[FieldTrip] ft_megrealign how to avoid?

Stolk, A. a.stolk at fcdonders.ru.nl
Thu Jul 11 17:58:54 CEST 2013

Hi Nenad, You may want to check whether there are systematic differences (over the whole group of subjects) in head position in the first place. Here's an example page showing how to check head position/movement in MEG: http://fieldtrip.fcdonders.nl/example/how_to_incorporate_head_movements_in_meg_analysis The same page also indicates how to 'regress out' variance accounted for by (different) head position. Applying this method on the datasets combined (trials of the two conditions for each subject) allows you to eliminate head position as a potential confound (by using ft_regressconfound prior to computing subject-level statistics). I have no first hand experience on to what extent megrealign can reduce the effects of different head position between two conditions, but from our experiences here we know that systematic differences in head position between conditions may even be noticeable at the source level. Optimally, one thus tries to minimize the influence of head movement wherever possible, i.e. already at recording. This may come in too late for your current dataset, but this online 'realignment' method may improve between- but also within-session consistency in future studies of yours involving the CTF MEG system (a Neuromag version is being developed): http://fieldtrip.fcdonders.nl/faq/how_can_i_monitor_a_subject_s_head_position_during_a_meg_session Hope these documentation pages may be of any help, Arjen ----- Oorspronkelijk bericht -----
> Van: "Nenad Polomac" <polomacnenad at gmail.com>
> Aan: fieldtrip at science.ru.nl
> Verzonden: Donderdag 11 juli 2013 17:09:40
> Onderwerp: [FieldTrip] ft_megrealign how to avoid?
> Dear all,
> I have CTF MEG data and I am interested in a gamma band topographies.
> We recorded two sessions for every subject. Now I would like to
> compare grand averages of these two sessions over all subjects(n=13).
> Without realignment these grand average topographies look ok and
> according to our expectation. However, I know that it would be
> reasonable to do realignment of all datasets (n=13x2) to common
> template. I have tried that with ft_megrealign which uses method
> suggested by Knösche, 2002 and this doesn't work. It gives me very
> weird results, meaning that topographies are totally changed and some
> new occipital activity emerges. I have plotted single subject head
> models(singleshell) together with sensors and they are accurately
> aligned. Then I tried different inwardshift options for ft_megrealign
> and this didn't bring so much success. So my question is, could I go
> without the realignment and what I need to do in order to overcome
> problem with realignment?
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