Di Xin, Biswal Bharat B
Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States.
Front Neuroimaging. 2023 Jan 10;1:1070151. doi: 10.3389/fnimg.2022.1070151. eCollection 2022.
Functional MRI (fMRI) has become a popular technique to study brain functions and their alterations in psychiatric and neurological conditions. The sample sizes for fMRI studies have been increasing steadily, and growing studies are sourced from open-access brain imaging repositories. Quality control becomes critical to ensure successful data processing and valid statistical results. Here, we outline a simple protocol for fMRI data pre-processing and quality control based on statistical parametric mapping (SPM) and MATLAB. The focus of this protocol is not only to identify and remove data with artifacts and anomalies, but also to ensure the processing has been performed properly. We apply this protocol to the data from fMRI Open quality control (QC) Project, and illustrate how each quality control step can help to identify potential issues. We also show that simple steps such as skull stripping can improve coregistration between the functional and anatomical images.
功能磁共振成像(fMRI)已成为研究大脑功能及其在精神和神经疾病中变化的常用技术。fMRI研究的样本量一直在稳步增加,越来越多的研究来自开放获取的脑成像数据库。质量控制对于确保成功的数据处理和有效的统计结果至关重要。在此,我们概述了一种基于统计参数映射(SPM)和MATLAB的fMRI数据预处理和质量控制的简单方案。该方案的重点不仅是识别和去除有伪影和异常的数据,还确保处理过程正确执行。我们将此方案应用于fMRI开放质量控制(QC)项目的数据,并说明每个质量控制步骤如何有助于识别潜在问题。我们还表明,诸如颅骨剥离等简单步骤可以改善功能图像和解剖图像之间的配准。