[Mne_analysis] sLORETA

Hari Bharadwaj hari at nmr.mgh.harvard.edu
Thu Sep 1 11:54:22 EDT 2011
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Hi Elena,

On Thu, September 1, 2011 11:32 am, Elena Orekhova wrote:
> Dear Hari,
> The scaling you suggest will make the data look ‘nice’, with the same mean
> and SD in the baseline. However, if will not resolve the problem.

If the baseline has enough (handwaving) time points, then the scaling
would produce timecourses where each point is distributed as N(0,1) (in
the null) for both left and right labels and  hence  OK for statistical
comparison purposes. Also since you are dividing out the sLORETA scaling
(which is the same for all time points), it is identical to the z-scores
you get from MNE.

> According to MNE manual sLORETA scales signal to noise in some way. It
> seems that it proportionally decreases amplitude of the
> signall-of-interest if it contains more noise.  Is there a way to correct
> for this down-scaling of the signal-of-interest by the sLORETA or would it
> be more correct to use MNE?

Yes.. the sLORETA solution scales the MNE by a factor that depends on SNR
at each vertex. Thus it supresses noisy vertices a little. The MNE is the
unscaled version which is in current units (say nano Amps) and hence you
can compare the left and right directly in a physiological sense. However
the localization will be biased towards superficial sources and noisy
sources. So one other thing you could do is use dSPM or sLORETA to do the
localization/selecting labels etc.. and then use the MNE time series from
those vertices for comparison.


> Thanks,
> Elena
> ________________________________________
> From: Hari Bharadwaj [hari at nmr.mgh.harvard.edu]
> Sent: Thursday, September 01, 2011 3:02 PM
> To: Elena Orekhova
> Cc: mne_analysis at nmr.mgh.harvard.edu
> Subject: Re: [Mne_analysis] sLORETA
> Hi Elena,
>    Just my 2 cents (below) before Matti gives his expert opinion..
> On Thu, September 1, 2011 4:07 am, Elena Orekhova wrote:
>> Dear Matti,
>> I would like to compare amplitudes of activation in response to auditory
>> stimuli in the right and left temporal labels.  I used sLORETA and
>> noticed
>> significant differences in the timecourses during pre-stimulus baseline.
>> As the sLORETA uses noise normalization, I guess that this effect is due
>> to different level of noise in the two labels.  Would it be correct to
>> scale the sLORETA timecourses by e.g. dividing them by the mean
>> pre-stimulus value before statistical comparison of the left and right
>> labels?
> Dividing by the mean is slightly less principled since mean is not a
> 'scale' parameter but rather a 'location' parameter. The scaling used in
> EEGLAB (especially for time-frequency maps) in this situation is a z-score
> derived as follows:
> If x(t) is the original timecourse in a label:
> z(t) = (x(t) - mean(x(baseline)))/std(x(baseline)) ;
> This would give use the same time series whether you use MNE or dSPM or
> sLORETA since they are just scaled versions of each other with the scale
> factor being constant in time.
>> I have also a related question.  The MNE software uses the same inverse
>> operator for MNE and sLORETA.  Is it at all identical to the sLORETA by
>> Pascual-Marqui (Standardized low-resolution brain electromagnetic
>> tomography (sLORETA): Technical details. METHODS AND FINDINGS IN
> It is identical to Pascual-Marqui's solution. 'sLORETA' is a misnomer in
> the sense that it is not a noise-normalized version of LORETA but rather a
> normalization of MNE using a slightly different denominator (related to
> the resolution matrix).
> Rey Ramirez's article on scholarpedia is a nice condensation of many of
> the source localization methods in use:
> http://www.scholarpedia.org/article/Source_localization
> Regards,
> Hari
>> With regards,
>> Elena
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> --
> Hari Bharadwaj
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Hari Bharadwaj

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