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That is excellent, Anderson. Really thanks for your clarification of my puzzles:) Very best, Mark 2012/12/3 Anderson M. Winkler <[log in to unmask]> > In principle yes, but I'm reluctant to say "always" and be forgetting some > scenario... In any case, Jeanette Mumford has an excellent page that > explains mean centering and similar issues here: > http://mumford.fmripower.org/mean_centering/ > > > > > 2012/12/3 Tseng Mark <[log in to unmask]> > >> Thanks Anderson. >> >> So, since I only have one EV (behaviour scores) in the correlation map (I >> am interested in brain regions correlated with the scores), should I always >> add another EV with ones (representing group mean) in order to absorb the >> mean? >> >> Thanks again. >> >> Mark >> >> 2012/12/3 Anderson M. Winkler <[log in to unmask]> >> >> Hi Mark, >>> >>> >>> 2012/12/2 Tseng Mark <[log in to unmask]> >>> >>>> Ah, thanks for your reminder of the bias, Anderson. I then used the >>>> mask in fsl atlas thresholded at 50% probability to do randomise, and the >>>> results were good. Is that ok? >>>> >>> >>> Sounds ok. >>> >>> >>> Still one point confuses me. In the correlation map that I want to run >>>> randomise, there is only one EV, that is, the behaviour scores, which has >>>> been demeaned. (I think that's why the warning message in randomise said >>>> "All design columns have zero mean") Since I have done demean, why do I >>>> have to demean it again while running randomise? >>>> >>> >>> The reason is that with all regressors having zero mean, there won't be >>> any one in the matrix to absorb the mean that is almost certainly present >>> in the image. It's as if fitting a line to the data and forcing it to cross >>> the y-axis at zero, when the best fit probably would be somewhere else. >>> >>> By mean-centering the data, all points are shifted so that the intercept >>> will indeed be at zero and you'll get the best fit for the slope, which is >>> what you care about. If a column with ones is included, it will absorb the >>> mean while the slope will be captured by the other regressor (the one with >>> the behavioural scores), such that both ways are equivalent. >>> >>> >>> Hope this helps! >>> >>> All the best, >>> >>> Anderson >>> >>> >>> 2012/12/2 Anderson M. Winkler <[log in to unmask]> >>> >>>> Hi Mark, >>>> >>>> I find interesting that the input file is called >>>> "filtered_func_data.nii.gz", which immediately suggests it's a time series. >>>> In any case, if this file contains group-level data, and if the columns of >>>> the design matrix have all zero mean, then yes, you can run randomise with >>>> the option -D. You can also add a column with ones to the design. >>>> >>>> Having said this, however, it sounds to me that there may be some >>>> selection bias in your study, i.e., using the peak voxels from one analysis >>>> to further run a second analysis on the same data. Note also that a sphere >>>> surrounding the peak isn't a safe FWER (or even FDR) procedure. >>>> >>>> All the best, >>>> >>>> Anderson >>>> >>>> >>>> >>>> 2012/12/2 Tseng Mark <[log in to unmask]> >>>> >>>>> Hi Anderson, >>>>> >>>>> Thanks for your reply. >>>>> No, not for 1st-level results. >>>>> >>>>> I have a hypothetical area, and this area was activated in a group >>>>> activation map (map 1) of our subjects. I want to prove further that this >>>>> area is correlated with a behaviour score. So, I did another group analysis >>>>> to see if a COPE is correlated with the score (let's called it correlation >>>>> map). I then created a spherical roi centred at the local maximal >>>>> coordinate of map 1 and used it to do small volumn correction by randomise >>>>> command in the correlation map. >>>>> >>>>> Mark >>>>> >>>>> 2012/12/2 Anderson M. Winkler <[log in to unmask]> >>>>> >>>>> Dear Mark, >>>>>> >>>>>> It sounds as if you were trying to run randomise for 1st level >>>>>> results (i.e., FMRI time series for a given subject), is this right? If >>>>>> yes, then randomise isn't the tool you need. Instead, in the Feat GUI, in >>>>>> the Post-stats tab, there are two options that may interest you: >>>>>> >>>>>> - Pre-threshold masking, in which you can supply the mask you have >>>>>> (in the same space as the subject's data, which I believe in your case is >>>>>> not the standard, but the native space). >>>>>> - Contrast masking, in which you can specify other contrasts, >>>>>> computed for the same subject, for masking. >>>>>> >>>>>> In case you'd like to use a mask from a group analysis (then probably >>>>>> in standard space) to a single subject, then you'll need to warp it to the >>>>>> subject's native space before using it for the Pre-threshold masking. >>>>>> >>>>>> Hope this helps! >>>>>> >>>>>> All the best, >>>>>> >>>>>> Anderson >>>>>> >>>>>> >>>>>> >>>>>> 2012/12/2 Mark <[log in to unmask]> >>>>>> >>>>>>> Hi, >>>>>>> >>>>>>> I want to do a small volume correction in a spherical ROI defined >>>>>>> from another group contrast in my study. I created a spherical ROI centred >>>>>>> in a certain coordinate with 5-mm radius (called sphere_roi.nii.gz), enter >>>>>>> the cope directory that I want to analyse, and then run: >>>>>>> >>>>>>> randomise -i filtered_func_data.nii.gz -o output_directory -d >>>>>>> design.mat -t design.con -m sphere_roi.nii.gz -T -c 2.3 >>>>>>> >>>>>>> The permutation then started but, before that, there appeared a >>>>>>> message: >>>>>>> >>>>>>> Warning: All design columns have zero mean - consider using the -D >>>>>>> option to demean your data. >>>>>>> >>>>>>> Anywhere wrong? Should I do anything, such as adding -D? >>>>>>> >>>>>>> Thanks in advance. >>>>>>> >>>>>>> Mark >>>>>>> >>>>>> >>>>>> >>>>> >>>> >>> >>> >> >