[Mne_analysis] Comparing conditions

[Daniel Goldenholz]

Yury

Thank you for your comments. That textbook you mentioned looks very good.
Thank you for the link.

On the subject of subtracting F statistics, the more I think about it, the
more I wonder if it was premature for me to issue that blanket statement
that such a technique does not make statistical sense. Perhaps Yury, you are
right and we are simply looking at the difference in SNR for some cases.
The things that worry me still are:
- if a dipole were to rotate in a fixed position with a fixed amplitude,
what would that do in the subtraction case?
- when subtracting these F statistics, no account is made for temporal
relationships. So if I take the subtraction of two different time points
from the same source with a sinusoidal signal that are out of phase by 180
degrees, wouldn't this seem like one time point is more "significant" than
the other, when in fact you are looking at different phases of the same
thing?
- If the noise is not the same (i.e. when comparing two different sources),
what is the meaning of the subtraction? Does it relate to significance of
effect, or merely SNR due to anatomy/physics?

Maybe I am just too new to the field and am asking questions that have been
well picked over already. Anyone who can see where I went astray, please
feel free to chime in and straighten me out.

Daniel

On Thu, Oct 2, 2008 at 1:20 PM, Yury Petrov <y.petrov at neu.edu> wrote:

> Daniel, first of all, thanks for the great MNE review. Some typos that I
> noticed:
> page 30: remove Gaussian source distributions
> page 33: theorm -> theorem
>
> I find the MNE derivation based on Bayesian max-likelihood method (e.g. in
> the Inverse Problem Theory book below) both simpler and more satisfactory.
> In particular, it makes the nature of the MNE assumptions much more
> explicit.
> http://www.ipgp.jussieu.fr/~tarantola/Files/Professional/Books/index.html<http://www.ipgp.jussieu.fr/%7Etarantola/Files/Professional/Books/index.html>
>
> I don't see what's 'not cool' with subtracting dSPMs for two conditions.
> dSPM is, essentially, a singnal-to-noise ratio. Assuming that your noise was
> the same in both conditions (i.e. the same noise covariance matrix) we just
> subtract signals, right?
>
>
> On Oct 2, 2008, at Oct 2, 2008 | 11:49 AM, Daniel Goldenholz wrote:
>
>  Hi Alex
>>
>> For what it is worth, I thought about these kinds of questions some time
>> ago and presented a talk that was supposed to open up further discussion and
>> debate. The PDF of that talk is here:
>>
>> http://www.nmr.mgh.harvard.edu/~daniel/links/presentation/stats_on_roi.pdf<http://www.nmr.mgh.harvard.edu/%7Edaniel/links/presentation/stats_on_roi.pdf>
>>
>> It includes some basics on the mathematics and assumptions inherent in
>> them. Then the talk veers into the speculative with some thoughts on newer
>> possible methods for comparing conditions when you have multiple subjects
>> and multiple conditions.
>>
>> I am still interested in developing these questions further, so let me
>> know if these ideas are helpful.
>>
>> Daniel
>>
>>
>> On Mon, Sep 29, 2008 at 6:28 AM, Alex Clarke <alex at csl.psychol.cam.ac.uk>
>> 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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>>
>>
>> --
>> Daniel Goldenholz MD, PhD
>> --------------------------------------------------------
>> http://www.nmr.mgh.harvard.edu/~daniel<http://www.nmr.mgh.harvard.edu/%7Edaniel>
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-- 
Daniel Goldenholz MD, PhD
--------------------------------------------------------
http://www.nmr.mgh.harvard.edu/~daniel
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