Hi,

The difference is probably due to the interpolation differences - in  
one case the data has been upsampled to standard space before applying  
the ROI, in the other the ROI is resampled into native space.

The other difference is due to the peak-peak height of the "effective  
regressor" calculated for each contrast.  In simple designs this can  
be the same as the PE for simple contrasts and for more complex  
designs with confounds etc it can be slightly different - see the  
NeuroImage paper on efficiency estimation.

Cheers.



On 21 Apr 2009, at 04:56, Jessica Smith wrote:

> Hello,
>
> Ive just run an fMRI experiment in which 14 participants completed  
> two runs
> of a task on two test days - so they have 2 runs for a placebo day  
> and 2 runs
> for a drug day. I have combined these runs in a higher level  
> analysis for each
> participant and then at the group level so I have a placebo gfeat  
> and drug
> gfeat.
>
> I would now like to investigate some ROIs using FeatQuery, and would  
> like to
> get percentage change values for these ROIs. But am very unsure  
> whether to
> do this by feeding in each Cope.feat from the drug & placebo gfeats.  
> I have or
> whether I should feed in each individual lower level feats. from  
> each run and
> average over these? The task was a block design, with 6 different  
> conditions
> plus rest periods on for 24 secs.
>
> As I was unsure I ran all of the above analyses to see if averaging  
> over the
> otuput from the two lower level Feat Query analyses would give me  
> the same
> percentage change value to that from the output from the gfeat Feat  
> Query
> analysis. However this doesnt seem to be the case i.e.,
>
> Outputs from FeatQuery's:
>
> - Mean % signal change (stats/cope) when input was Cope.feat from  
> Gfeat
> analysis (combination of run1 and run2) =  0.276
>
> - Mean % signal change (stats/cope) when input was Lower level feat  
> Run 1
> =  0.386
>
> - Mean % signal change (stats/cope) when input was Lower level feat  
> Run 2
> =  0.08794
>
> The average of 0.386 and 0.08794 isnt 0.276 as I had expected it  
> would.
>
> (I have attached an excel file with the values from the FeatQuery  
> analyses)
>
> Am I doing something wrong? Or have I missed something or need to  
> correct
> for something? Or is one method more accurate than the other?
>
> Also why is it the case the the % change values for the PE and COPE  
> are the
> same when I run FeatQuery on the Cope.feat from the gfeat analysis  
> but the
> PE and COPE values differ from each other when I run the FeatQuery  
> on each
> of the lower level feats that make up the gfeat???
>
> Any advice or suggestions would be greatly appreciated,
>
> Many many thanks in advance.
>
> Jess Smith
>
>
>
>
> <ROI_ FEATQUERY_outputs_JessSmith.xls>


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Stephen M. Smith, Professor of Biomedical Engineering
Associate Director,  Oxford University FMRIB Centre

FMRIB, JR Hospital, Headington, Oxford  OX3 9DU, UK
+44 (0) 1865 222726  (fax 222717)
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