Dear PyMVPA community, I would like to get your feedback for an analysis I'm trying, I think I'm in the right track but I don't want to miss something.
I have a block design in which I presented images belonging to 4 categories and I took 6 acquisitions. The thing is that I scanned dogs and humans. I want to test whether both species represent the categories in a similar way. I took the coordinates of a voxel of interest in the human brain, took the voxels withing a sphere around it and calculated the euclidean distance between blocks. Then I took this "goal distance vector" and calculated the spearman correlation of similar vectors but obtained in a sphere around each voxel a dog's brain. I repeated this procedure for each human and each dog. Thus getting correlation maps for each dog x human. Then I threshold the maps and binarized the results. I finally added the maps and creating a group map which tells me how many correlations were above chance on each voxel. To test wheter it is significant or not, I took random coordinates in the human brain and repeated the process so I can get a distribution of how many false positive correlations I can get in a map, and then I plan to use this distribution to have a statistical threshold. Does it make sense to you? Sorry for the long explanation :s Regards Raul
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