Dear Mark,
Thanks for the response. I would ask you some questions more..
I have images with more than 1700 time points..
- I will reduce the components, do you think 100 could be ok? or something like 35? Any criteria in this choice?
I know that FIX works automatically detecting noise components, but I can not use that because I have no R and Matlab.
- Do you have a sense on when a standalone version will come out?
In the meanwhile I would ask you some suggestions in recognizing noise components. As far as I understood I should base my selection on anatomic shape, and power spectrum.
- At what frequency do generally heart beat and respiration related artifacts map?
- For commonly used tasks with stimuli lasting between 3 and 10 seconds, where should they map in the power spectrum? - --
- What do I need to know to estimate a task related signal frequency in the power spectrum?
- For let's say HR 60bpm,can I expect the the heart beat to map around 0.0116 Hz? So in the FSL report I should find it all on the left around a value of 16. But there I also find signals that look like task related.
- Are signals with a wide window of frequency most probably to consider noise?
Back to anatomic criteria, can I consider as rule of thumb bad components those having:
-stripe shape
-medulla intense signal (maybe swallow related or heart beat or respiration related?),
-brain edges locations, with positive signals on a side and negative on the other side?
-Orbitofrontal or low temporal strong activation?
Thank you very much for your time and any hint!