4.6 Article

Common Data Elements, Scalable Data Management Infrastructure, and Analytics Workflows for Large-Scale Neuroimaging Studies

期刊

FRONTIERS IN PSYCHIATRY
卷 12, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fpsyt.2021.682495

关键词

human brain; neuroimaging; multi-level assessment; large-scale studies; common data element; data processing pipelines; scalable analytics; bids format

资金

  1. National Institute of General Medical Sciences, National Institutes of Health [P20GM121312]
  2. U.S. Department of Defense [W81XWH-12-10697]
  3. Laureate Institute for Brain Research (LIBR)
  4. William K. Warren Foundation

向作者/读者索取更多资源

Neuroscience studies require robust bioinformatic support and expertise to preprocess and integrate high-dimensional datasets. This study introduces a scalable data management infrastructure supporting multiple analytics workflows and utilizing the BIDS format for various types of data, demonstrating its utility through exemplar results.
Neuroscience studies require considerable bioinformatic support and expertise. Numerous high-dimensional and multimodal datasets must be preprocessed and integrated to create robust and reproducible analysis pipelines. We describe a common data elements and scalable data management infrastructure that allows multiple analytics workflows to facilitate preprocessing, analysis and sharing of large-scale multi-level data. The process uses the Brain Imaging Data Structure (BIDS) format and supports MRI, fMRI, EEG, clinical, and laboratory data. The infrastructure provides support for other datasets such as Fitbit and flexibility for developers to customize the integration of new types of data. Exemplar results from 200+ participants and 11 different pipelines demonstrate the utility of the infrastructure.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据