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Dear SPM Experts,

I need help building a statistical analysis in SPM8. I know exactly what I 
want to do, but there seem to be a lot of different ways of doing it.

Here's the plot: Of 40 subjects of different age groups (Age 8 to 18) I 
have activation images (.nii) of an oddball task, generated by 3D source 
reconstruction of EEG files. They all did the same oddball task in the 
evening and in the morning, after sleep.

In earlier studies my research group found that the GMFP in such a task 
decreases over night, so now I wanted to test if the involved areas also 
decrease over night.

My first idea was to use imCalc to subtract the morning-images from the 
evening-images, but I encountered the problem, that the areas involved in 
solving the task differ in differerent age groups, so of course those 
differential images also show different voxels involved. 
So even though I find a greater neuronal response in the evening in all 
subjects, it makes no sense to t-test the differential images, since the 
remaining activation is in different voxels.

The next idea was to define ROIs of the same size in all differential 
images (evening minus morning) and average the activation therein. Does 
this make sense? What functions of the fMRI part of SPM8 is able to do 
this? 

Apart from this, as I am primarily interested in activation of frontal 
regions, is it necessary to cut away the rest of the activation, for 
exmaple by using MarsBaR? Or is there a tool in SPM8 wherein I can set all 
areas I'm not interested in to zero?

Another idea was to simply use a two-paired dependant t-test using both 
morning and evening images, but I think this won't get me any information 
about the decrease of activation that happened over night.


Also, I'd be interested if the decrease of activation (or of involved 
clusters, respectively) changes with age. How should this be done? Do I 
have to use the differential images or can I work with the images of 
evening and morning?


Any hints, solutions and clues are greatly appreciated.

Thank you,

Urs Bachofner    
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