失读症数据联盟:失读症神经影像数据共享、分析和高级研究的综合平台。

IF 3.1 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Rishikesh V Phatangare, Mark A Eckert, Li Luo, Kenneth I Vaden, James Z Wang
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引用次数: 0

摘要

神经影像学研究已经并将继续推进我们对阅读障碍的神经生物学的理解。整合这些研究的数据有可能重复发现,通过以理论为重点的研究加深理解,并提供意想不到的发现。对于需要足够大且定义明确的参与者群体以获得足够实验能力的问题,特别是对于年龄、语言背景和认知特征可能影响成像结果的复杂疾病,这种数据整合非常重要。我们开发了一个数据共享平台,提供数据存储库、图像处理资源和数据分析工具,重点是跨回顾性数据集的数据协调(https://dyslexiadata.org)。在这里,我们总结了数据共享、下载、成像指标,以及在设计和通过该存储库提供的资源时需要考虑的质量和隐私问题。通过提供对相对较大的多站点数据集的访问,研究人员可以测试关于阅读发展和残疾的假设,测试新的数据分析方法,甚至在平台内,并推进对阅读障碍的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dyslexia Data Consortium: A Comprehensive Platform for Neuroimaging Data Sharing, Analysis, and Advanced Research in Dyslexia.

Neuroimaging studies have and continue to advance our understanding of the neurobiology of dyslexia. Integration of data from these studies has the potential to replicate findings, deepen understanding through theoretically focused research, and provide for unexpected discovery. This data integration can be important for questions where a sufficiently large and well-defined group of participants is necessary for sufficient experimental power, particularly for a complex disorder where age, language background, and cognitive profiles can impact imaging results. We have developed a data-sharing platform to provide a data repository, image processing resources, and data analysis tools, with an emphasis on data harmonization across retrospective datasets ( https://dyslexiadata.org ). Here, we summarize data sharing, download, imaging metrics, and quality and privacy considerations in the design of and resources available through this repository. By providing access to a relatively large multisite dataset, researchers can test hypotheses about reading development and disability, test novel data analysis methods, even within the platform, and advance understanding of dyslexia.

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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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