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I think it makes more sense to add a physio property rather than treating physio as a confound - this gives more flexibility for instituting the extra steps we need (searching for physio, allowing different meta-data, calculating different derivatives).
Some thoughts I hope will be helpful for any MRIQC shifts to plotting raw/minimally preprocessed physio signals on top of the carpet plot:
Explicitly initializing a dataframe for each physio signal within FMRISummary() in a set of allowable use cases seems like the simplest way to implement this, considering that the sampling rate could potentially vary across each modality (we discussed cardiac, respiratory, and eye-tracking) and so dataframes unfortunately can't necessarily be shared (certainly not with the confounds)
To avoid being too clunky, it might be beneficial to be proscriptive in choosing a representative signal within any cardiac/respiratory brackets (e.g. privileging PPG within cardiac to avoid having to do to much artefact-related processing)
Within fMRIplot(), confoundplot() is very flexible as it does not actually scale the x-axis in any meaningful way. So, as long as data is pre-cropped to align with the scan, there's no need to use a separate plotting function
Modify the reportlet (https://github.com/nipreps/nireports/blob/main/nireports/reportlets/modality/func.py) so that physiological signals are also "pluggable". This implies:
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