Demonstrating quality control procedures for fMRI in DPABI

Quality control (QC) is an important stage for functional magnetic resonance imaging (fMRI) studies. The methods for fMRI QC vary in different fMRI preprocessing pipelines. The inflating sample size and number of scanning sites for fMRI studies further add to the difficulty and working load of the QC procedure. Therefore, as a constituent part of the Demonstrating Quality Control Procedures in fMRI research topic in Frontiers, we preprocessed a well-organized open-available dataset using DPABI pipelines to illustrate the QC procedure in DPABI. Six categories of DPABI-derived reports were used to eliminate images without adequate quality. After the QC procedure, twelve participants (8.6%) were categorized as excluded and eight participants (5.8%) were categorized as uncertain. More automatic QC tools were needed in the big-data era while visually inspecting images was still indispensable now.

Automated summary

Quality control is crucial in fMRI studies, as it helps to eliminate images without adequate quality. In this study, we used DPABI pipelines to preprocess an open-available dataset and illustrated the QC procedure. Results showed that 8.6% of participants were excluded and 5.8% were categorized as uncertain after the QC procedure. While automatic QC tools are needed in the big-data era, visually inspecting images is still essential.