Hi Eugene,
Could you please elaborate briefly -- what sort of wrapper script are
you thinking about to accomplish this?
thanks,
-MH
On Thu, 2010-06-24 at 16:15 +0000, Eugene Duff wrote:
> Hi Michael -
>
>
> FEAT is the same as SPM - a voxel with 0 in any of the inputs will be
> excluded from the analysis. I'm not sure maps where the values of
> different voxels reflect statistics associated with different subsets
> of the total input set would usually be very interpretable. I doubt
> other packages have the capability you're looking for. It shouldn't
> be all that hard to write a wrapper script to FEAT that does what you
> want though.
>
>
> Cheers,
>
> Eugene
>
> --
>
> Centre for Functional MRI of the Brain (FMRIB) | University of Oxford
> John Radcliffe Hospital | Headington
> OX3 9DU | Oxford | UK
>
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> --
>
>
> On 24 June 2010 15:45, Michael Harms <[log in to unmask]> wrote:
> Hello,
> Continuing my fixation with voxels that are missing data
> (i.e., voxels
> with zeros), I was wondering how these voxels get treated
> going to a 2nd
> level analysis (or similarly, how voxels that weren't actually
> acquired
> at acquisition, due to limited slice coverage, get treated as
> the first-
> level images are transformed into standard-space for the 2nd
> level
> analysis).
>
> In particular, are such 0's treated as just any other
> legitimate value,
> or are they used to create a mask for the 2nd level images?
> And, if the
> latter, does a single instance of a 0 in a voxel of one
> subject result
> in a 0 at that voxel in the 2nd level results
>
> Basically, I'm trying to determine if there is any common
> software
> package for image statistics that can compute voxel-wise
> statistics
> using the subset of subjects that have data available at that
> voxel in
> standard-space, while just "ignoring" the subjects that don't
> have data
> at the voxel -- i.e., allowing for varying d.f's across
> voxels in the
> statistic computation. (For example, from what I can tell so
> far, SPM
> doesn't appear to have this capability, as a NaN at a voxel in
> a single
> subject results in a NaN at that voxel in the group level
> contrast).
>
> I see that FEAT does have an option in Higher-level analysis
> to
> automatically "de-weight" outliers, which seems quite similar
> in concept
> (and perhaps could even be used to achieve the behavior that I
> seek?)
>
> thanks,
> -MH
>
>
> --
> Michael Harms, Ph.D.
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> Washington University School of Medicine
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