Mohammadi Seyyed Erfan, Shabani Hasti, Begmaz Mohammad Mahdi, Dehaghani Narjes Soltani
Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran.
Department of Computer Science, Shahid Beheshti University, Tehran, Iran.
Psychophysiology. 2025 Jul;62(7):e70109. doi: 10.1111/psyp.70109.
Magnetoencephalography (MEG) data are often contaminated by various noise and artifacts, necessitating meticulous preprocessing. However, no pipeline has comprehensively examined all aspects of the different types of MEG noise, nor has any automatic preprocessing pipeline ever been presented. The impracticality of performing visual inspections for the preprocessing of large-scale resting state datasets, combined with the absence of automation, hinders the ability to take advantage of such datasets, including increased generalizability. Additionally, the absence of a standardized sequence for MEG preprocessing steps affects the reproducibility of research studies. Our MEG Automatic Pipeline (MEGAP) can automatically reduce noise and artifacts and is the first pipeline that can be used to preprocess large-scale resting state MEG datasets. We developed this pipeline by integrating and sequencing recent algorithms for each preprocessing step, ensuring automated execution, standardization, and organized outputs. The key features of MEGAP include correcting head movements, removing line noise without applying a notch filter, annotating muscle artifacts, removing sensor and environmental noise, and automatically detecting artifact components in Independent Component Analysis (ICA). We validated our pipeline using simulated and experimental data from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) dataset. Substantial improvements were achieved based on different evaluation criteria such as Normalized Mean Square Error (NMSE), correlation, and Signal to Noise Ratio (SNR). MEGAP provides a robust framework for MEG data preprocessing, significantly reducing the manual effort in preprocessing by automating the required steps, contributing to more consistent and reproducible neuroimaging research outcomes, and facilitating the analysis of large-scale MEG studies.
脑磁图（MEG）数据常常受到各种噪声和伪迹的污染，因此需要进行细致的预处理。然而，目前尚无任何流程全面检查过不同类型MEG噪声的各个方面，也从未有过自动预处理流程被提出。对大规模静息态数据集进行预处理时进行视觉检查不切实际，再加上缺乏自动化，这阻碍了利用此类数据集的能力，包括降低了可推广性。此外，MEG预处理步骤缺乏标准化顺序会影响研究的可重复性。我们的MEG自动处理流程（MEGAP）能够自动减少噪声和伪迹，是首个可用于预处理大规模静息态MEG数据集的流程。我们通过整合并按顺序排列每个预处理步骤的最新算法来开发此流程，确保自动执行、标准化和有条理的输出。MEGAP的关键特性包括校正头部运动、不使用陷波滤波器去除电源噪声、标注肌肉伪迹、去除传感器和环境噪声，以及在独立成分分析（ICA）中自动检测伪迹成分。我们使用来自剑桥衰老与神经科学中心（Cam-CAN）数据集的模拟和实验数据对我们的流程进行了验证。基于归一化均方误差（NMSE）、相关性和信噪比（SNR）等不同评估标准，取得了显著改进。MEGAP为MEG数据预处理提供了一个强大的框架，通过自动化所需步骤显著减少了预处理中的人工工作量，有助于实现更一致且可重复的神经影像研究结果，并促进大规模MEG研究的分析。
