[FieldTrip] Motor beta activity - DICS solution more noisy than sensor data?

["Jörn M. Horschig"]

Hi Eelke,

since everyone jumped on the train, here my 2 cents:
To verify whether this is a newly introduced bug, maybe run a tutorial 
test script that includes beamforming. If they look alright, it gets 
more likely that it is you or your data and not fieldtrip :) It's not 
definite evidence of course though. SinceVitoria also experiences 
strange things, it might be something worthwhile to investigate.

My initial guess from the plots is that there is something wrong with 
the forward model. All unit problems should have been resolved, but just 
to be sure you could check whether all objects are in the same unit 
(make it 'cm' as the grads are).

Best,
Jörn

Charidimos Tzagarakis wrote:
> Eelke,
> Thinking again about my second suggestion (regarding individual 
> variability) I actually can't think of a case where this could 
> realistically produce what you get. On the other hand, looking at TF 
> maps per subject and channel (on the "helmet" layout), normalised with 
> a "rest" epoch,  may help spot something unusual.
> Best,
> Haris
>
> Charidimos [Haris] Tzagarakis MD, PhD, MRCPsych
> University of Minnesota Dept of Neuroscience and Brain Sciences Center
>
>
>
> On 21 November 2013 18:09, Charidimos Tzagarakis <haristz at gmail.com 
> <mailto:haristz at gmail.com>> wrote:
>
>     Hi Eelke,
>     Provided there is no major recent revision of the DICS code, I
>     would have expected motor desynchronisation to show up pretty
>     well. Are the maps shown at source and channel level straight
>     differences of L and Right hand conditions at the beta band (I
>     hope I am correctly interpreting your paradigm) ? If so it might
>     be helpful in pinpointing the problem/as a sanity check  to see
>     what happens when you use beta desynchonisation (ie change
>     relative to the baseline) instead for each condition, and see
>     source/channel maps of that separately for L and R and then when
>     you take the difference. I suppose the main element this checks
>     for is whether L and R conditions have the same baseline.
>     This doesn't immediately explain why source and channel results
>     are different but in the absence of any other clues it may be a
>     way to 2ble check the whole process.
>
>
>     Another point to consider is that, although beta changes should
>     appear in all subjects, it is possibly true that there are
>     individual differences in the actual beta range and frequency bin
>     of maximum effect. If you are using the same settings for all
>     subjects when you beamform with DICS you may be missing some of
>     the effect (true, this is also the case for channel data but there
>     may be subtle differences that add up - there are many voxels and
>     few channels). I believe it may be useful to see what happens when
>     you run the beamformer tailored to each subject's particular beta
>     characteristics (ie change the "foi" for each subject, keep the
>     tapsmofrq the same - possibly smaller) and then combine everything
>     (you'll need of course to come up with a relative metric such as
>     perc. change when you combine all subjects to account for the
>     slightly different frequencies you used )
>
>     Best,
>     Haris
>
>     Charidimos [Haris] Tzagarakis MD, PhD, MRCPsych
>     University of Minnesota Dept of Neuroscience and Brain Sciences Center
>
>      
>
>
>     On 21 November 2013 10:36, Eelke Spaak <eelke.spaak at donders.ru.nl
>     <mailto:eelke.spaak at donders.ru.nl>> wrote:
>
>         Fellow FieldTrippers,
>
>         Currently I am looking at a contrast for left- versus
>         right-hand index
>         finger button presses. As expected, on sensor level (combined
>         planar
>         gradient, grand average) I see a clear lateralisation in beta band
>         power starting at least 0.5s before the button press (see
>         https://db.tt/Rtch3Qjy). Both 'blobs' are significant; there is
>         clearly more beta power ipsilateral to the response hand. I would
>         prefer to do further analyses on source level, so I attempt to
>         reconstruct the sources for this effect using DICS beamformer
>         (common
>         filter, applied to both conditions separately; fixedori and
>         realfilter
>         = 'yes'). The grand average results for this (again contrast
>         left vs
>         right response hand) are shown at https://db.tt/IBQZG0d8 .
>         (Ignore the
>         R/L-flip, this is radiological convention.)
>
>         As you can see, the source level solution is much more blurry
>         than on
>         sensor level. This picture is without using any regularisation
>         (lambda
>         parameter), the results are even worse when I use lambda =
>         '5%'. The
>         negative blob (right hand higher power than left) becomes
>         'marginally
>         significant' on source level (p ~ 0.06) where it was p < 0.001 on
>         sensor level. The positive blob is nowhere near significant.
>         Also, the
>         individual results are much less topographically consistent on
>         source
>         than on sensor level (explaining the worse statistics).
>
>         I have checked the segmentation of my MRIs, the 'gray' seems to be
>         nicely within the head all the time. Also, I have manually
>         verified
>         the alignment of headmodel, sourcemodel, and gradiometer
>         information
>         for all subjects.
>
>         As a final note, the above sensor-level plot was taken from a
>         'slice'
>         out of a planar-gradient time-frequency analysis (mtmconvol). The
>         ingredient for the beamformer was an mtmfft fourier spectrum
>         on the
>         axial gradiometer data, obtained for just the time-frequency
>         range of
>         interest (subselect toilim [-0.5 0], mtmfft foi = 23,
>         tapsmofrq = 7).
>         When I compute condition-averaged power based on these fourier
>         spectra
>         and look at the contrast, the results are again as expected:
>         https://db.tt/n2P3UKcQ (of course less localised because of axial
>         gradient vs planar). The freq structures underlying this
>         contrast are
>         exactly the same as those going into ft_sourceanalysis, so the
>         problem
>         must be in the source analysis step (and/or in the preparation
>         of the
>         geometric information, although these seem fine by visual
>         inspection).
>
>         Does anyone have any idea that might explain these seemingly
>         contradictory results? I would have expected demixing to improve
>         signal-to-noise ratio, rather than worsen it.
>
>         Thanks!
>         Best,
>         Eelke
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