ICA AROMA agg vs non-agg




I am aware of using the --use-aroma flag, to run ICA AROMA. However, I’m curious whether there is an option to choose to run agg vs non-aggressive ICA aroma. If not, is there a way I could use the outputs from the --use-aroma flag to perform aggressive ICA aroma?

Settings: singularity, fMRI prep v1.2.5



Yes that is possible. To perform aggressive denoising after running FMRIPREP you need to regress out the aroma_motion_* regressors found in _desc-confounds_regressors.tsv file from the _desc-preproc_bold.nii.gz file.


Thank you @ChrisGorgolewski. I will give that a go :slight_smile:


I just want to clarify something. If we’re using the aggressive denoising strategy on the _desc-preproc_bold.nii.gz data, do we also need to include the motion parameters (trans_x trans_y trans_z rot_x rot_y rot_z) in our nuisance regression, or will they already be regressed out by the ICA-AROMA confounds?

I realize that we would want to include these motion parameters in a nuisance regression if we were just using the _desc-preproc_bold.nii.gz data without ICA-AROMA denoising; however, does it make sense to include them with _desc-preproc_bold.nii.gz + aggressive ICA-AROMA confounds, (or even for analyzing the non-aggressive smoothAROMAnonaggr_bold.nii.gz data?).

Thanks for the clarification.


As described in the original paper and fMRIPrep’s documentation, the “aggressive” mode of ICA-AROMA results in a series of confound regressors.

In principle, there is nothing theoretically incorrect in using the aggressive ICA-AROMA regressors with motion parameters. I would recommend you to check on the correlation matrix to see whether the motion parameters are adding any new information or they are basically captured by some other confounds. That means that the GLM will probably “favor” one over the set of highly correlated confounds to explain noise, and that does not have any further implication (unless you are interested in that fitting particularly).

You are right here. Those time-series have gone through denoising, so a second step adding motion regressors may actually re-induce motion artifacts in your signal.

Please check my reply for accuracy, @rastko.


I’d only add this:

Correlation with the 6 realignment parameters (trans_x trans_y trans_z rot_x rot_y rot_z) is an explicit criterion in the AROMA classifier; components that are more correlated with motion are more likely to be classified as nuisance by ICA-AROMA.* Furthermore, in the aggressive model, the nuisance components aren’t competing with signal-of-interest components to explain variance in the dataset; thus, all of the variance that can be explained by nuisance components should be removed in the regression step.

For the above reasons, there is likely to be substantial collinearity between nuisance components and realignment parameters; fitting realignment parameters in a separate model could be redundant or could remove additional temporal degrees of freedom from the data for little benefit. The original implementation of ICA-AROMA recommended supplementing the ICA-based denoising procedure with a confound model that incorporated mean signal from white matter and cerebrospinal fluid compartments (white_matter and csf in fmriprep), but didn’t include the 6 motion parameters. We’ve found, along with others, that adding the mean global signal into the model can also substantially improve denoising performance, but some reviewers might still find it controversial.

So, in short, in addition to Oscar’s warning about potential reintroduction of artefactual variance, there might be other reasons not to include the realignment parameters in the nuisance regression.

*Technically, the criterion uses a linear discriminant analysis on a 2D feature space including (i) the maximum temporal correlation with any of the motion parameters and (ii) spatial overlap with a mask indicating voxels at the edge of the brain. The details are in Fig 2A in the original Pruim article.