[FieldTrip] MNE Source Reconstruction Sanity Check

"Jörn M. Horschig" jm.horschig at donders.ru.nl
Fri Jul 25 08:46:19 CEST 2014


Dear Roey,

I agreet that this is a bad idea - independently of what result you will 
get, the error is just too big to draw any reliable conclusions. Imho, 
you can better try using ICA to decompose your data into components.

Concerning the headmodel, there is a standard BEM headmodel template 
available in FieldTrip.

Best,
Jörn

On 7/24/2014 8:50 PM, Roey Schurr wrote:
> Dear Jim,
> Thank you for drawing my attention to this problem. I have actually 
> tried building a realistic head model using OPENMEG but encountered 
> some compitability problems since our lab does not use Linux. This is 
> indeed one of the most important (short) future tasks - being able to 
> use such realistic head models.
> Best,
> roey
>
>
> On Thu, Jul 24, 2014 at 6:34 PM, E688205 <j.herring at fcdonders.ru.nl 
> <mailto:j.herring at fcdonders.ru.nl>> wrote:
>
>     Dear Roey,
>
>     To add to Diego's comments, since you are dealing with EEG data a
>     single sphere headmodel is not a good idea because it does not
>     take into account the differences in conductivity between the
>     skull, scalp, and brain. This is not a problem for MEG but is
>     important for EEG. Therefore it is better to use, for example, a
>     BEM head model.
>
>     Best,
>
>     Jim
>
>     On 23 jul. 2014, at 16:38, "Lozano Soldevilla, D. (Diego)"
>     <d.lozanosoldevilla at fcdonders.ru.nl
>     <mailto:d.lozanosoldevilla at fcdonders.ru.nl>> wrote:
>
>>     Dear Roey,
>>
>>     In my opinion it's definitely not a good idea to compute MNE
>>     using 19 sensors. There are studies that have found a drastic
>>     localization precision from 31 to 63 electrodes and further
>>     improvements till 123:
>>
>>     http://www.ncbi.nlm.nih.gov/pubmed/15351361 (see figure 1)
>>     http://www.ncbi.nlm.nih.gov/pubmed/12495765
>>
>>     Although it's very difficult to know the "minimum" number of
>>     electrodes needed to accurately localize a given source (it
>>     depends on the strength of the source you want to localize,
>>     source reconstruction algorithm, data noise...), 19 electrodes
>>     are too low to trust the results you can get.
>>
>>     best,
>>
>>     Diego
>>
>>
>>     ------------------------------------------------------------------------
>>      From roeysc atgmail.com  <http://gmail.com>   Mon Jul 21 11:21:32 2014
>>     From: roeysc atgmail.com  <http://gmail.com>  (Roey Schurr)
>>     Date: Mon, 21 Jul 2014 12:21:32 +0300
>>     Subject: [FieldTrip] MNE Source Reconstruction Sanity Check
>>     Message-ID: <CAHm4wZBRYo4fV63EL9yXaAQ_W43cHF_8J2b+rNyzd55x4aRviw at mail.gmail.com  <mailto:CAHm4wZBRYo4fV63EL9yXaAQ_W43cHF_8J2b+rNyzd55x4aRviw at mail.gmail.com>>
>>
>>     Dear fieldtrippers,
>>
>>
>>
>>     I want to do a sanity check on mne source reconstruction.
>>
>>     I'm working on continuous EEG recordings (19 electrodes), estimating the
>>     source reconstruction activity using the *mne* (minimum norm estimate)
>>     method, a *template MRI* (Colin27) and a *singlesphere* headmodel. As a
>>     sanity check for the source reconstruction itself, I wanted to compare
>>     conditions in which I could estimate the loci of significant changes, e.g.:
>>     rest vs movement of the hand, moving the right hand vs the left hand, etc.
>>     I have about 60 seconds of recording for each condition.
>>
>>
>>
>>     What I did was:
>>
>>     1) Segment the recording of each condition into many "trials" of 2 seconds
>>     each.
>>
>>     2) For each trial, average the activity in each of the 90 ROIs of the aal
>>     atlas (I excluded the cerebellum from the source reconstruction).
>>
>>
>>
>>     I was wondering what comparison would be best in this case. Since this is
>>     not Evoked Responses data, I find it hard to find relevant ideas, and would
>>     like to hear your thoughts.
>>
>>
>>
>>     1) I did a frequency analysis (mtmfft) in conventional bands of interest
>>     and ran ft_freqstatistics on the resulting structures (using ttest2 and the
>>     bonferoni correction for the multiple comparison problem). This gave some
>>     results, however for most conditions they are not very encouraging (the
>>     ROIs that showed significant differences were not close to those that I
>>     have assumed).
>>
>>
>>
>>     *QUESTION 1*: do you think this is a proper method? Note that I did not use
>>     a frequency based source reconstruction in the first place, because I'm
>>     ultimately interested in the time course in the source space.
>>
>>
>>
>>     2) I was wondering if a cluster based permutation test is impossible to use
>>     here, since this is a continuous recording, so clustering according to time
>>     adjacency seems irrelevant.
>>
>>
>>
>>     *QUESTION 2*: is it possible to use a cluster based statistical test here?
>>     If so, it could be better than a-priori averaging the source activity in
>>     the atlas ROIs, which could mask some of the effects, if they are located
>>     in a small area.
>>
>>
>>
>>     3) Another possibility is looking at the data itself. Unfortunately I
>>     encountered some problems using ft_sourcemovie, though this is a subject
>>     for a different thread.
>>
>>
>>
>>     Any thoughts and advice are highly appreciated!
>>
>>     Thank you for taking the time,
>>
>>     roey
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>>
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-- 
Jörn M. Horschig
PhD Student
Donders Institute for Brain, Cognition and Behaviour
Centre for Cognitive Neuroimaging
Radboud University Nijmegen
Neuronal Oscillations Group
FieldTrip Development Team

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