[Mne_analysis] Comparing conditions

[Yury Petrov]

I didn't understand Padraig's arguments. Minimum norm is a linear  
method. This means that the same as for the forward matrix the inverse  
matrix is calculated irrespective of the actual signals measured,  
except for the noise covariance term. So if you add the noise  
covariance matrices for the two conditions and feed the resulting  
"difference" covariance matrix to the inverse routine, it will only  
change the signal to noise ratios in the trivial way. But the matrix  
will remain essentially the same, and the topography of the solution  
should not be "distorted".

On Sep 29, 2008, at Sep 29, 2008 | 11:19 AM, Padraig Kitterick wrote:

> Because you are distorting the dipolar topographies when you do a  
> subtraction at the sensor level. The resulting data is likely  
> contain field patterns which do not relate to the leadfields of the  
> actual sources that gave rise to the data. Thus, any source  
> reconstruction which relies on lead field models, i.e. minimum norm,  
> will give spurious results.
>
> -Padraig
>
> Yury Petrov wrote:
>> Why not first subtract one average response from the other and  
>> then  localize?
>>
>> On Sep 29, 2008, at Sep 29, 2008 | 6:28 AM, Alex Clarke wrote:
>>
>>
>>> Hi there,
>>>
>>> I have a question regarding how best to statistically compare two   
>>> conditions. So far I have only being comparing between 2  
>>> conditions  using ROIs and comparing current estimates over time.  
>>> However, I'd  also like to see the difference between two  
>>> conditions across the  whole brain. I was wondering what the best  
>>> approach to this was  (Ideally ending up with a dSPM map of  
>>> condition1 - conditon2).
>>>
>>> Any help on this would be appreciated
>>>
>>>
>>> Thanks,
>>>
>>> Alex Clarke
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>>>
>>
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>>
>
>
> -- 
> Pádraig Kitterick
> Graduate Student
> Department of Psychology
> University of York
> Heslington
> York YO10 5DD
> UK
>
> Tel: +44 (0) 1904 43 2883
> Email: p.kitterick at psych.york.ac.uk
>
>