Hi all,
I’m working on running FSL GLM analyses on fMRIprep preprocessed data with AROMA. I’ve added the non-steady-state volume confounds to my first-level confound matrix to avoid the issues associated with including those volumes in analysis, but was hoping to gain a better understanding of why the number of non-steady-state volumes varies from 0-3 between subjects/scans despite using the same scanner protocols. What methods / metrics is fMRIprep using to determine whether or not a volume is classified as non-steady-state?
Thanks!
Natalie
FMRIPREP uses a simple image intensity outlier detection heuristic implemented in Nipype. More details and references here https://github.com/nipy/nipype/blob/d2a48e1eb/nipype/algorithms/confounds.py#L974-L995
A lot of these processes are stochastic and depend on environment (for example temperature) so it’s possible that steady state is reached at different times from one scan to another.
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Great, thank you! Is the global signal value used for this calculation the same as that reported in the global signal column of the confounds-regressors tsv output of fMRIprep?
I’m not 100% sure but I think global signal uses brain mask. Non-steady state detector averages values over the whole image.