Dear FSL
I am currently engaged in processing rs-fMRI data from a group of neonates.
I am trying to get to grips with the steps involved in removing nuisance signals and then processing the resulting residual 4D images for seed based connectivity analysis.
I am having some difficulty and wonder if I am making some basic error somewhere.
When I do the final FEAT analysis on the Res4D signals I tend to get little statistically significant clusters even in and around the seed regions. So far I have tried Auditory (Left) and Motor (left) seeds (cubes of 5mm side) located at typical MNI spatial locations employed elsewhere.
The nuisance ts's I am using are:
Whole brain - average ts inside brain mask
WM - 5mm radius masks based on two (R/L) deep frontal white matter areas (averaged ts) - identified from WM segmentation
CSF - masks based on lateral ventricles (R/L) (average ts) - identified from CSF segmentation
These masks are formed for each individual subject
After running the initial feat with motion signals and the three nuisance ts above (intensity normalisation on, FILM prewhitening off) the Res4D is normalised (subtract mean and divide by std and add 100) and submitted to another feat analysis using one EV based on a chosen seed region mask ts, extracted from the Res4D image.
The settings I am using for this final stage are:
Motion Correction - off
Slice Timing - off
BET extraction - off
Intensity Normalisation - off
FILM prewhitening - on
Model: Motion correction off; EV based on seed region added
Does this look appropriate. Glad for any suggestions.
David
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