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NEURO-LEARN: a Solution for Collaborative Pattern Analysis of Neuroimaging Data.
Neuroinformatics ( IF 2.7 ) Pub Date : 2020-06-10 , DOI: 10.1007/s12021-020-09468-6
Bingye Lei 1, 2, 3 , Fengchun Wu 2, 4 , Jing Zhou 1, 2 , Dongsheng Xiong 1, 2 , Kaixi Wang 1 , Lingyin Kong 1 , Pengfei Ke 1 , Jun Chen 5, 6 , Yuping Ning 2, 4 , Xiaobo Li 2, 7 , Zhiming Xiang 5, 8 , Kai Wu 1, 2, 3, 4, 5, 6, 9
Affiliation  

The development of neuroimaging instrumentation has boosted neuroscience researches. Consequently, both the fineness and the cost of data acquisition have profoundly increased, leading to the main bottleneck of this field: limited sample size and high dimensionality of neuroimaging data. Therefore, the emphasis of ideas of data pooling and research collaboration has increased over the past decade. Collaborative analysis techniques emerge as the idea developed. In this paper, we present NEURO-LEARN, a solution for collaborative pattern analysis of neuroimaging data. Its collaboration scheme consists of four parts: projects, data, analysis, and reports. While data preparation workflows defined in projects reduce the high dimensionality of neuroimaging data by collaborative computation, pooling of derived data and sharing of pattern analysis workflows along with generated reports on the Web enlarge the sample size and ensure the reliability and reproducibility of pattern analysis. Incorporating this scheme, NEURO-LEARN provides an easy-to-use Web application that allows users from different sites to share projects and processed data, perform pattern analysis, and obtain result reports. We anticipate that this solution will help neuroscientists to enlarge sample size, conquer the curse of dimensionality and conduct reproducible studies on neuroimaging data with efficiency and validity.



中文翻译:

NEURO-LEARN:神经影像数据协作模式分析的解决方案。

神经影像仪器的发展促进了神经科学研究。因此,数据采集的精细度和成本都大大提高了,这导致了该领域的主要瓶颈:有限的样本量和神经影像数据的高维度。因此,在过去的十年中,数据池和研究协作的思想越来越受到重视。随着想法的发展,协作分析技术应运而生。在本文中,我们介绍了NEURO-LEARN,这是一种用于神经影像数据的协作模式分析的解决方案。它的协作方案包括四个部分:项目,数据,分析和报告。尽管项目中定义的数据准备工作流程通过协作计算降低了神经影像数据的高维度,汇总派生数据并共享模式分析工作流程以及在Web上生成的报告,可以扩大样本数量并确保模式分析的可靠性和可重复性。通过结合使用此方案,NEURO-LEARN提供了一个易于使用的Web应用程序,该应用程序允许来自不同站点的用户共享项目和处理的数据,执行模式分析并获取结果报告。我们预计,该解决方案将帮助神经科学家扩大样本量,克服维度的诅咒,并有效且有效地对神经影像数据进行可重复的研究。NEURO-LEARN提供了一个易于使用的Web应用程序,该应用程序使来自不同站点的用户可以共享项目和处理的数据,执行模式分析并获取结果报告。我们预计,该解决方案将帮助神经科学家扩大样本量,克服维度的诅咒,并有效且有效地对神经影像数据进行可重复的研究。NEURO-LEARN提供了一个易于使用的Web应用程序,该应用程序使来自不同站点的用户可以共享项目和处理的数据,执行模式分析并获取结果报告。我们预计,该解决方案将帮助神经科学家扩大样本量,克服维度的诅咒,并有效且有效地对神经影像数据进行可重复的研究。

更新日期:2020-06-10
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