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Hi Markus,<div><br></div><div>Did you consider trying out the beamforming algorithm with multiple dipoles as a source model?</div><div>As opposed to the dipole-fitting (which pinv'es the concatenated leadfields), the beamformer actually tries to suppress contributions of the other guys (provided they are not too much correlated in time). Obviously, the suppression will be more robust against violations of the temporal uncorrelation-assumption, when the leadfield-columns are less correlated. However, I would guess it's worth a try, and it's exactly what you try to achieve: to avoid mixing up correlated leadfield components. But perhaps I did not grasp the problem completely... ;o)</div><div><br></div><div>Yours,</div><div><br></div><div>JM</div><div><br><div><div>On May 28, 2008, at 4:00 PM, Markus Bauer wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite"> <br> Hi fieldtrippers (in particular probably Robert)<br> <br> I have a few slightly more advanced questions regarding the use of dipoles as spatial filters and the creation of multi-dipole models with fieldtrip (questions in bold)<br> I know that the functionality of fieldtrip is not designed for multi-dipole models, but neither Beamformer nor MNE worked somewhat reliably (only worked in rather few subjects and then was usually pretty smeared still), as often is the case...(for evoked fields in this case). So I finally decided to once go back to the "gold standard" of somatosensory EEG/MEG research<br> <br> I have used a serial fit-procedure to fit components of the SEP to EEG data. The early components (50 and 80 ms post response) are largely determined by two single dipoles, plus a symmetric dipole later for bilateral S2. So it is kind of ok to fit simple dipoles to those (though clearly not ideal)<br> <br> My first question: <br> <b>Are there plans to extend the functionality of dipolefitting to allow for proper serial fit (i.e. adding to be fitted dipoles to existing ones) ? I guess it would not be that much rewriting to include a fixed dipole and only fit the parameters of the other?<br> </b><br> Anyway, in order to create spatial filters I have then fitted the orientation of each dipole (estimated from the moments) and thereby reduced the leadfield to a onedimensional one.<br> In order to get to the sourcewaveforms, I have concatenated the (one dimensional) leadfields associated with each source into a common leadfield matrix (4 sources corresponding to 4 rows and 124 columns for each channel) and then took the pseudoinverse of this combined leadfield matrix - for use as spatial filters to obtain sourcewaveforms - for the "complete solution".<br> <br> When I looked at those filters obtained, however, I realized that it did not work that nicely, since the filters looked for at least two components very similar - more similar than the respective leadfields!! (which is contra-intuitive)<br> The topographies of the peaks where I fitted dipoles to are beautiful single dipolar topographies and so are the single leadfield matrices, but the filters are anything else than clean. It is clear that they will represent a mixture between the sources - mutually "supressing" each other. But at least for two of them (the early sources for 50 and 80 ms with clearly distinct topos, presumably area3b and area1) they pretty much mixed up. I was surprised about this cause the topoghraphies look so distinct and I would have assumed this should come out nicely (also considering all the Hari-studies)<br> <br> Anyway, a look at the correlation matrix of the leadfields showed that despite the topographies of those dipoles had quite a different orientation they were correlated at 0.77 (weights over channels). So I guess what happens is identical to a suppressor effect in multiple regresion - when the predictors are too correlated their weights get mixed up.... <br> <br> I tend to use the individual spatial filters now - not the pseudoinverse of the combined source model (consisting of only four, well identifiable sources).<br> My question:<br> <br> <b>Is this justified - to take individual filters when having a multi-dipole model ? </b><i><br> </i><br> I also realized that when I only included the first two sources (the ones described above), and take the pseudoinverse the separation succeeds much better.<br> <br> <b>- Might prior regularization of the leadfield help ?<br> - Are there any standards about this - when to still use a combined leadfield model ? I guess should be the same as in multiple regression cause formulas more or less identical...? But I guess nobody publishing sourcewaveforms from multidipole model cares about it...<br> </b><br> I was a bit "shocked" to see how much this can falsify the results (the waveforms), I also wouldn't have expected the leadfields (and filters...) of such rather different sources to be that strongly correlated. I assume it will be not much different in MEG, cause they were both tangential dipoles, looking almost identical as in MEG (apart from 90deg rotation due to field geometry).<br> My strategy is therefore to either use individual filters of single dipoles or reduce the dipolar model...any other suggestions ??<br> It seems it is really a very ill-posed problem that we're spending our time with....<br> <br> <br> thanks and best wishes <br> Markus <br> <p>----------------------------------</p><p>The aim of this list is to facilitate the discussion between users of the FieldTrip toolbox, to share experiences and to discuss new ideas for MEG and EEG analysis.</p><p> <a href="http://listserv.surfnet.nl/archives/fieldtrip.html">http://listserv.surfnet.nl/archives/fieldtrip.html</a></p><p> <a href="http://www.ru.nl/fcdonders/fieldtrip/">http://www.ru.nl/fcdonders/fieldtrip/</a></p></blockquote></div><br></div></body></html><p>----------------------------------</p>
<p>The aim of this list is to facilitate the discussion between users of the FieldTrip toolbox, to share experiences and to discuss new ideas for MEG and EEG analysis.</p>
<p> http://listserv.surfnet.nl/archives/fieldtrip.html</p>
<p> http://www.ru.nl/fcdonders/fieldtrip/</p>