Thanks, Martin! I think we’re just going to try and make one confound matrix (including the cosineXX regressors for hp filtering) during the first-level analyses in FSL. Based on all the related threads (see links below), it seems like that would be the best way of accounting for shared variance and avoid re-introducing noise.
Within that confound matrix, we would include the cosineXX regressors, the non-steady state regressors, and CSF/WM regressors alongside the aromaXX regressors. I’d like to limit loss of tDoF, but I wonder if there’s any advantage in also including the 6RP (or even the 24RP) motion parameters as an added layer of protection again head motion? I realize that some of the aromaXX regressors should be correlated with the rotations and translations (and their expansions), but I don’t think any of the evaluation papers (e.g., yours or the Lydon-Staley et al., 2019, Network Neuroscience) included a pipeline with AROMA and motion regressors.
Best,
Links to related threads in case it’s helpful for others:
Hello experts,
after running fmriprep, I get a very nice tsv with the computed confounds. Would you use all of them as nuisance regressors in a GLM? Or are some more suited for resting state data vs. task-based data?
Thanks!
Matteo
Hi all,
I ran into Chris M. at OHBM and he pointed out that when using the aCompCor regression strategy, the cosine regressors should be used too (this is not documented, we should change that)
Can anyone verify that the comparison below makes sense:
We are moving from bash scripts that implemented Thomas Yeo’s pipeline at MGH to fmriprep, and want to be able to compare traditional global signal regression to CompCor and/or ICA-AROMA:
For the aCompCor regression:
aCompCor00, aCompCor01, aCo…
Thanks very much! This makes sense.
I have come across another issue that baffles me. I have 5 runs for one participant and at least for 2 of them, the *space-MNI152NLin2009cAsym_desc-smoothAROMAnonaggr_bold.nii file has its first three volumes non-smoothed, it looked like the nonsteady volumes are added back after ica-aroma, but not smoothed. Again, this happens for two runs out of the 5 and for several subjects. Why do you think that is?
Hi all,
If I want to use ICA-AROMA to denoise my data, and use tcompcor and acompcor as additional nuisance regressors, does the order in which I apply them matter? Or do I need to find a way to apply them at the same time? To ask another way, am I in danger of reintroducing noise with another regressor after applying ICA-AROMA, since I’m applying those regressors (tcompcor, acompcor) to already partially cleaned data (ICA-AROMA denoised), while the regressors (tcompcor, acompcor) were derived …
Hello,
I was wondering if anyone had any suggestions about best practices for using fmriprep + ICA-AROMA processed data for a PPI analysis?
I have already seen a few threads about using data and noise regressors generated by the fmriprep + ICA-AROMA pipeline but I haven’t found a definitive answer to my question. I think the main issue here relates to the CSF and WM regressors, which are particularly important for PPI. I understand that in fmriprep these are calculated pre-AROMA, hence using t…
Hi all,
I’ve been using fmriprep with ica-aroma to analyze a multiband dataset from a 2 minute long continuous scan, and then applied a separate highpass filter. This procedure yielded an artifact, apparent when performing inter-subject correction of the full timecourses within each voxel. In the image below you can see unusual correlations in voxels that seem to include a lot of CSF, such as in sulci and ventricles (in addition to the expected higher correlations in auditory cortex). This arti…
Greetings,
I am testing the effects of various pre-processing “noise model” regressors (columns in the *_confounds.tsv from fmriprep) on rsfMRI connectivity’s association with motion (mean Framewise Displacement, in the *_confounds.tsv) on a dataset of ~325 young adults scanned on the same Prisma scanner. To do the noise model regression, I am using AFNI’s 3dTproject (in nipype), which affords simplicity in that it allows for regressing out “noise” time-series, censoring (…zeroing, or interpola…
Hi,
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
Cheers,
Thapa
Hello,
I am using fmriprep to preprocess my rsfMRI data and I am curious if appending ‘CSF’ and ‘WM’ columns from the *confounds.tsv (as well as perhaps other columns, e.g., ‘GlobalSignal’) to the MELODIC mixing matrix is an acceptable approach for simultaneously regressing out “noise” independent components and physiological signals. I have some Nipype code for doing this ( github.com/sjburwell/fmriprep_denoising ), but the process goes as follows:
run fmriprep with the AROMA option turned ‘…
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