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
>>
>>
>>
>
