Oops, should clarify, variance smoothing does work with both the vbm
and fMRI, it's only the fMRI adjusted for VBM with --vxl and --vxf
options that seems to work without -v but not with (using the same -v
3 for everything, not that that should matter)
Thanks,
Ged
On 6 May 2010 14:15, DRC SPM <[log in to unmask]> wrote:
> Hi Matthew,
>
> I've just found out that it works if I don't use variance smoothing...
> any ideas? Let me know if you still want the data (i.e. if you can get
> "-v 3 --vxl=3 --vxf=confounds.nii" to work on any other data).
>
> Many thanks,
> Ged
>
>
> On 6 May 2010 13:53, Matthew Webster <[log in to unmask]> wrote:
>> 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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