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Thank you. On Tue, Jul 7, 2009 at 9:57 AM, Mark Jenkinson <[log in to unmask]> wrote: > Hi, > > If you are doing a higher level analysis then all of your subjects should > be in > the same (standard) space. Hence it doesn't really matter which image you > use as the background image (which is what example_func is used here for). > If you look at the two images they should look pretty much the same, if not > then > there may be issues with your registrations to standard space. > > All the best, > Mark > > > On 1 Jul 2009, at 19:52, Sushravya Raghunath wrote: > > Experts, >> >> I have Young (controls and patients) and Old (controls and patients) data. >> I want to carry out conjunction analysis of (Young control > Young patients) >> and (Old controls > Old patients). I have the third level feat analysis >> results for Young and Old run separately with contrast control > patients. >> >> I am trying to use easythresh_conj as mentioned in the quoted mail below >> as p.s. >> >> In easythresh_conj stats/zstat1 stats/zstat2 mask 2.3 0.05 example_func >> grot, if I use zstat1 of Young and zstat1 of Old then what would >> example_func be? I would have two example_func, one for young and one for >> old. >> >> Can I carry out analysis this way? Any help would be appreciated, >> >> Thanks, >> Sushravya >> >> p.s: >> >> "Dear Yvonne, >> I saw your first message and meant to reply, but Tim & Joe have done most >> of the work. Just to clarify/amplify: >> >> The product of p-values approach only works (as Tim said) to test the >> 'global' null hypothesis (no effects real), not the conjunction null (as >> many as all but one effect real). >> >> Testing the conjunction null works by creating a min z image, and making >> inference on it just as if it were a regular z image. This applies to >> uncorrected voxel-wise and corrected voxel- and cluster-wise inferences. >> >> As a stab at this, take a look at the attached easythresh_conj. If you >> supply it two z images, it will do conjunction inference on them and give >> you the standard easythresh output. >> >> easythresh_conj stats/zstat1 stats/zstat2 mask 2.3 0.05 example_func grot" >> >> If you look at the code, all it does is take the min of the two z images >> and then do all the other easythresh stuff on the min. >> >> One detail is that easythresh has to estimate the smoothness from the >> statistic image itself, which results in varying P-values for the same >> cluster sizes depending on the z images used. If you have a single feat >> directory from which the images come from, try using the -s option to >> specify the smoothness file (stats/smoothness), which will give more >> accurate (and contrast-independent) P-values. E.g. >> >> easythresh_conj -s stats/smoothness stats/zstat1 stats/zstat2 mask 2.3 >> 0.05 example_func grot" >> >> Lastly, note that this is a conservative procedure... i.e. proper >> conjunction inference has to account for the worst case scenario, where one >> statistic image is wildly significant, and the other one null. Because of >> this, you may find it hard to attain significance with this method. Just a >> warning. >> >> Let me know if you have any troubles with it. >> >> -Tom" >> >>