Thanks for your replyJohn, i really appreciate it.
Just to be clear. These aren't new subjects that I? Resting differently and adding in, these are the same subjects but just a new analysis. So i'm normalising the first level contrast images in exactly the same way that I normalised the functional (and structural) images for these same subjects in a previous analysis. I only see this inflation of the volume when normalising the contrast images though. Also, the normalised cons don't look 'flowery', there is just a continuation (beyond the brain in some/all panes) of uniformly grey voxels (basically to the edge of the FOV) which is inflating the mask at the second level.
I can go back and run the first level on normalised images as you suggest. I guess my curiosity just got the better of me and I wondered why normalising in exactly the same way did not cause these effects for functional and structural images, only the contrast images.
Cheers,
B.
_______________________________________
From: John Ashburner [[log in to unmask]]
Sent: 05 June 2013 22:36
To: Lawson, Rebecca
Cc: [log in to unmask]
Subject: Re: [SPM] Dartel normalise contrast mask
In general, it is safest to use the same (or similar) preprocessing
for all your subjects. Whether you do the GLM after or before
spatially normalising should not really make a large difference.
There will be a small discrepancy due to the REML estimation of the
temporal correlations, but the thing causing the main difference will
be the behaviour of the masking.
The easiest thing would be to use the "Run Dartel (existing template)"
option (using the previously computed Template data from the other
subjects) on the additional subjects, followed by the Normalise to MNI
space option, and then the first level analysis to get the contrast
images.
Generally, the flowery/swirly artifacts from normalising to MNI space
will occur for regions of the head where no data was available in the
original scans.
Best regards,
-John
On 5 June 2013 19:00, Lawson, Rebecca <[log in to unmask]> wrote:
> Hello,
>
>
>
> I’ve used Dartel to normalise some functional images and then taken these to
> first level analysis, then smoothed the contrast images and taken them to
> second level analysis. So long as I set the following during smoothing :
> matlabbatch{1}.spm.spatial.smooth.im = 1;%set mask to 1 to avoid crazy mask
> my second level mask looks normal.
>
>
>
> I now have a new first level analysis conducted in native space. I am
> attempting to normalise and then smooth the 1st level contrast images using
> Dartel. I have tried both the ‘normalise2mni’ routine and also the ‘combine
> deformations’ route (followed by a separate smoothing step) to achieve the
> normalisation of these contrast images. In both cases the normalised
> contrasts (and hence the mask created at the second level) are massive! The
> voxels around the brain (that are NaN in the functional images) are all
> given numbers very close to zero, and hence appear grey right to the edge of
> the FOV. Using the ‘normalise2mni’ routine this inflation occurs in x, y and
> z planes. Using the ‘combine deformations’ route this inflation only occurs
> in the x plane (left to right).
>
>
>
> I have already searched the spm archive and this “greying” of the contrast
> image beyond the brain looks different to the “flowery” effects that other
> people have reported after normalising contrast images using Dartel.
>
>
>
> Any ideas folks?
>
>
>
> B.
>
>
>
> -------------------------------------------------------
>
> Dr Rebecca Lawson
>
> UCL Institute of Cognitive Neuroscience
>
> 17 Queen Square
>
> London
>
> WC1N 3AR
>
>
