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
>>
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