14.2.10. Birn (2023). Quality control procedures and metrics for resting-state functional MRI¶
Introduction¶
Here we present commands used in the following paper:
- Birn RM (2023). Quality control procedures and metrics for resting-state functional MRI. Front Neuroimaging 2:1072927.
Abstract: The monitoring and assessment of data quality is an essential step in the acquisition and analysis of functional MRI (fMRI) data. Ideally data quality monitoring is performed while the data are being acquired and the subject is still in the MRI scanner so that any errors can be caught early and addressed. It is also important to perform data quality assessments at multiple points in the processing pipeline. This is particularly true when analyzing datasets with large numbers of subjects, coming from multiple investigators and/or institutions. These quality control procedures should monitor not only the quality of the original and processed data, but also the accuracy and consistency of acquisition parameters. Between-site differences in acquisition parameters can guide the choice of certain processing steps (e.g., resampling from oblique orientations, spatial smoothing). Various quality control metrics can determine what subjects to exclude from the group analyses, and can also guide additional processing steps that may be necessary. This paper describes a combination of qualitative and quantitative assessments to determine the quality of fMRI data. Processing is performed using the AFNI data analysis package. Qualitative assessments include visual inspection of the structural T1-weighted and fMRI echo-planar images, functional connectivity maps, functional connectivity strength, and temporal signal-to-noise maps concatenated from all subjects into a movie format. Quantitative metrics include the acquisition parameters, statistics about the level of subject motion, temporal signal-to-noise ratio, smoothness of the data, and the average functional connectivity strength. These measures are evaluated at different steps in the processing pipeline to catch gross abnormalities in the data, and to determine deviations in acquisition parameters, the alignment to template space, the level of head motion, and other sources of noise. We also evaluate the effect of different quantitative QC cutoffs, specifically the motion censoring threshold, and the impact of bandpass filtering. These qualitative and quantitative metrics can then provide information about what subjects to exclude and what subjects to examine more closely in the analysis of large datasets.
- Study keywords:
artifacts; connectivity; fMRI; motion; quality control
Main programs:
3dWarp, 3dSkullStrip, @SSwarper, 3dTshift, 3dcalc,
align_epi_anat.py, 3dNwarpApply, 3dAllineate,
1dnorm, 1d_tool.py, 1deval, 3dROIstats, 3dTstat,
3dDeconvolve,
3dhistog, 3dTcorr1D
Download scripts¶
... or copy+paste the following in a terminal:
git clone https://github.com/rbirn/OpenQC.git
Note: This work was one of several contributed to the following Frontiers Research Topic project, described here:
- Taylor PA, Etzel JA, Glen D, Reynolds RC (2022). Demonstrating Quality Control (QC) Procedures in fMRI.
The datasets analyzed within it are publicly available and located here:
- Taylor PA, Etzel JA, Glen D, Reynolds RC, Moraczewski D, Basavaraj A (2022). FMRI Open QC Project. DOI 10.17605/OSF.IO/QAESM
View scripts¶
Because there are so many scripts for this project, we recommend downloading the full set from the github pages, above. There are helpful supplemental notes there, as well.
