Department of Neurology, TUM School of Medicine, Technical University of Munich, München, Germany.
TUM-Neuroimaging Center, TUM School of Medicine, Technical University of Munich, München, Germany.
Sci Data. 2023 Sep 11;10(1):613. doi: 10.1038/s41597-023-02525-0.
Biomarker discovery in neurological and psychiatric disorders critically depends on reproducible and transparent methods applied to large-scale datasets. Electroencephalography (EEG) is a promising tool for identifying biomarkers. However, recording, preprocessing, and analysis of EEG data is time-consuming and researcher-dependent. Therefore, we developed DISCOVER-EEG, an open and fully automated pipeline that enables easy and fast preprocessing, analysis, and visualization of resting state EEG data. Data in the Brain Imaging Data Structure (BIDS) standard are automatically preprocessed, and physiologically meaningful features of brain function (including oscillatory power, connectivity, and network characteristics) are extracted and visualized using two open-source and widely used Matlab toolboxes (EEGLAB and FieldTrip). We tested the pipeline in two large, openly available datasets containing EEG recordings of healthy participants and patients with a psychiatric condition. Additionally, we performed an exploratory analysis that could inspire the development of biomarkers for healthy aging. Thus, the DISCOVER-EEG pipeline facilitates the aggregation, reuse, and analysis of large EEG datasets, promoting open and reproducible research on brain function.
在神经和精神疾病中,生物标志物的发现严重依赖于应用于大规模数据集的可重复和透明的方法。脑电图(EEG)是识别生物标志物的一种很有前途的工具。然而,脑电图数据的记录、预处理和分析既耗时又依赖于研究人员。因此,我们开发了 DISCOVER-EEG,这是一个开放且完全自动化的流水线,可以轻松、快速地预处理、分析和可视化静息态 EEG 数据。遵循脑成像数据结构(BIDS)标准自动预处理数据,并使用两个开源且广泛使用的 Matlab 工具箱(EEGLAB 和 FieldTrip)提取和可视化大脑功能的生理相关特征(包括振荡功率、连通性和网络特征)。我们在两个包含健康参与者和精神疾病患者 EEG 记录的大型公开可用数据集上测试了该流水线。此外,我们还进行了一项探索性分析,这可能为健康老龄化的生物标志物开发提供启示。因此,DISCOVER-EEG 流水线促进了大型 EEG 数据集的聚合、再利用和分析,推动了大脑功能的开放和可重复研究。