I am under the impression that fslf_sbca has not been officially released yet:
https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=fsl;391e9b62.1107

I was playing around with fsl_sbca but was unsure of how to enter the results from fsl_sbca into a group analysis.  Is there documentation showing how to use fsl_sbca?

Thanks,
- BettyAnn



> Hi,
> 
> I would try using the fsl_sbca function instead of going through feat.  It
> may be a bit simpler.
> 
> As for what you did in feat, you wouldn't want to prewhiten the data, but
> that probably didn't cause your issue.  You would also need to normalize
> your seed after extracting from the res4D image *even if you already
> normalized the res4d.
> 
> Most likely, if you just getting speckled results, Feat couldn't find the
> brain.  Check the mask that it created for your analysis and verify that it
> actually covers the whole brain.  If it didn't, you'll need to double check
> how you added 100 to the residual image.  The purpose of that was to make
> the mean nonzero so feat could find the brain, if you just added 100 to the
> res4d image, that won't work.  You'd need to multiply the mask by 100, add
> it to the res4d and be sure to use -odt float in your fslmaths command to
> save the output in floating point.
> 
> It may be easier to just use fsl_sbca.
> 
> Cheers,
> Jeanette
> 
> On Wed, Mar 21, 2012 at 7:35 AM, Dr. David Watson <[log in to unmask]>wrote:
> 
> > 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