Hi Ged,
The versions of randomise from 4.1.3 onwards should work correctly with confound EV's. If you're
getting a persistent error message, I would be happy to look over the data if you upload it to our site.
Many Regards
Matthew
> 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
>>>>> ---------------------------------------------------------------------
>>>>> -
>>>>> --
>>>>> ---
>>>>>
>>>>
>>>
>>
>
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