Dear VBM experts,
I would like to use VBM (vbm8) for our study but I am confused as it seems that everyone does something more or less different.
We have one structural image for time point 1 and another (2 months later) for time point 2 (both transformed to niftis) for each of our 22 participants and 23 control subjects.
Of special interest for me is the time x group interaction that should be possible to analyze following the vbm8 manual.
I understand that everything should be pretty straightforward when choosing "Process longitudinal data" in vbm8. However, I am not sure if the first 3 steps (2x Realign & bias correction) are more thought for fMRI time series, especially because I found a "recipe" (see below) by John Ashburner in the archives using coregister and more instead. I am just not sure which way to go, and if for example an AC-PC reorientation should be done manually before the coregistration step.
I am also confused that some authors chose to use their own TPMs although their participants were not coming from a special population.
Concerning the detailed options I am also not sure what would be recommended as e.g. again in the coregistration step the help says that I COULD use the Normalised Cross Correlation for the objective function as it is within-modality..but is it the best to do?
Best,
Vincent
On Wed, 2010-10-13 at 16:31 +0100, John Ashburner wrote:
> DARTEL is not so suited to longitudinal registration as it works at a
> lower resolution than HDW and because it is based on aligning tissue
> classes. Both of these mean that it may not be as accurate at
> estimating the very small deformations that occur within subject over
> time.
>
> There are many many ways to do a longitudinal analysis, but here is
> one approach that may be acceptable, which involves identifying
> regions of longitudinal change in GM.
>
> 1) Coregister the early and late scans of each subject together. This
> will provide the initial rigid body alignment that HDW will use.
>
> 2) Run the HDW within subject. This could involve registering the
> early to the late scan, or the late to the early scan. Because this
> registration is not exactly inverse consistent, this choice is likely
> to change the findings slightly. Lets say that the late scan is
> registered to the early one (ie early image stays fixed and late image
> is warped to match it). This will generate a map of Jacobian
> determinants (j*.img) that encode relative volume changes. Larger
> determinants (greater than one) will encode regions of growth. Values
> less than one encode regions of shrinkage.
>
> 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.
>
> Note the asymmetric treatment of the early and late scans. To be a
> bit more rigorous, you could repeat the analysis with early and late
> scans swapped around to see if you get the same results. See the
> following paper for more info....
>
> Bias in estimation of hippocampal atrophy using deformation-based
> morphometry arises from asymmetric global normalization: An
> illustration in ADNI 3 T MRI data
> NeuroImage, Volume 50, Issue 2, 1 April 2010, Pages 434-445
> Paul A. Yushkevich, Brian B. Avants, Sandhitsu R. Das, John Pluta,
> Murat Altinay, Caryne Craige and the Alzheimer's Disease Neuroimaging
> Initiative
>
> Another approach would be to pre-process the data both ways and create
> weighted averages of the smoothed spatially normalised Jacobian scaled
> difference images (depending how the Imcalc bit is done the second
> time, you may need to subtract one from the other in the averaging
> procedure). Then do the stats on these averages. This should (I
> hope) solve the problem reported by Yushkevich et al, and make better
> use of the data.
>
>
> Best regards,
> -John
|