Developing Cloud-based tools for Big Neural Data

开发基于云的大神经数据工具

• 批准号: 8830141
• 负责人: Joost B Wagenaar
• 金额: $ 19.22万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-29 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8830141
• 关键词: Animal Model; Applications Grants; Area; Big Data; Biomedical Engineering; Cardiac; Clinical; Collaborations; Communities; Complement; Complex; Computational Technique; Data; Data Analyses; Data Provenance; Data Set; Electrophysiology (science); Emergency Care; Epilepsy; Evaluation; Feedback; Fostering; Foundations; Funding; Genetic; Genomics; Goals; Health; Human; Image; Incentives; Individual; Institution; Knowledge; Laboratories; Learning; Machine Learning; Metadata; Methods; Mining; Modality; National Institute of Neurological Disorders and Stroke; Nature; Neurosciences; Organism; Performance; Physiological; Process; Protocols documentation; Research; Research Infrastructure; Research Personnel; Role; Science; Scientist; Series; Solutions; Standardization; Statistical Models; Techniques; Time; Training; base; career; cloud based; cohort; comparative; computer science; data acquisition; data integration; data management; data mining; data sharing; improved; nervous system disorder; novel; novel strategies; relating to nervous system; statistics; tool; tool development; translational neuroscience

DESCRIPTION (provided by applicant): Big data has the potential to dramatically advance the electrophysiology biodata sciences in similar ways that it has transformed Genetics. Differences between these two areas dictate separate approaches to apply Big Data tools, and methods in order to provide successful assets to the research community. For one, neural datasets are very heterogeneous by nature. The data is difficult to interpret without knowing specifics about the data acquisition protocol, the experimental paradigm and the physiological state of the recorded subject. Many neural datasets are complemented with complex meta-data sets, which should be an integral component in any effort to integrate and share these data with other researchers. The goal of this project is to develop novel, generalizable Big Data tools to facilitate cloud-base analysis of complex multi-scale neural data. Epilepsy research will be used as a specific use case to guide the development of the tools. A cohort of established senior investigators performing epilepsy research will use and validate these tools in their laboratories. Epilepsy research is currently limited by its narrow focus on single models (animal or human) in individual centers and laboratories. Just as Genetics was revolutionized through Big Data techniques, so too can Epilepsy research be transformed through novel approaches to standardize, share, and mine data across groups of investigators. Over the past several years I have co-developed a NINDS funded cloud-based data platform, ://ieeg.org, giving me a central role in developing Big Data solutions for neural data, such as customized data sharing, large-scale cloud-based data analysis, and search and interrogation techniques for complex data and metadata. My scientific objectives for this project are: (1) to develop generalizable tools to curate, analyze, and interrogate multi-scale neural data, and (2) to create a platform that will galvanize a research community focused on sharing data, and methods to advance Big Data research in the basic and translational neurosciences. Equally important to this proposal, I present a training plan to prepare me for an academic career focused on Big Data in the neurosciences. This plan supplements my background in bioengineering and statistical modeling of neural data with broader data-science expertise in data integration and machine learning, and deeper domain knowledge of the clinical neurosciences. I have assembled a group of collaborators, basic investigators and clinician scientists, who will use the tools developed in this project to analyze and validate their data and methods. I will use the results of this project as the foundation for a R01 Grant application, in which I will expand the developed platform and tools to target other research domains (TBI, Emergency Care, Cardiac), as well as integrate other data-modalities such as Imaging, and Genomics. OMB No. 0925-0001/0002 (Rev. 08/12 Approved Through 8/31/2015) Page Continuation Format Page

描述(由申请人提供)：大数据有可能以类似于它改变遗传学的方式显着促进电生理学生物数据科学的发展。这两个领域之间的差异要求采用不同的方法来应用大数据工具和方法，以便为研究社区提供成功的资产。首先，神经数据集本质上是非常不同的。如果不知道数据采集协议、实验范式和被记录对象的生理状态的具体情况，就很难解释这些数据。许多神经数据集与复杂的元数据集相辅相成，在任何整合和与其他研究人员共享这些数据的努力中，元数据集都应该是不可或缺的组成部分。该项目的目标是开发新的、可推广的大数据工具，以促进基于云的复杂多尺度神经数据的分析。癫痫研究将作为一个具体的用例来指导工具的开发。进行癫痫研究的一群老牌高级调查人员将在他们的实验室使用和验证这些工具。癫痫的研究目前受限于单个中心和实验室的单一模型(动物或人类)。就像遗传学通过大数据技术发生了革命性的变化一样，癫痫研究也可以通过新的方法来改变，以标准化、共享和挖掘跨调查小组的数据。在过去的几年里，我参与开发了一个由NINDS资助的基于云的数据平台：//ieeg.org，这让我在为神经数据开发大数据解决方案方面发挥了核心作用，例如定制数据共享、大规模基于云的数据分析，以及针对复杂数据和元数据的搜索和询问技术。我这个项目的科学目标是：(1)开发可概括的工具来管理、分析和审问多尺度神经数据，以及(2)创建一个平台，以激励专注于共享数据的研究社区，以及推动基础神经科学和翻译神经科学中的大数据研究的方法。对这项提议同样重要的是，我提出了一项培训计划，为我专注于神经科学中的大数据的学术生涯做好准备。这项计划补充了我在生物工程和神经数据统计建模方面的背景，拥有更广泛的数据科学专业知识，包括数据集成和机器学习，以及临床神经科学领域的更深层次知识。我已经召集了一组合作者、基础研究人员和临床科学家，他们将使用在这个项目中开发的工具来分析 并验证他们的数据和方法。我将利用这个项目的成果作为R01 Grant应用程序的基础，在该应用程序中，我将扩展开发的平台和工具，以针对其他研究领域(TBI、急救、心脏)，以及整合其他数据模式，如成像和基因组学。OMB编号0925-0001/0002(08/12版批准至2015年8月31日)页面续格式页面