Kuplicki Rayus, Touthang James, Al Zoubi Obada, Mayeli Ahmad, Misaki Masaya, Aupperle Robin L, Teague T Kent, McKinney Brett A, Paulus Martin P, Bodurka Jerzy
Laureate Institute for Brain Research, Tulsa, OK, United States.
Department of Community Medicine, Oxley College of Health Sciences, University of Tulsa, Tulsa, OK, United States.
Front Psychiatry. 2021 Jun 17;12:682495. doi: 10.3389/fpsyt.2021.682495. eCollection 2021.
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.
神经科学研究需要大量的生物信息学支持和专业知识。众多高维和多模态数据集必须进行预处理和整合，以创建强大且可重复的分析流程。我们描述了一种通用数据元素和可扩展数据管理基础设施，它允许多个分析工作流程来促进大规模多层次数据的预处理、分析和共享。该过程使用脑成像数据结构（BIDS）格式，并支持MRI、fMRI、脑电图、临床和实验室数据。该基础设施为Fitbit等其他数据集提供支持，并为开发者提供灵活性以定制新型数据的整合。来自200多名参与者和11种不同流程的示例结果证明了该基础设施的实用性。
