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Hi Matthew, Has this internal version with better handling of voxelwise nuisance covariates made it out yet? It's not obvious to me from the comments here: http://www.fmrib.ox.ac.uk/fsl/fsl/whatsnew.html#revisions If it's not yet released, please could you give me a link to a binary? I'm currently sat with: Linux 2.6.21.5-smp i686 Intel(R) Xeon(TM) but have access to a couple of other Linux machines (32 or 64 bit), and might be able to borrow a Mac if easier. With version 2.1 of randomise, I seem to be getting errors about incompatible dimensions (see below), even though my fMRI and VBM data have the same x, y, z, and t dimensions, and my design.mat has the same t dimension. Is it possible this is a bug that is fixed in the newer versions, or do you think I have miscounted somewhere? As a sanity check, I have tried using just the fMRI data, or just the VBM data (with the same design, contrast, and mask) and both work without any errors, which would seem to imply that the dimensions should be consistent for fMRI adjusted for VBM, right? (the only change to the apparently successful fMRI analysis is to add --vxl=3 and --vxf=vbm.nii, does that sound right?). I briefly wondered if EVs were counted from zero (like voxels and times in fslview, etc.) but using --vxl=2 for my third EV also results in the same error. Many thanks, Ged P.S. The not entirely helpful error message: ERROR: Program failed An exception has been thrown Logic error:- detected by Newmat: incompatible dimensions Trace: SubMatrix(=). On 9 April 2009 10:32, Matthew Webster <[log in to unmask]> wrote: > Hi, > It looks like you are using the voxelwise EV as a confound, rather than > an EV of interest, this is a situation which is handled a lot better in our > internal build of randomise - if you let me know what architecture you are > using ( Mac, Linux 32/64 etc ) I can send you a link to newer binary. > > Many Regards > > Matthew > >> I tested with few images to understand. I used a simple regression >> model: >> >> Constant Variable >> 1.000000e+00 8.000000e+00 >> 1.000000e+00 1.000000e+01 >> 1.000000e+00 1.200000e+01 >> 1.000000e+00 1.800000e+01 >> 1.000000e+00 2.000000e+01 >> 1.000000e+00 2.200000e+01 >> >> I used Glm to generate my .mat and .con files, so the voxel-dependent >> EV column was filled with mean values across all voxels for each image. >> >> Constant Variable voxel-dependent >> 1.000000e+00 8.000000e+00 0.000000e+00 >> 1.000000e+00 1.000000e+01 0.000000e+00 >> 1.000000e+00 1.200000e+01 0.000000e+00 >> 1.000000e+00 1.800000e+01 1.704375e-04 >> 1.000000e+00 2.000000e+01 1.704375e-04 >> 1.000000e+00 2.200000e+01 1.704375e-04 >> >> With this type of analysis, I noticed that my statistical map (contrast >> 0 -1 0) was different from the analysis without voxel-dependent EV even >> in voxels where there was no variation in the other imaging modality. >> After that, I tested putting only 1 in the voxel-dependent EV column: >> >> Constant Variable voxel-dependent >> 1.000000e+00 8.000000e+00 1 >> 1.000000e+00 1.000000e+01 1 >> 1.000000e+00 1.200000e+01 1 >> 1.000000e+00 1.800000e+01 1 >> 1.000000e+00 2.000000e+01 1 >> 1.000000e+00 2.200000e+01 1 >> >> This time, I got the same statistics for both analysis in the voxel >> where there was no variation in the other imaging modality but different >> statistics in voxel where there was variation. In this case, the results >> makes more sense. Basically, you want to correct voxels where the >> variation in the other imaging modality explains the results in you >> imaging modality of interest. I'm not sure if I'm correct . Maybe, I'm >> missing something as I'm combining permutations and voxel-dependent >> variables. Could you help me to clarify this thing? >> >> Herve >> >> >> >> -----Original Message----- >> From: Matthew Webster [mailto:[log in to unmask]] >> Sent: Tuesday, April 07, 2009 12:26 PM >> To: [log in to unmask] >> Subject: Re: [FSL] randomise and voxel-dependent EVs >> >> Hi, >> The numbers in the column that you replace with the voxelwise-EV >> will be used to determine valid permutations - does this explain the >> results you're seeing? >> >> Many Regards >> >> Matthew >> >>> It seems to work but I noticed that the numbers in the column I want >>> to replace with voxel-dependent EV still matter. Is each images >>> weighted by the corresponding number in the column? Should I put a >>> column fill with only with 1? >>> >>> Herve >>> >>> -----Original Message----- >>> From: Matthew Webster [mailto:[log in to unmask]] >>> Sent: Thursday, March 26, 2009 6:32 AM >>> To: [log in to unmask] >>> Subject: Re: [FSL] randomise and voxel-dependent EVs >>> >>> Hi, >>> You need to supply two additional inputs to randomise: A 4D volume >> >>> file, where each voxel timeseries corresponds to equivalent >>> voxel-dependent EV and a number telling randomise which column in your >> >>> original input design the voxelwise EV replaces: >>> >>> e.g --vxl=2 --vxf=my_input_EV >>> >>> tells randomise for each voxel to replace the 2nd EV in your input >>> design with the appropriate "timeseries" from my_input_EV. >>> >>> Many Regards >>> >>> Matthew >>> >>>> Thank for your answer but I'm not sure how to do it. When you use >>>> randomise, you feed it with your 4D image, the design.mat file and >>>> the >>> >>>> design.con file but none of them contain information about the >>>> voxel-dependent EV except one column including mean across all >>>> voxels. >>>> How can I specify my voxel-dependent EV in the randomise model? >>>> >>>> Thanks, >>>> >>>> Herve >>>> >>>> >>>> -----Original Message----- >>>> From: Steve Smith [mailto:[log in to unmask]] >>>> Sent: Wednesday, March 25, 2009 9:00 AM >>>> To: [log in to unmask] >>>> Subject: Re: [FSL] randomise and voxel-dependent EVs >>>> >>>> Hi, >>>> >>>> See the usage - you should just be able to specify this additional >>>> component to your model. >>>> >>>> Cheers. >>>> >>>> >>>> >>>> On 24 Mar 2009, at 14:42, Herve Lemaitre wrote: >>>> >>>>> Hi FSL experts, >>>>> >>>>> In a TBSS analysis, I would like to use randomise with a statistical >> >>>>> design including one voxel-dependent EV. Is it implemented in >>>>> randomise and do I have to change the way to run randomise to take >>>>> into account this EV? >>>>> >>>>> Thanks, >>>>> >>>>> Herve Lemaitre >>>>> >>>>> >>>>> >>>> >>>> >>>> --------------------------------------------------------------------- >>>> - >>>> -- >>>> --- >>>> Stephen M. Smith, Professor of Biomedical Engineering Associate >>>> Director, Oxford University FMRIB Centre >>>> >>>> FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK >>>> +44 (0) 1865 222726 (fax 222717) >>>> [log in to unmask] http://www.fmrib.ox.ac.uk/~steve >>>> --------------------------------------------------------------------- >>>> - >>>> -- >>>> --- >>>> >>> >> >