[Mne_analysis] group dSPM?

[Hari Bharadwaj]

Hi Elena,
    If you have roughly similar #trials across subjects for noise-cov
estimation and averaging, that is very reasonable if you are looking
at an ROI.. However, in order to deal with the multiple comparison
problem across vertices, you'll have to do some kind of
bootstrapping/permutations to determine what the p = 0.05 threshold at
the whole brain level is... If your effects are strong, you could be
conservative and do a vertewise bonferroni correction or FDR..

Regards,
Hari







On Sun, June 10, 2012 10:34 am, Elena Orekhova wrote:
> Dear Alex,  Hari
>
> Do you think it is possible to estimate significance of the group mean
> dSPM values?
> In case of loose constrains the dSPM values come from F distribution with
> dof 3 and Npoints x 3 (Dale, 2000). One can e.g. calculate distribution of
> the means of N (N=number of subjects) randomly generated F values and
> empirically assess what value would correspond to e.g. p=0.05.
>
> Elena
>
> ________________________________________
> From: Alexandre Gramfort [gramfort at nmr.mgh.harvard.edu]
> Sent: Friday, June 08, 2012 10:26 PM
> To: Hari Bharadwaj
> Cc: Elena Orekhova; mne_analysis at nmr.mgh.harvard.edu; Tatiana Stroganova
> Subject: Re: [Mne_analysis] group dSPM?
>
> if the noise normalization is different for the 3 orientations you
> cancel the effect
> of the fixed or loose orientation and end up with solutions that look very
> much
> like free orientation solutions.
>
> Alex
>
> On Fri, Jun 8, 2012 at 7:12 PM, Hari Bharadwaj <hari at nmr.mgh.harvard.edu>
> wrote:
>> Question: Why is the noise normalization the same for the 3
>> orientations?
>> Shouldn't it depend on the 3 lead fields?
>>
>> Hari
>>
>> On Fri, June 8, 2012 12:01 pm, Alexandre Gramfort wrote:
>>>>  If you combine using A = x^2 + y^2 + z^2 (i.e without the square root
>>>> or
>>>> square what MNE gives you) and if x,y,z are *after noise
>>>> normalization*,
>>>> then it is reasonable to assume that A is chi2 as long as the noise
>>>> covariance was computed using a large number of points, I wouldn't be
>>>> concerned about variances being different since each has approximately
>>>> variance 1 in the null...
>>>
>>> the noise normalization is the same for x, y and z and as you
>>> regularize
>>> more the tangential components than the radial I don't think the
>>> variance will be
>>> the same even when you apply dSPM to noise. But I should check as it's
>>> just an intuition.
>>>
>>>> The maps we have gotten from running long permutation tests at the
>>>> group
>>>> level and then thresholding using this transformation look very
>>>> similar.
>>>> So our current practice is to use this transform and run the usual
>>>> parametric analyses and invest time in running permutations as
>>>> confirmation once we see something we like.
>>>
>>> ok. Thanks for sharing your experience.
>>>
>>> Alex
>>>
>>>
>>>
>>
>>
>> --
>> Hari Bharadwaj
>> PhD Candidate, Biomedical Engineering,
>> Boston University
>> 677 Beacon St.,
>> Boston, MA 02215
>>
>> Martinos Center for Biomedical Imaging,
>> Massachusetts General Hospital
>> 149 Thirteenth Street,
>> Charlestown, MA 02129
>>
>> hari at nmr.mgh.harvard.edu
>> Ph: 734-883-5954
>>
>>
>>
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-- 
Hari Bharadwaj
PhD Candidate, Biomedical Engineering,
Boston University
677 Beacon St.,
Boston, MA 02215

Martinos Center for Biomedical Imaging,
Massachusetts General Hospital
149 Thirteenth Street,
Charlestown, MA 02129

hari at nmr.mgh.harvard.edu
Ph: 734-883-5954