In working on generating the ctf files for our data, it occurred to  
me that the process of projecting the 3D coordinates onto the 2D  
plane can result in the electrodes not being evenly distributed even  
if they were originally evenly spaced on the head.  It seems to me  
that this could affect the Gaussian statistics in that the error  
variance will not be spatially homogenous (the smoothness will vary  
locally over the 2D plane).  If I understand how this works  
correctly, this means that the smoothness of the periphery (where the  
electrodes tend to be more widely spaced on the 2D plane) would  
generally be underestimated, resulting in a bias towards  
significance.  Could this also play a part in this observation?   
Relying on voxelwise statistics, as Stefan suggests, would address  
this issue of course.  I'm not sure how the multiple comparison  
corrections would work though.  Since they rely on estimating the  
resels, doesn't that mean they would be affected too?  If my  
reasoning is correct, would the 3D analyses be affected in some  
manner too?  They wouldn't have the problem with the projection to a  
2D plane but would proximity to sensors affect the local smoothness?

Cheers!

Joe


On Jun 22, 2006, at 5:27 AM, Stefan Kiebel wrote:

> 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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Joseph Dien
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