Dear Yael,
the Gaussian random field theory cannot have an influence on such a
potential bias for the edges. You can see that by observing that the
statistical maps (statistical values -> uncorrected p-values) already
show your observed 'edge' pattern.
In your images, the most significant effects are located at the sensors.
This means that any analysis (e.g. conventional ANOVA of channel data)
would find exactly the same results as you do, because SPM takes care to
leave the channel data unchanged (by locating data from a single channel
in a single voxel without mixing it with data from other channels). In
other words, to exclude any potential artefacts due to interpolation
between channels, you could choose to only report statistical
values/p-values within voxels, that contain channel data (indicated by
the green crosses). These maxima are corrected for multiple comparisons
(which is a difference to more conventional analyses in the ERP community).
If there is an edge bias, it might have to do with the way
intersubject-variability expresses itself in EEG.
A powerful way to analyse EEG/MEG data would be do first source
reconstruct your images. This allows you to make inferences about
sources in the brain.
Hope this helps,
Stefan
> Dear Stefan,
>
> I postoed this message to the SPM-list but no one has answered..I hoped you can
> help..
>
> We have been evaluating some EEG results using SPM. In many cases we
> noticed the significant effects SPM found was located on the edges(the
> boundaries of the head map) of our F-map. We added two images as an example.
> Is there something in the Random Field correction that might bias the
> results to prefer the edges of the map?
>
> Thanks,
> Yael.
>
>
>
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--
Dr. Stefan Kiebel
Wellcome Dept of Imaging Neuroscience
Institute of Neurology, UCL
12 Queen Square
London WC1N 3BG
Phone: (+44) 20 7833 7478
Fax: (+44) 20 7813 1420
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