Dear all,

how to apply modulation in VBM is still a matter of debate (at least for me)
and most of the VBM papers reflect this problem. Some papers report results
using unmodulated images (=density or concentration), some show findings for
modulated images (=volume), and there are also reports for both. Personally,
I have prefered to use unmodulated images with SPM2. 
However, normalization in SPM5 is of much higher accuracy because a
canonical registration of the GM/WM/CSF segmentations is iteratively used
and I have noticed that more of the anatomical information is now coded in
the deformations and modulation seems to be much more appropriate.
Nevertheless, it is not clear what is the best way to apply modulation and 
to remove confounds due to different brain size.

If we follow the commonly used terms “volume” for modulated data and
“density” (or concentration) for unmodulated data and concentrate on GM
there are many possible ways to correct or not correct for different brain size:

No modulation:

Correction      Interpretation
----------      --------------
nothing         relative density
globals 	“localised” relative density after correcting for total GM
                or TIV (multiplicative effects)
AnCova          “localised” relative density that can not be explained by 
                total GM or TIV (additive effects)


Modulation:

Correction      Interpretation
----------      --------------
nothing         absolute volume
globals         relative volume after correcting for total GM or TIV
                (multiplicative effects)
AnCova          relative volume that can not be explained by total GM or 
                TIV (additive effects)

Total intracranial volume (TIV) can be approximated by calculating the sum
of GM and WM, although sometimes additionally CSF is used or manually
derived TIV values are used.

These many options are confusing and it is difficult to find the best way
which fits to your hypothesis.

Thus, I have tried to find an easier solution which might be appropriate for
most hypotheses. Modulated images can be optionally saved by correcting for
non-linear warping only. Volume changes due to affine normalisation will be
not considered and this equals the use of default modulation and globally
scaling data according to the inverse scaling factor due to affine
normalisation. I recommend this option if your hypothesis is about effects
of relative volumes which are corrected for different brain sizes. This is a
widely used hypothesis and should fit to most data. The idea behind this
option is that scaling of affine normalisation is indeed a multiplicative
(gain) effect and we rather apply this correction to our data and not to our
statistical model. These modulated images are indicated by "m0" instead of
"m" using the VBM5.1 toolbox.

Best regards,

Christian

____________________________________________________________________________

Christian Gaser, Ph.D.
Assistant Professor of Computational Neuroscience
Department of Psychiatry
Friedrich-Schiller-University of Jena
Philosophenweg 3, D-07743 Jena, Germany
Tel: ++49-3641-935805	Fax:   ++49-3641-935280
e-mail: [log in to unmask]
http://dbm.neuro.uni-jena.de