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Dear Kim,

yes, for running AROMA you have the pipeline mostly correct. Just note that
if you run AROMA it will do the component classification itself (never
hurts to check though) and incorporate fsl_regfilt in one go, so no need to
run that yourself.

After AROMA you'd indeed run a first level statistics analysis, but with
the temporal filtering in the preprocessing turned on.

Yes, when running AROMA on a feat directory the masking is taken care of
for you.

hth,
Maarten


On Thu, Aug 10, 2017 at 11:59 PM, Kim Meier <[log in to unmask]> wrote:

> Hi all, I'm currently applying all the new knowledge I got from this
> year's FSL course. I have questions regarding using ARMOA on single-subject
> task MRI to remove noise components. The FSL course/practical had a very
> useful tutorial on looking at/identifying noise and motion components
> manually and with FIX and AROMA. However it didn't contain a full example
> on how to incorporate these into a task-based analysis, so could I get some
> feedback/guidance on whether the strategy I've inferred is
> correct/reasonable?
>
> To confirm, the general strategy is:
>
> (Step 1) Load in original 4D data, run a First-level analysis:
> Preprocessing (in FEAT GUI) and do everything I would be planning to do in
> the full analysis (pre-stats, and registration)
>
> (Step 2) Use whatever method (AROMA, FIX, manual) to ID components that
> will be removed
>
> (Step 3) Use fsl_regfilt to remove these components from the original 4D
> data
>
> (Step 4) Load in this new denoised data and run a First-level analysis:
> Statistics (in FEAT GUI) .. continue as normal with the stats and post-stats
>
> I've been reading about ICA-AROMA in the provided papers (both very
> helpful) but I am still a big confused about the correct steps for
> implementing it as no one in my lab has taken this strategy yet. The manual
> suggests leaving the MELODIC work to the AROMA command; it also suggests
> not doing temporal filtering at that stage. So if I understand this
> correctly, if I'm planning to run ICA-AROMA after FEAT the specific
> strategy I would do is:
>
> (Step 1) Load in original 4D data, run a First-level analysis:
> Preprocessing (in FEAT GUI) and do everything I would be planning to do in
> the full analysis (pre-stats and registration) .
>
> Question: in pre-stats, (a) don't turn on MELODIC, (a) turn off BET, and
> (a) turn off temporal filtering. Is that correct? Do I also (4) change
> "High pass filter cutoff" in the "Data" tab to something else so it can be
> applied later - e.g. after doing the full model so I can estimate the high
> pass filter from the design? (Or is this completely ignored until the
> post-processing stage anyway?)
>
> (Step 2) Run AROMA by specifying the feat directory and the output.
>
> Question: it's not clear to me how I can incorporate a mask here (since
> using FEAT's extraction is not advised for AROMA). If I'm running AROMA on
> a feat directory is this step taken care of for me (as the manual states
> "MELODIC will be run within ICA-AROMA using the appropriate mask" - that
> means it will do the bet for me on the example_func image or should I be
> doing a bet on this image myself before running AROMA)?
>
> (Step 3) Use fsl_regfilt to remove these components from the original 4D
> data after inspecting the identified components in fsleyes.
>
> Question: Before I move on to step 4, is it necessary to do the highpass
> temporal filtering by doing another preprocessing run with only highpass
> temporal filtering (everything else unchecked)? Based on the Pruim et al
> (2015) paper Fig 1 it looks like the temporal filtering was done as a step
> between what I describe as steps 3 & 4.
>
> (Step 4) Load in this new denoised data and run a First-level analysis:
> Statistics (in FEAT GUI) .. continue as normal with the stats and
> post-stats.
>
> Thank you for your feedback. If I've completely missed resources somewhere
> that has all this information, please let me know!
>
> Cheers,
> Kim
>



-- 
Maarten Mennes, Ph.D.
Senior Researcher
Donders Institute for Brain, Cognition and Behaviour
Radboud University Nijmegen
Nijmegen
The Netherlands

Google Scholar Author Link
<http://scholar.google.com/citations?user=pLlSTVgAAAAJ&hl=en>