High-pass filter and ICA-AROMA on multiband-data

[dlevitas]

Hi George,

@G.Tertikas

If I were to apply a highpass filter to the fMRIprep output without the ICA_AROMA and then run ICA_AROMA on the filtered data, would the results be worse or better compared to the ICA_AROMA cleaned data produced automatically by fMRIprep?

I believe you would want to perform the opposite (i.e. apply highpass filter to the ICA-AROMA denoised fMRIPrep data). See this post for more information.

@G.Tertikas

Would that influence the data in my case, where I have multiband data with 2000 slices (and thus a lot of components in ICA)?

I assume by “slices” you’re referring to volumes. With such large data, fMRIPrep provides an --aroma-melodic-dimensionality option where you can specify the number of components generated (e.g. --aroma-melodic-dimensionality 100. See this post for guidance.

@G.Tertikas

I do not seem to understand exactly what output to use in a subsequent fMRI analysis. Would you use the ICA_AROMA denoised data and the motion regressors already calculated in a GLM? Would you altenatively need to do a high-pass filter of the ICA cleaned data, and would that introduce any noise in the data? Would using the motion regressors in combination with ICA introduce new noise in the system?

If you’re using the ICA-AROMA denoised fMRIPrep output data then you don’t need to include the motion parameters; doing so may reintroduce noise.

I should also note that ICA-AROMA was not designed on multi-band data, so be aware of that. However, from what I’ve been on Neurostars and with my own data, ICA-AROMA works reasonably well on multi-band data.

Hope this helps a bit.