Thanks a lot for the prompt reply! I absolutely agree - clearly the variability between subjects makes a lot of sense. I select my ROI based on functional criteria, which means that (presumably) I take the voxels which process relevant information. I never get the overlap of these ROIs across subjects in search-light. I did not bring this point originally in order to simplify my question - I meant the ideal case where I have 10 identical twins in my group-level, with a complete ROI overlap :) But, even in this case I have a p-value issue which does not permit me to achieve significance in search-light.
BTW, why one should use a search-light which is substantially smaller than ROI? I deliberately make it of size of my ROI to make results more comparable between two analyses. I move my search-light each time one voxel in one of the directions, so each voxel participates in dozens of lights. At the end, I average all the predictions in each voxel. I would appreciate if you notify me once your paper is in press. On Mon, Feb 25, 2013 at 11:41 AM, J.A. Etzel <[email protected]>wrote: > On 2/25/2013 1:10 PM, Vadim Axel wrote: > >> Absolutely naive question: suppose I have single a-priori defined ROI >> where I get a modest group-level beyond chance prediction of >> p-value=0.01 (one-tail t-test vs. 0.5, across subjects). Now I run a >> group level whole-brain search-light and I am expected to find at least >> one cluster of beyond chance prediction in the environment of my ROI. >> Correct? >> > No. > > I have a paper in (hopefully the last cycle of) review that goes into > detail about these issues. But here's a brief version of some of the > relevant ideas. I'm assuming you're using a linear SVM and proper > cross-validation, and also that the searchlight is substantially smaller > than the ROI. > > Two possible explanations come to mind: > 1) The single searchlights are too small to hold enough voxels to classify > accurately, but the ROI can, because there is weak information present in > much of the ROI. Linear SVMs can combine weak information from many voxels, > so can sometimes classify better with more voxels. > > 2) There is a lot of spatial variability between subjects. Suppose only a > small part of the ROI is informative. If that part falls withing the ROI > for everyone, then the ROI might classify well at the group level. But if > each person only has a small informative area on their searchlight map, the > group map could come out non-significant (people's maps don't overlap > enough). > > > A few suggestions: > 1) If your hypothesis is about the ROI, stick with the ROI-based analysis, > adding control ROIs (or whatever) as necessary, but not doing the > searchlight analysis. > > 2) If you need the searchlight analysis for a particular purpose, do some > sensitivity testing, and look closely at the single-subject maps. For > example, how much do the maps change with different searchlight radii? Did > you normalize to atlas space before or after the searchlight? Did you > smooth the data? Smooth the individual subject maps? etc. > > 3) Check the sensitivity of the ROI-based finding. For example, How much > does it change if the ROI boundaries are altered slightly? How much > variation is there between subjects - does the ROI classify well in most > everyone, or just a few people? > > > Hope this gets you started, and good luck. > Jo > > > > -- > Joset A. Etzel, Ph.D. > Research Analyst > Cognitive Control & Psychopathology Lab > Washington University in St. Louis > http://mvpa.blogspot.com/ > > ______________________________**_________________ > Pkg-ExpPsy-PyMVPA mailing list > Pkg-ExpPsy-PyMVPA@lists.**alioth.debian.org<[email protected]> > http://lists.alioth.debian.**org/cgi-bin/mailman/listinfo/** > pkg-exppsy-pymvpa<http://lists.alioth.debian.org/cgi-bin/mailman/listinfo/pkg-exppsy-pymvpa> >
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