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COINS:一款针对大型异构数据集构建的创新型信息学和神经影像学工具套件。

COINS: An Innovative Informatics and Neuroimaging Tool Suite Built for Large Heterogeneous Datasets.

机构信息

The Mind Research Network Albuquerque, NM, USA.

出版信息

Front Neuroinform. 2011 Dec 23;5:33. doi: 10.3389/fninf.2011.00033. eCollection 2011.

Abstract

The availability of well-characterized neuroimaging data with large numbers of subjects, especially for clinical populations, is critical to advancing our understanding of the healthy and diseased brain. Such data enables questions to be answered in a much more generalizable manner and also has the potential to yield solutions derived from novel methods that were conceived after the original studies' implementation. Though there is currently growing interest in data sharing, the neuroimaging community has been struggling for years with how to best encourage sharing data across brain imaging studies. With the advent of studies that are much more consistent across sites (e.g., resting functional magnetic resonance imaging, diffusion tensor imaging, and structural imaging) the potential of pooling data across studies continues to gain momentum. At the mind research network, we have developed the collaborative informatics and neuroimaging suite (COINS; http://coins.mrn.org) to provide researchers with an information system based on an open-source model that includes web-based tools to manage studies, subjects, imaging, clinical data, and other assessments. The system currently hosts data from nine institutions, over 300 studies, over 14,000 subjects, and over 19,000 MRI, MEG, and EEG scan sessions in addition to more than 180,000 clinical assessments. In this paper we provide a description of COINS with comparison to a valuable and popular system known as XNAT. Although there are many similarities between COINS and other electronic data management systems, the differences that may concern researchers in the context of multi-site, multi-organizational data sharing environments with intuitive ease of use and PHI security are emphasized as important attributes.

摘要

大量具有良好特征的神经影像学数据,尤其是针对临床人群的数据,对于推进我们对健康和患病大脑的理解至关重要。这些数据可以以更具普遍性的方式回答问题,并且还有可能产生源自原始研究实施后构想的新方法的解决方案。尽管目前人们对数据共享的兴趣日益浓厚,但神经影像学界多年来一直在努力寻找最佳方法,以鼓励在脑影像学研究之间共享数据。随着在各站点之间更加一致的研究(例如,静息功能磁共振成像、弥散张量成像和结构成像)的出现,跨研究数据池的潜力继续获得动力。在思维研究网络,我们开发了协作信息学和神经影像学套件(COINS;http://coins.mrn.org),为研究人员提供了一个基于开源模型的信息系统,其中包括基于网络的工具,用于管理研究、对象、成像、临床数据和其他评估。该系统目前托管了来自九个机构、300 多个研究、超过 14000 个对象以及超过 19000 次 MRI、MEG 和 EEG 扫描会话的,另外还有超过 180000 次临床评估。在本文中,我们提供了对 COINS 的描述,并与称为 XNAT 的有价值和流行系统进行了比较。尽管 COINS 与其他电子数据管理系统有许多相似之处,但在多站点、多组织数据共享环境中,易于使用和 PHI 安全性是研究人员关注的重要属性,因此强调了 COINS 与其他系统的差异。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db17/3250631/70044791538e/fninf-05-00033-g001.jpg

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