to isolate to problem I need more information:
- Is this problem also occuring for unmodulated and modulated images
(including the affine component)?
- Please can you send me the raw T1-image and the *seg.mat file to my mail
Christian Gaser, Ph.D.
Assistant Professor of Computational Neuroscience
Department of Psychiatry
Friedrich-Schiller-University of Jena
Jahnstrasse 3, D-07743 Jena, Germany
Tel: ++49-3641-934752 Fax: ++49-3641-934755
e-mail: [log in to unmask]
On Wed, 6 May 2009 14:31:55 -0700, Neil Chatterjee <[log in to unmask]> wrote:
>Thank you for your quick reply!
>I am using SPM5 on a Windows XP x64 installation of Matlab R2008b.
>I kept the options at defaults except for 'Set origin'. I've attached a
>copy of a template script used to generate these images.
>They are m0 instead of m because the images are modulated only by the
>non-linear compression map. To quote the tooltip: "Modulated images
>can be optionally saved by correcting for non-linear warping only
>... 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...These
>modulated images are indicated by 'm0' instead of 'm'."
>John Ashburner wrote:
>> My best guess is that the modulated images are stored as some form of
>> datatype, with a scalefactor. To help me narrow down the cause of the
>> problem, I need a few more details before I try to figure out the cause.
>> Which version of SPM are you using?
>> Which options did you use to generate the modulated images?
>> Why are they called m0wc1*.nii instead of mwc1*.nii?
>> Best regards,
>> On Tuesday 05 May 2009 21:05, Neil Chatterjee wrote:
>>> Dear SPMers,
>>> I came across several oddities in my m0wc1*.nii (modulated, normalized,
>>> gray matter) images yesterday, and I am hoping someone here can shed some
>>> light on the situation. Apologies for the length of this correspondence,
>>> but I wanted to be precise in explaining the problem observed.
>>> Anyways, looking at a typical m0wc1*.nii image, the voxel values have the
>>> following strange properties:
>>> 1) There are no voxels with a value greater than 1
>>> 2) There are ~580,000 voxels with a value of exactly 1. Actually, they all
>>> have a value of exactly 1.000000059138983, which in itself is kind of
>>> strange. 3) Of the non-zero valued voxels in the m0wc1*.nii image, 42.4% of
>>> them are exactly (to double precision) the same as in the wc1*.nii images.
>>> In a previous thread, Dr. Ashburner said that
>>>> The contents of a modulated image are a voxel compression map multiplied
>>> by tissue belonging
>>>> probabilities (which range between zero and one)
>>>> The total volume of grey matter in the original image can be
>>>> determined by summing the voxels in the modulated, spatially
>>>> normalised image and multiplying by the voxel volume (product of voxel
>>> That the total volume of gray matter in the original image can be
>>> determined by integration implies conservation of probability of gray
>>> matter. It follows that the voxel compression map would have values >1 in
>>> areas where there has been positive compression (shrinking) and values <1
>>> in areas where there has been negative compression (expansion). With this
>>> in mind, the properties described above lead me to the following
>>> A) There are no voxels with high probability (p~1.0) of being gray matter
>>> that were positively compressed (shrunk) in normalizing, else there would
>>> exist modulated voxels with value > 1.
>>> B) There exist several voxels that either i) had a gray matter probability
>>> of exactly 1 and were not compressed even one iota or ii) were compressed
>>> in exact (to double precision!) proportion to their uncertainty of being
>>> gray matter. Else there would not exist modulated voxels with value = 1
>>> exactly C) 42.4% of probable gray matter voxels neither shrunk nor expanded
>>> in the process of morphing to standard space.
>>> I just can't wrap my head around any of those conclusions. I feel like
>>> either I'm totally misunderstanding what happens with modulation or
>>> something is very very wrong with my images. I understand that the
>>> non-linear only modulation (m0 instead of m) changes things, but
>>> substituting "non-linear compression" for "compression" above does not make
>>> the observations any less strange. If any guru out there can make sense of
>>> all this, it would be much appreciated.
>>> Neil Chatterjee
>>> Research Assistant
>>> Stanford Systems Neuroscience and Pain Lab
>>> [log in to unmask]