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FSL  May 2010

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Subject:

Re: randomise and voxel-dependent EVs

From:

DRC SPM <[log in to unmask]>

Reply-To:

FSL - FMRIB's Software Library <[log in to unmask]>

Date:

Thu, 6 May 2010 14:15:52 +0100

Content-Type:

text/plain

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text/plain (230 lines)

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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