Neuroinformatics, Mind Research Network Albuquerque, NM, USA.
Front Neuroinform. 2010 Apr 21;3:36. doi: 10.3389/neuro.11.036.2009. eCollection 2010.
A neuroinformatics (NI) system is critical to brain imaging research in order to shorten the time between study conception and results. Such a NI system is required to scale well when large numbers of subjects are studied. Further, when multiple sites participate in research projects organizational issues become increasingly difficult. Optimized NI applications mitigate these problems. Additionally, NI software enables coordination across multiple studies, leveraging advantages potentially leading to exponential research discoveries. The web-based, Mind Research Network (MRN), database system has been designed and improved through our experience with 200 research studies and 250 researchers from seven different institutions. The MRN tools permit the collection, management, reporting and efficient use of large scale, heterogeneous data sources, e.g., multiple institutions, multiple principal investigators, multiple research programs and studies, and multimodal acquisitions. We have collected and analyzed data sets on thousands of research participants and have set up a framework to automatically analyze the data, thereby making efficient, practical data mining of this vast resource possible. This paper presents a comprehensive framework for capturing and analyzing heterogeneous neuroscience research data sources that has been fully optimized for end-users to perform novel data mining.
神经信息学(NI)系统对于脑成像研究至关重要,它可以缩短研究构思和结果之间的时间。这种 NI 系统需要在研究大量对象时能够很好地扩展。此外,当多个站点参与研究项目时,组织问题会变得越来越困难。优化的 NI 应用程序可以缓解这些问题。此外,NI 软件能够协调多个研究,利用可能导致研究发现呈指数级增长的优势。基于网络的 Mind Research Network(MRN)数据库系统是通过我们在 200 项研究和来自 7 个不同机构的 250 名研究人员的经验而设计和改进的。MRN 工具允许收集、管理、报告和高效利用大规模的异构数据源,例如多个机构、多个主要研究者、多个研究计划和研究以及多模态采集。我们已经收集和分析了数千名研究参与者的数据组,并建立了一个框架来自动分析这些数据,从而使对这一巨大资源进行高效、实用的数据挖掘成为可能。本文提出了一种全面的框架,用于捕获和分析异构的神经科学研究数据源,该框架已针对最终用户进行了全面优化,以执行新的数据挖掘。