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

[Vitoria Piai]

Hi all, Eelke,

Maybe adding to Eelke's remark, although I'm not working with motor beta 
activity and lateralised index.
I'm also using DICS and my very clear, focal sensor-level effects all of 
a sudden become localised to the whole brain or a whole hemisphere!
I happen to have heard from Jan-Mathijs that there may something going 
on at the moment, but at least for now, Eelke, I don't think this error 
is unique to our data (and I was actually going to post my incompatible 
sensor-source results here soon, so now it's less work for me :)

Looking forward to hearing your updates,
(I'm interrupting my source-level analyses for the time to get a better 
feeling for the sensor-level data first)
Vitória

On 21-11-2013 17:36, Eelke Spaak 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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-- 
Vitória Piai
PhD Candidate
Donders Institute for Brain, Cognition and Behaviour
Centre for Cognition, Radboud University Nijmegen
Montessorilaan 3, B.01.05
6525 HR Nijmegen
The Netherlands

Email : V.piai at donders.ru.nl
Phone : +31 24 3612635
www.vitoriapiai.com