> So after looking into the m-file spm_dartel_norm_fun.m it is shown that the default tpm file is /usr/local/spm8/toolbox/Seg/TPM.nii which is a grey matter segmented template that is trying to use for WHITE MATTER normalizing!!
The TPM.nii file is actually 4D. You can display the other volumes in
the file (not just the first) by changing the "1" in the file selector
to "1:6".
> So, is there any other white matter template that I can redirect when doing these analysis?
/usr/local/spm8/toolbox/Seg/TPM.nii,2
Best regards,
-John
>
> Rodrigo Perea <[log in to unmask]> wrote:
>>
>> So this is the first time I am running a longitudinal dartel preprocessing in white matter with the following steps shown below (from Longitudinal VBM using DARTEL) :
>>
>> However, for some reason step 6 tilts all my images so before smoothing everything is normal but the smoothed images are tilted to the right. The templates in step 5 are aligned as well as the previous segmented images from the early stage. I don't know what is going on. I am attaching a picture of a single subject and its images (first two images are the structural registered MRIs, the third image is the segmented image, the fourth is the flow field from the fifth image (6th iterated Template0. The sixth image is the tilted smooth modulated... :/
>>
>> Any ideas?
>>
>> Here are the steps I used:
>>
>>
>> 1) Coregister the early and late scans of each subject together. Thiswill provide the initial rigid body alignment that HDW will use.2) Run the HDW within subject. This could involve registering theearly to the late scan, or the late to the early scan. Because thisregistration is not exactly inverse consistent, this choice is likelyto change the findings slightly. Lets say that the late scan isregistered to the early one (ie early image stays fixed and late imageis warped to match it). This will generate a map of Jacobiandeterminants (j*.img) that encode relative volume changes. Largerdeterminants (greater than one) will encode regions of growth. Valuesless than one encode regions of shrinkage.3) Segment the early scan to generate grey and white matter, as wellas "imported" grey and white matter, which will be used by dartel.4) For each subject, create a map of GM volumetric difference. Thiscan be done using ImCalc and involves subtracting the grey matter fromthe early scan from the amount of grey matter that we would expectfrom the late scan. The early time point GM is simply what is in thec1 image. Assuming accurate segmentation and longitudinalregistration, the grey matter in the late time point can be computedby multiplying the Jacobian determinants by the c1 image. Puttingthis all together, you would select the j image and the c1 image, andevaluate 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 couldalso normalise these differences by dividing by the time between thescans. 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 dartelall the early data together (ie run dartel to align all c1 scans tothe group average GM, while simultaneously aligning the c2 to thegroup average WM).6) Use the normalise to MNI space option of dartel to generatesmoothed Jacobian scaled spatially normalised versions of the imagesgenerated in (4).7) Do the stats.
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