[FieldTrip] ft_megrealign how to avoid?

[Stolk, A.]

Dear Nenad, You're welcome of course. You're right that after head movement compensation using ft_regressconfound, the sensor level data ideally is not used anymore for source modeling (see http://fieldtrip.fcdonders.nl/example/how_to_incorporate_head_movements_in_meg_analysis -> bottom page, for why this is problematic), i.e. as is required for ft_megrealign. Optimally, ft_regressconfound is therefore used as a last step just prior to ft_XXXstatistics, whether at the sensor level (after ft_megrealign) or at the source level. To address your objective, i.e. showing that a difference is due to the experimental manipulation and not due to different head positions; using ft_regressconfound on trials of, say, condition A and B combined will remove any trial-by-trial signal variance that is due to different head positions from the mean head position of condition A and B. In other words, if a systematic difference observed between condition A and B is caused by systematically different head position between condition A and B, this difference will be removed by ft_regressconfound. If not, the difference may not be caused by different head position, and you have good indication to exclude head position as a potential confound. Yours, Arjen ----- Oorspronkelijk bericht -----
> Van: "Nenad Polomac" <polomacnenad at gmail.com>
> Aan: fieldtrip at science.ru.nl
> Verzonden: Vrijdag 12 juli 2013 15:43:35
> Onderwerp: Re: [FieldTrip] ft_megrealign how to avoid?
> Dear Arjen,
> Thank you very much for you answer! I forgot to mention that I applied
> ft_megrealign on averaged single subject data since we are interested
> in evoked auditory early gamma response. I am familiar with the online
> and offline methods for removing variance which comes from head
> movement. These are very useful tolls. I've tested ft_regresconfound
> before and it worked fine. However, it will not be very helpful in
> this case. Because if I apply ft_regresconfound as you suggested than
> I will loose gradiometers information and than ft_megrealign will not
> work. Furthermore my evoked gamma grand average topographies (TFR of
> planar and axial gradiometers) look now as expected without any
> transformation(ft_regresconfound or ft_megrealign). So what I need is
> some objective evidence that condition difference is due to experiment
> rather than different head positions. :)
> Thank you and all the best!
> Nenad
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