• 关键词: Adopted; Air; Archives; Brain; Brain imaging; Businesses; Clinical; Collection; Communities; Companions; Computer software; Data; Data Collection; Data Security; Data Set; Data Storage and Retrieval; Diagnostic; Disease; Documentation; Evaluation; Event; Funding; Funding Agency; Genetic; Goals; Grant; Health; Image; Individual; Knowledge Extraction; Learning; Marketing; Methods; Mind; Mind-Body Method; Mining; Modeling; Phase; Population; Positioning Attribute; Privacy; Publications; Research; Research Design; Research Infrastructure; Research Personnel; Research Subjects; Services; Site; Small Business Innovation Research Grant; Small Business Technology Transfer Research; Structure; Surveys; System; Techniques; Testing; Thinking; Time Study; Training Support; United States National Institutes of Health; Work; combat; commercialization; data management; data mining; data sharing; experience; neuroimaging; neuroinformatics; neuropsychological; novel; prospective; public health relevance; response; social; tool; user friendly software; user-friendly

DESCRIPTION (provided by applicant): There is a tremendous need for tools that allow NIH sponsored investigators to easily share and disseminate data from brain imaging studies. Access to large neuroimaging data sets is a critical aspect of learning about how the brain works both in health and disease. Most typically, large data sets are collected by one investigator, used for a specific purpose, and then often archived. Sometimes these data are shared with collaborators, typically after an extended effort. Such an approach takes a very narrow view of what could be learned from these rich data sets. For example, there are often multiple questions that could be asked but weren't conceived at the time of the study design. There are also a plethora of advanced analytic techniques and approaches, developed by multiple groups, which can be applied to these data to identify hidden structure or answer new questions. However, there are many barriers to data sharing of multimodal neuroimaging data and the community has tried for years to confront the various barriers. In this Phase I STTR we are proposing a unique approach which is to take the neuroinformatics tools that we have been developing at the Mind Research Network and create a user friendly neuroinformatics suite which will enable prospective management and sharing of studies, assessments, and neuroimaging data. The ability to handle prospective (ongoing) studies including privacy aspects is, we believe, critical to encouraging sharing as the control of whether and who to share with will be under the individual investigator's control. Sharing is then quite simple to enable as all the data are collected, stored, and managed within a larger infrastructure. We plan to explore the feasibility of our approach, which will be to market the services required to implement and sustain our neuroinformatics tools. This will include installation, support, training, and data storage among other things. The successful completion of this project would represent a major transformation which could propel scientific sharing and knowledge extraction of diverse types of data into a practical and widely used model. 2

描述（由申请人提供）：非常需要能够让NIH资助的研究人员轻松共享和传播脑成像研究数据的工具。访问大型神经成像数据集是了解大脑在健康和疾病中如何工作的一个关键方面。最典型的情况是，大型数据集由一名调查员收集，用于特定目的，然后通常存档。有时这些数据会与协作者共享，通常是在经过长时间的努力之后。这种方法对从这些丰富的数据集中可以学到什么采取了非常狭隘的观点。例如，通常有多个问题可以提出，但在研究设计时没有想到。还有许多由多个小组开发的先进分析技术和方法，可以应用于这些数据，以识别隐藏的结构或回答新的问题。然而，多模态神经成像数据的数据共享存在许多障碍，并且社区多年来一直试图面对各种障碍。在第一阶段STTR中，我们提出了一种独特的方法，即采用我们在思维研究网络中开发的神经信息学工具，并创建一个用户友好的神经信息学套件，该套件将实现前瞻性管理和共享研究，评估和神经成像数据。我们认为，处理前瞻性（正在进行的）研究（包括隐私方面）的能力对于鼓励共享至关重要，因为是否共享以及与谁共享的控制权将在个体研究者的控制下。这样，共享就变得非常简单，因为所有数据都是在一个更大的基础设施中收集、存储和管理的。我们计划探索我们的方法的可行性，这将是市场所需的服务，以实施和维持我们的神经信息学工具。这将包括安装、支持、培训和数据存储等。该项目的成功完成将标志着一个重大转变，可以推动科学共享和知识提取的各种类型的数据成为一个实用和广泛使用的模型。2