Good afternoon,

Sorry about the hyper-vigilance, but am working pretty much solo and need to be sure of myself...

I have single 10 minute runs of resting state fmri- one run per subject  

I took the following steps, as below, and my question is if this is the best way of going about it?  (image is 37 slices, 3.4 mm thick)
1.  BET structural image for each individual and the single MNI 2 yo image.  
2. FEAT with:
DATA 
- high pass filter at 100.  (I tried lower numbers like 65 and seemed to allow too much noise)
PRE-STATS
- motion correction with MCFLIRT
- B0 unwarping
- Slice time correction for interleaved
- Brain extraction
-Smoothing 5 mm
-highpass temporal filtering
- MELODIC ICA data exploration
STATS
-FILM prewhitening
-motion parameters added to moodel
POST-STATS
-cluster threshold 2.3, P 0.05 (although don't think I really need this step, but was interesting to see what was activated by this analysis)
REGISTRATION
-Main structural with Linear Normal Search and 12 DOF
-Standard space with Linear Normal Search and 12 DOF

Then I viewed the 174 ICA noise sources and picked the ICs that were most obviously noise and reran FEAT renduring the  Denoised ICA functional data, which I then went to MELODIC: 
DATA - same as above
PRE-STATS - same as above
Registration - same as above
STATS
- Variance-normalise timecourses
-Automatic diminsionality estimation
-Single session ICA
POST-STATS
-Threshold IC maps 0.5
- No model or contrasts as this is purely resting state data

It looks like I have redundancy in my steps between FEAT and MELODIC, but not sure about the work around with denoising... and motion correction added in... Although do remember Christian Beckmann saying this was Gaussian source of noise so would be accounted for in MELODIC.  

Most Gratefully,
Varina