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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 > > Ph: +44 (0) 1865 222 523 | Mob: +44 (0) 7946 362 059 | Fax: +44 (0) > 1865 222 717 > > -- > > > 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. > -------------------------------------------------------------------- > Conte Center for the Neuroscience of Mental Disorders > Washington University School of Medicine > Department of Psychiatry, Box 8134 > Renard Hospital, Room 6604 Tel: 314-747-6173 > 660 South Euclid Ave. Fax: 314-747-2182 > St. Louis, MO 63110 Email: [log in to unmask] > -------------------------------------------------------------------- > > >