Stawiski Marc, Bucciarelli Vittoria, Vogel Dorian, Hemm Simone
Neuroengineering Group, Institute for Medical Engineering and Medical Informatics, School of Life Sciences, FHNW University of Applied Sciences and Arts Northwestern Switzerland, Muttenz, Switzerland.
Front Neuroinform. 2024 Sep 5;18:1435971. doi: 10.3389/fninf.2024.1435971. eCollection 2024.
Neuroscience studies entail the generation of massive collections of heterogeneous data (e.g. demographics, clinical records, medical images). Integration and analysis of such data in research centers is pivotal for elucidating disease mechanisms and improving clinical outcomes. However, data collection in clinics often relies on non-standardized methods, such as paper-based documentation. Moreover, diverse data types are collected in different departments hindering efficient data organization, secure sharing and compliance to the FAIR (Findable, Accessible, Interoperable, Reusable) principles. Henceforth, in this manuscript we present a specialized data management system designed to enhance research workflows in Deep Brain Stimulation (DBS), a state-of-the-art neurosurgical procedure employed to treat symptoms of movement and psychiatric disorders. The system leverages REDCap to promote accurate data capture in hospital settings and secure sharing with research institutes, Brain Imaging Data Structure (BIDS) as image storing standard and a DBS-specific SQLite database as comprehensive data store and unified interface to all data types. A self-developed Python tool automates the data flow between these three components, ensuring their full interoperability. The proposed framework has already been successfully employed for capturing and analyzing data of 107 patients from 2 medical institutions. It effectively addresses the challenges of managing, sharing and retrieving diverse data types, fostering advancements in data quality, organization, analysis, and collaboration among medical and research institutions.
神经科学研究需要生成大量异构数据（例如人口统计学数据、临床记录、医学图像）。在研究中心对这些数据进行整合和分析对于阐明疾病机制和改善临床结果至关重要。然而，临床数据收集通常依赖于非标准化方法，如纸质文档记录。此外，不同部门收集的数据类型各异，这阻碍了高效的数据组织、安全共享以及对FAIR（可查找、可访问、可互操作、可重用）原则的遵循。因此，在本手稿中，我们展示了一个专门的数据管理系统，旨在增强深部脑刺激（DBS）研究工作流程，DBS是一种用于治疗运动和精神疾病症状的先进神经外科手术。该系统利用REDCap促进医院环境中的准确数据采集以及与研究机构的安全共享，采用脑成像数据结构（BIDS）作为图像存储标准，并使用特定于DBS的SQLite数据库作为综合数据存储库以及与所有数据类型的统一接口。一个自行开发的Python工具实现了这三个组件之间的数据流自动化，确保它们的完全互操作性。所提出的框架已成功用于捕获和分析来自2个医疗机构的107名患者的数据。它有效地解决了管理、共享和检索不同数据类型的挑战，促进了数据质量、组织、分析以及医学和研究机构之间合作的进步。
