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Dear Chen, > thanks replay. Please correct me if I am wrong. Jones's paper discussed > about the non-normally distribution residual after applied different > smoothing filter on FA multiple subjects' analysis under GLM method. If the > residual has non-normally distribution, does it means this statistic method > did not fit previously hypothesis ? so the FA analysis should not use GLM > method to perform statistic inference but rather use non-parametric method ? > Or it still could use GLM method to analysis FA image ? As I said, I haven't actually read Dereks paper (which I probably should), but I'll still try to reply. As you increase the filter width the intensity in any given voxel becomes a mixture of intensities from a larger and larger number of voxels. Hence, by virtue of the central limit yada yada the values will become increasingly normal distributed. Hence, by using a wider filter you may pre-condition your data to better adhere to the assumption of normal distributed errors. The other effect of filter width comes from the matched filter theorem, causing you to become less sensitive to focal activations with small spatial extent and more to widespread activations with large spatial extent. As for the use of GLM. Even when your errors are not normal distributed you can still use the GLM to derive meaningful parameter estimates such as "mean FA in group 1", even though these may no longer be ML estimates. Also, it may still be meaningful to form a t-statistic and interpret it in terms of "reliability of difference between group 1 and group 2". The problem comes if you then try to calculate a p-value for that t-statistic y assuming that it is t-distributed, because it is likely not to be. I think it is quite useful for you to conceptually divide your analysis into "estimation" and "inference" (for lack of better terminology), where the first step refers to the estimation of parameters and a test-statistic (e.g. t) and the second to the translation test-statistic->p-vale. It is (mainly) at the second step that you will have problems when your data are not normal distributed. For this reason (and reasons of efficiency when you have low df) I would suggest using randomisation tests when you have reason to believe that you have non-normal data. That way you are not forced to impose a lot of smoothness (that you may or may not want to have in your data) and you know that your test is always valid. The only cost it comes with is a little extra processing time. Most often this means still using the GLM machinery for estimation of parameters and test-statistic. It is only the final step (the t->p translation) that you replace. Good luck Jesper