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

[Charidimos Tzagarakis]

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> 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> 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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>
>
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