well, I am not an expert in vbm analysis but John
is right when he says that c1 images show probabilities not densities.
However, if you estimate the partial volume coeficients of each voxel
then this coefficients can be more related to the "density" of the tissue.
Anyway I am aware that pvc´s in T1 images not only depend on the proton density
of the voxels but also on the T1 relaxation constant.
Jose
Mensaje citado por John Ashburner <[log in to unmask]>:
> There isn't really any density. The c1 images show the probability of
> GM, which typically is values either close to zero or close to one.
> Only with Jacobian scaling (modulation) would there be any useful
> measure.
>
> The original idea of "density" was never anything to do with cell
> packing or anything like that. It was simply about the proportion of
> GM in the smoothed data.
>
> Best regards,
> -John
>
> On 28 October 2010 20:58, Ze Wang <[log in to unmask]> wrote:
> > Thanks. But even in that perfect case, the voxel-wise comparisons between
> > the subjects' tissue density maps (the c* maps) should still show the
> > density difference. Am I right here?
> > Ze
> > On 10/28/2010 3:18 PM, John Ashburner wrote:
> >>
> >> If all images were to be (hypothetically) exactly aligned, then the
> >> grey matter in one subject's brain would exactly align with the grey
> >> matter in another subjects spatially normalised data. This would make
> >> the preprocessed grey matter images identical (although exact
> >> registration would never be possible).
> >>
> >> Best regards,
> >> -John
> >>
> >> On 28 October 2010 16:29, Ze Wang<[log in to unmask]> wrote:
> >>>
> >>> Hi John,
> >>> Sorry to keep you busy, but I'm still struggling with this issue.
> >>> In the VBM/wiki (http://en.wikibooks.org/wiki/SPM/VBM), I found
> >>> several
> >>> words about modulation: "
> >>> If you take the limiting case of extremely precise (not necessarily
> >>> extremely accurate) registration and segmentation, the pre-processed
> >>> concentration images are likely to be all identical. An analysis of these
> >>> data would not show you anything."
> >>>
> >>> which is basically the same as what you suggested in this thread of
> >>> email.
> >>> But I can't understand why the pre-processed concentration images are
> >>> likely to be all identical if an extremely precise registration and
> >>> segmentation was achieved. Should we still get a subject specific
> >>> probability map (c* map) for each tissue type, whose value should not be
> >>> changed by registration?
> >>>
> >>> Thanks
> >>> Ze
> >>> On 10/28/2010 11:00 AM, Ze Wang wrote:
> >>>>
> >>>> Hi John,
> >>>> Except the registration errors, will the unmodulated analysis pick
> >>>> up
> >>>> the voxelwise tissue (or other property) difference? Like sub A, B have
> >>>> a
> >>>> grey matter probability of 0.8 and 0.7 in Hippocampus, respectively.
> >>>> Instead
> >>>> of looking at the volumetric difference, should we use the unmodulated
> >>>> analysis to see this voxel wise difference?
> >>>> Also, as people are also interested in the correlation between
> >>>> tissue
> >>>> intensity (actually the probability after segmentation) and some
> >>>> behavior
> >>>> scores. Should we also use the unmodulated normalized images?
> >>>>
> >>>> I appreciate your comments!
> >>>>
> >>>> Thank you!
> >>>> Ze
> >>>> On 10/28/2010 7:48 AM, John Ashburner wrote:
> >>>>>
> >>>>> Unmodulated analysis shows regions where registration error differs
> >>>>> significantly among the populations, whereas modulated analysis
> >>>>> attempts to show regions of volumetric difference.
> >>>>>
> >>>>> Everyone accepts that using a different linear model to fit data will
> >>>>> give different results. In the same way, different pre-processing is
> >>>>> also expected to produce different findings (as this also changes how
> >>>>> differences among the data are modelled).
> >>>>>
> >>>>> Best regards,
> >>>>> -John
> >>>>>
> >>>>> On 28 October 2010 12:07, Cullen, Alexis<[log in to unmask]>
> >>>>> wrote:
> >>>>>>
> >>>>>> Hi SPM experts
> >>>>>>
> >>>>>> I have been running some analyses on a on a set of structural
> >>>>>> paediatric
> >>>>>> MRI
> >>>>>> images using the VBM 5 toolbox in SPM 5. I have carried out the
> >>>>>> segmentation
> >>>>>> and normalisation steps according to the VBM 5 manual with the
> >>>>>> exception
> >>>>>> that I have segmented against a custom template produced using the
> >>>>>> Template-O-Matic toolbox. A 2-sample t-test has been used to compare
> >>>>>> my
> >>>>>> two
> >>>>>> groups and these analyses have identified sig differences in areas
> >>>>>> that
> >>>>>> would be expected in the literature. However, I am concerned as the
> >>>>>> results
> >>>>>> produced from the modulated (non-linear) and unmodulated images differ
> >>>>>> substantially. I understand that the analysis of modulated and
> >>>>>> unmodulated
> >>>>>> images will answer different questions and that the two techniques
> >>>>>> will
> >>>>>> give
> >>>>>> varying results. However, I would have expected based on the
> >>>>>> literature
> >>>>>> and
> >>>>>> my understanding of the modulation process that analyses of
> >>>>>> unmodulated
> >>>>>> data
> >>>>>> would give differences generally in the same areas as the modulated
> >>>>>> but
> >>>>>> with
> >>>>>> more extensive group differences (e.g., Fortino et al, 2009;
> >>>>>> Schizophrenia
> >>>>>> Research). Or at least that group differences would be in the same
> >>>>>> direction
> >>>>>> using both image types i.e., areas of reduced volume in both analysis
> >>>>>> types.
> >>>>>>
> >>>>>> In contrast I have found group differences in opposite directions
> >>>>>> using
> >>>>>> the
> >>>>>> two images, namely that in modulated analyses my group of interest
> >>>>>> shows
> >>>>>> predominately increased GM and WM relative to the control group, but
> >>>>>> in
> >>>>>> unmodulated analyses the group of interest show predominately
> >>>>>> decreased
> >>>>>> GM
> >>>>>> and WM (albeit in different areas). I have one grey matter finding
> >>>>>> that
> >>>>>> is
> >>>>>> maintained in both analyses (interest group> control group),
> >>>>>> otherwise
> >>>>>> there seems to be no overlap between the results.
> >>>>>>
> >>>>>> I have repeated the analysis several times as the sample has expanded
> >>>>>> and
> >>>>>> the same finding has come up. I also checked my segmentation outputs
> >>>>>> and
> >>>>>> none of them look strange and there are no real inhomogeneity issues.
> >>>>>>
> >>>>>> I am just wondering whether anyone has any thoughts on whether this is
> >>>>>> some
> >>>>>> sort of error or whether the findings are possible? The areas of
> >>>>>> decreased
> >>>>>> GM and WM are more in line with what I would expect to see in an adult
> >>>>>> population but it is possible that in this child/adolescent population
> >>>>>> some
> >>>>>> specific areas might be increased in volume.
> >>>>>> I would be keen to hear if anyone has any thoughts/explanations for
> >>>>>> this.
> >>>>>> Many thanks, Alexis
> >>>>>>
> >>>>>> Alexis Cullen
> >>>>>> Research Worker/PhD Student
> >>>>>> R&R and CHADS projects
> >>>>>> Department of Forensic Mental Health Science (PO23)
> >>>>>> Institute of Psychiatry, De Crespigny Park
> >>>>>> Denmark Hill, SE5 8AF
> >>>>>> [log in to unmask]
> >>>>>> 020 7848 5678
> >>>>>>
> >>>>>>
> >>>>
> >>>
> >>> --
> >>> --------------------------------------------------
> >>> Ze Wang, Ph. D
> >>> Research Assistant Professor of Biomedical Engineering,
> >>> Department of Psychiatry,
> >>> School of Medicine,
> >>> University of Pennsylvania,
> >>> 3900 Chestnut Street,
> >>> Philadelphia, PA 19104, USA
> >>> Tel: 215-222-3200 ext 123
> >>> Fax: 215-386-6770
> >>>
> >>>
> >
> >
> > --
> > --------------------------------------------------
> > Ze Wang, Ph. D
> > Research Assistant Professor of Biomedical Engineering,
> > Department of Psychiatry,
> > School of Medicine,
> > University of Pennsylvania,
> > 3900 Chestnut Street,
> > Philadelphia, PA 19104, USA
> > Tel: 215-222-3200 ext 123
> > Fax: 215-386-6770
> >
> >
>
--
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