On 8 March 2013 14:05, Jeff Browndyke <
[log in to unmask]> wrote:
> This is great, John. How would you handle this sort of longitudinal scenario with 3 time points?
>
> Regards,
> Jeff Browndyke
>
> Sent from my iPad
>
> On Mar 8, 2013, at 6:19 AM, John Ashburner <
[log in to unmask]> wrote:
>
>> For looking at GM atrophy from longitudinal pairs of scans, you could
>> do the following....
>>
>> For each subject
>> Run the SPM12b pairwise longitudinal registration to generate the
>> subject average and Jacobian difference (jd)
>> Segment the subject average, generating c1, rc1, rc2
>> Use ImCalc to compute c1.*jd (possibly dividing the result by the
>> time difference to give the rate of atrophy)
>>
>> Run Dartel, aligning the rc1 and rc2 images from all subjects together
>>
>> Normalise and smooth the c1.*jd images
>>
>> Run stats
>>
>> Note that computing TIV in SPM8 is not as straightforward as it was,
>> because the fluid class (c3) does not just consist of CSF. There is
>> also eyeball fluid, and a bit of other stuff too.
>>
>> Best regards,
>> -John
>>
>> On 7 March 2013 21:21, Richard Binney <
[log in to unmask]> wrote:
>>> Dear John,
>>>
>>> I have a loose idea but would you please help me by adapting your pipeline
>>> posted further back in this threadsuch that it applies when the longitudinal
>>> registration toolbox in SPM12b proceeded step 3)? (see below)
>>>
>>> Many thanks
>>>
>>> 3) Segment the early scan to generate grey and white matter, as well
>>> as "imported" grey and white matter, which will be used by dartel.
>>>
>>> 4) For each subject, create a map of GM volumetric difference. This
>>> can be done using ImCalc and involves subtracting the grey matter from
>>> the early scan from the amount of grey matter that we would expect
>>> from the late scan. The early time point GM is simply what is in the
>>> c1 image. Assuming accurate segmentation and longitudinal
>>> registration, the grey matter in the late time point can be computed
>>> by multiplying the Jacobian determinants by the c1 image. Putting
>>> this all together, you would select the j image and the c1 image, and
>>> evaluate
>>> i2.*(i1-1)
>>>
>>> Alternatively, you may wish to just use the volumetric difference,
>>> which would be by selecting the Jacobain image and evaluating
>>> (i1-1)
>>>
>>> If the time difference between the scans is variable, then you could
>>> also normalise these differences by dividing by the time between the
>>> scans. This may simplify the design matrix when you fit the GLM,
>>> although it does represent a slightly different model.
>>>
>>> 5) After all the within subject preprocessing is done, you can dartel
>>> all the early data together (ie run dartel to align all c1 scans to
>>> the group average GM, while simultaneously aligning the c2 to the
>>> group average WM).
>>>
>>> 6) Use the normalise to MNI space option of dartel to generate
>>> smoothed Jacobian scaled spatially normalised versions of the images
>>> generated in (4).
>>>
>>> 7) Do the stats.
>>>
>>>
>>>
>>> On Wed, Mar 6, 2013 at 5:59 AM, Maria Serra <
[log in to unmask]> wrote:
>>>>
>>>> Thank you for your answer.
>>>>
>>>>
>>>> I underestand that you suggested me to use the Jacobian determinants
>>>> (j*.img) instead of the flow fields. However, that procedure gives an error:
>>>>
>>>> - - - - -
>>>> Running 'Normalise to MNI Space'
>>>>
>>>> ** "jy_r1stEpisode_AOC_097re" **
>>>> Failed 'Normalise to MNI Space'
>>>> Error using ==> dartel3
>>>> Wrong number of dimensions.
>>>> In file "/usr/local/spm8/spm_dartel_integrate.m" (v2107), function
>>>> "spm_dartel_integrate" at line 69.
>>>> In file "/usr/local/spm8/toolbox/DARTEL/spm_dartel_norm_fun.m" (v4194),
>>>> function "deal_with_subject" at line 157.
>>>> In file "/usr/local/spm8/toolbox/DARTEL/spm_dartel_norm_fun.m" (v4194),
>>>> function "spm_dartel_norm_fun" at line 132.
>>>>
>>>> The following modules did not run:
>>>> Failed: Normalise to MNI Space"
>>>>
>>>> - - - - -
>>>>
>>>> Regarding the time dependent asymetries, when I multiplied Jacobian
>>>> determinants by the c1 image, I divided it by the time between scans as
>>>> follows: i2.*(i1-1)/x. Is it a good way to solve the problem of the time
>>>> differences between scans?
>>>>
>>>> thank you,
>>>>
>>>> Maria
>>>