Excluding outliers with fmriprep output or mriqc

Dear Neurostars experts,

I am analyzing fmri data (resting state) along with T1w, T2w and T2star and already successfully performed fmriprep on this dataset.

I am trying to exclude outliers in case they don’t comply with minimum quality standards.

However, I don’t really know how to do it. I tried to obtain IQMs with mriqc on the raw BIDS data set, but this doesn’t help me excluding the preprocessed outliers.

Would anyone know how to exclude the preprocessed outliers and under which criteria/factor parameters and threshold(s)?

Thanks in avance,


Loïc D.

Hi Loïc,

First, I’d like to point out that QC of the raw data should be performed before fMRIPrep (or any other processing for this matter). That means that the proper workflow is running MRIQC first, doing the QC on the original data. Pre-specify the participants to be excluded for low quality reasons and then run fMRIPrep only on those who survived.

That said, I’ll try to reply to your question. MRIQC generates one visual report per participant, modality, task, run etc. If time permits, ideally you should open up all these reports one-by-one, screen them on your browser and annotate them with a quality rating and potentially noting MR artifacts you might have found. About 1yr ago I started a walk-through guide about the anatomical reports of MRIQC that you might find useful: https://1387-54756129-gh.circle-artifacts.com/1/home/ubuntu/scratch/docs/interpreting.html

Please make sure you are using the latest MRIQC version (i.e. 0.15.0) because I fixed some critical bugs and improved the reports. They come with a handy widget for annotating the quality. Make sure you click the “Download” button (that will get you a json file per image, you can collate all those jsons into one excel table for your records). You may want to click also on “Post to API”, which basically sends your annotations to a database where they are crowdsourced (see https://doi.org/10.1038/s41597-019-0035-4).

If time is limiting you, then there are some lazy approaches using MRIQC “group reports”. That will help you catch outlier images that fall far from the common distribution of your sample. Those are the first images to look at (please have in mind that they could be outliers for good reasons too!).

Some (unfortunately muted) videos showing how that works are presented here: https://mriqc.readthedocs.io/en/stable/reports.html#demo-anatomical-reports



Dear @oesteban,

Thanks a lot for your help!

Is there an IQM or combination of IQMs that is more important than others and that would be critical for data exclusion?



Loïc Daumail

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