[FieldTrip] Why are time-frequency results so much different from Beamformer source localization?
Stephen Whitmarsh
stephen.whitmarsh at gmail.com
Tue Nov 24 11:00:31 CET 2020
Hi Patrick,
Concerning L-R, it's not about the MRI, but rather how you are looking at
it - top to bottom, or bottom-to-up, left as left (anatomical), or left as
right (radiological). One thing you can do is to not use the 'ortho', but
rather 'surface' method of plotting - that would give you a real 3D
representation. If that ends up looking like how you would expect it then
perhaps that would be enough :).
I don't know why inward_shift doesn't work for you; but check this tutorial
in which it is used:
https://www.fieldtriptoolbox.org/tutorial/beamformer_lcmv/
What I also don't really get is how you get those very dramatic
pixelated edges in the source plot, while you keep very smooth / highres
grid inside?
Anyway, that sourceplot doesn't look very good, it seems even worse/weirder
than the previous one. The way you mask isn't helping either - you are
cutting away one whole part of the contrast and I don't see why you would
want to do that. Perhaps use rel
Op di 24 nov. 2020 om 10:29 schreef Patrick Wiegel <
patrick.wiegel at sport.uni-freiburg.de>:
> Hi Stephen, hi fiedltrip community
>
> first of all, thanks for the good discussion that we have:-).
>
> The 2 channels in the back were indeed a bit noisy, so I removed those. I
> attached the results from the new mtmfft analysis. The abs and rel
> differences between A and B become clearer now, although you could still
> argue to remove the most right posterior channel. However, I don’t want to
> loose too much information and I think it is okay to stick with this.
>
> Your argumentation on the right/left behavior in MRIs was helpful. I had
> to think/read about that for a while. Since I am using the standard_MRI
> from the fieldtrip page (
> https://github.com/fieldtrip/fieldtrip/tree/master/template/headmodel), I
> cannot certainly tell where left and right is. the transformation matrix of
> the standard MRI is the following which leads me to assume that right is
> right?
>
> 0.1000 0 0 -12.8500
> 0 0.1000 0 -12.8500
> 0 0 0.1000 -12.8500
> 0 0 0 1.0000
>
>
> I tried to use a larger grid during ft_prepare_sourcemodel, however, it
> seems that as soon as I use a negative inward shift, the ft_sourceanalysis
> function does not work. I get the following error.
>
>
> Error using svd
> Input to SVD must not contain NaN or Inf.
>
> Error in ft_inverse_dics>pinv (line 590)
> [U,S,V] = svd(A,0);
>
> Error in ft_inverse_dics (line 475)
> filt = pinv(lf' * invC * lf) * lf' * invC; % Gross eqn. 3 & van
> Veen eqn. 23, use PINV/SVD to cover rank
> deficient leadfield
>
> Error in ft_sourceanalysis (line 763)
> dip(i) = ft_inverse_dics(sourcemodel, sens, headmodel, [],
> squeeze_Cf, methodopt{:}, leadfieldopt{:});
>
> Error in FT_example_without_baseline (line 121)
> sourceAll_beta = ft_sourceanalysis(cfg, data_beta);
>
>
> Not using a negative inward-shift results in the attached
> source-differences between conditions.
> Best, Patrick
>
>
>
> *Patrick Wiegel*
> Department of Sport and Sport Science
>
> University of Freiburg
> Sandfangweg 4
> 79117 Freiburg i. Br .
>
> phone: +49 (0)761/ 203-4550
> email: patrick.wiegel at sport.uni-freiburg.de
> <patrick.wiegel at sport.uni-freiburg.de>
> web: www.sport.uni-freiburg.de
>
> Am 20.11.2020 um 13:02 schrieb Stephen Whitmarsh <
> stephen.whitmarsh at gmail.com>:
>
> Hi Patrick,
>
> Things are becoming clearer now, good to see.
>
> So yes, that artefact in the back of the head: I would definitely take
> care of that. Maybe do another run of (visual) artefact rejection, or
> remove those channels.
> That the effect is less apparent in the relative difference is not
> surprising as the absolute differences are expected to be bigger than on
> other channels due to large amplitudes of the artefacts, which will be
> 'normalized' over channels when using a relative difference.
>
> I would be careful about interpreting left-right on the plot. Note that
> you assume you are looking from the top/front of the head
> downwards/backwards - but there is no way of confirming that based on the
> picture! People often place some kind of marker on the left or right side
> of the head when doing an MRI scan - e.g. a vitamin D (?) capsule I think
> so that one can verify the radiological versus anatomical (?)
> representation (LR is flipped in radiological plots). You could do this
> once, and verify your pipeline, if you want to be 100% sure.
>
> Lastly, it seems that the source picture could benefit from using a
> slightly larger beamformer scanning grid so to overlap more with the edge
> of the brain/skull. I used to do that using a negative value for the inward
> shift parameter (cfg.inwardshift) in ft_prepare_sourcemodel. Maybe there is
> an easy way for you to do that in your pipeline as well.
>
> Looking good!
> Stephen
>
>
>
>
>
> Op vr 20 nov. 2020 om 12:31 schreef Patrick Wiegel <
> patrick.wiegel at sport.uni-freiburg.de>:
>
> Dear Stephen,
>
> thanks for the response.
>
> I followed your suggestions and attached the plots. I performed Morlet
> wavelets TF analyses and mtmfft for conditions A and B. I did not use a
> baseline correction to see whether this is necessary. As you can see in the
> topographical plots, both conditions (A and B) have high power in 2
> posterior electrodes (I guess some kind of artifact or noise is the
> reason). Using the abs. difference between A and B does not get rid of that
> fact, probably because one condition is more affected than the other. Using
> the rel. difference does a better job and resembles my previous
> baseline-corrected results. I also compared the baselines and they were not
> different. The two conditions were furthermore randomized within the same
> protocol. I assume that adding a baseline is thus unnecessary.
>
> Regarding the source_analysis, the results for individual conditions A and
> B (NAI) as well as abs dif between A and B look weird again. But that’s
> very likely due to the same problem as for the power. For rel dif between A
> and B, the source plot seems more reasonable, although I expect the source
> to be in the right hemisphere and not in the left as shown in the plot. I
> controlled that all my input data (electrodes, volume condition model, MRI)
> have the same definition of the axes (x+ pointing right, y+ pointing
> anterior, z+ pointing superior) so I don’t think there is a right-left
> mix-up!?
>
> Now I am wondering whether I have to accept the result for the rel
> difference in sources between A and B (which is very hard to interpret and
> justify after what I am observing in the TF analysis, namely an effect in
> the other hemisphere) or whether there might be still some issue in my
> approach. It is very hard for me to believe that the source analysis does
> have so strong assumptions such that it can be in such strong contrast to
> my power data.
>
> If I am correct, what I input into the source analyses (apart from my
> models) for conditions A and B are 1) the corresponding power matrix (64
> channels x 1 power value) and the CSD (2016 * 1). It could of course be
> that the source analysis is very sensitive to the large abs differences in
> power/CSD data between electrodes. However, I also used cfg.supchan
> (suppressing specific channels) in the source analysis for the 2 channels
> that were „off“ to see whether this makes it different.However, the source
> result is similar.
>
> Any comment is appreciated. All plots, data and scripts are at (
> https://github.com/PatrickWiegel/EEG-Beamformer-Source-localisation).
>
> Best, Patrick
>
> %%
>
> cfg = [];
> cfg.headmodel = vol;
> cfg.elec = elec_aligned;
> cfg.reducerank = 2;
> cfg.resolution = 1;
> sourcemodel_beta = ft_prepare_leadfield(cfg,data_beta);
>
> %%
>
> cfg = [];
> cfg.method = 'dics';
> cfg.sourcemodel = sourcemodel_beta;
> cfg.headmodel = vol;
> cfg.elec = elec_aligned;
> cfg.frequency = 28;
> cfg.dics.projectnoise = 'yes';
> cfg.dics.keepfilter = 'yes';
> cfg.dics.realfilter = 'yes';
> sourceAll_beta = ft_sourceanalysis(cfg, data_beta);
>
> %%
>
> cfg = [];
> cfg.method = 'dics';
> cfg.headmodel = vol;
> cfg.elec = elec_aligned;
> cfg.dics.projectnoise = 'yes';
> cfg.dics.keepfilter = 'yes';
> cfg.dics.realfilter = 'yes';
> cfg.sourcemodel = sourcemodel_beta;
> cfg.sourcemodel.filter = sourceAll_beta.avg.filter;
>
> source_beta_1 = ft_sourceanalysis(cfg, data_beta_1);
> source_beta_0 = ft_sourceanalysis(cfg, data_beta_0);
>
>
> %%
>
> sourceNAI1 = source_beta_1;
> sourceNAI1.avg.pow = source_beta_1.avg.pow ./ source_beta_1.avg.noise;
>
> sourceNAI0 = source_beta_0;
> sourceNAI0.avg.pow = source_beta_0.avg.pow ./ source_beta_0.avg.noise;
>
> cfg = [];
> cfg.parameter = 'pow';
> cfg.operation = 'x1./x2';
> sourceDiff_beta_rel = ft_math(cfg, source_beta_1,source_beta_0);
>
> cfg = [];
> cfg.parameter = 'pow';
> cfg.operation = 'x1 - x2';
> sourceDiff_beta_abs = ft_math(cfg, source_beta_1,source_beta_0);
>
> %%
> cfg = [];
> cfg.downsample = 2;
> cfg.parameter = 'pow';
> sourceDiffInt_beta_dif_rel = ft_sourceinterpolate(cfg,
> sourceDiff_beta_rel , mri_realigned);
> sourceDiffInt_beta_dif_abs = ft_sourceinterpolate(cfg,
> sourceDiff_beta_abs , mri_realigned);
> sourceDiffInt_beta_dif_0 = ft_sourceinterpolate(cfg, sourceNAI0 ,
> mri_realigned);
> sourceDiffInt_beta_dif_1 = ft_sourceinterpolate(cfg, sourceNAI1 ,
> mri_realigned);
>
> %%
> maxval_beta_rel = max(sourceDiffInt_beta_dif_rel.pow);
> maxval_beta_abs = max(sourceDiffInt_beta_dif_abs.pow);
>
> %%
> cfg = [];
> cfg.nonlinear = 'no';
> sourceDiffIntNorm_beta_dif_rel = ft_volumenormalise(cfg,
> sourceDiffInt_beta_dif_rel);
> sourceDiffIntNorm_beta_dif_abs = ft_volumenormalise(cfg,
> sourceDiffInt_beta_dif_abs);
> sourceDiffIntNorm_beta_dif_0 = ft_volumenormalise(cfg,
> sourceDiffInt_beta_dif_0);
> sourceDiffIntNorm_beta_dif_1 = ft_volumenormalise(cfg,
> sourceDiffInt_beta_dif_1);
>
> *Patrick Wiegel*
> Department of Sport and Sport Science
>
> University of Freiburg
> Sandfangweg 4
> 79117 Freiburg i. Br .
>
> phone: +49 (0)761/ 203-4550
> email: patrick.wiegel at sport.uni-freiburg.de
> <patrick.wiegel at sport.uni-freiburg.de>
> web: www.sport.uni-freiburg.de
>
> Am 19.11.2020 um 14:59 schrieb Stephen Whitmarsh <
> stephen.whitmarsh at gmail.com>:
>
> Dear Patrick,
>
> Nice to see you are progressing. The TFR topoplots look nice (and the
> effect seems even stronger in the mtmfft version)!
>
> I agree the source reconstruction looks pretty iffy. A single dot like
> that shouldn't be there - no source in Beamformer is so focal. It suggests
> something is off, which could be as simple as a single electrode doing
> something weird (given how focal and superficial the artefact is).
>
> I would take another step back;
> I would first look at the distribution of your absolute beta, i.e. without
> baseline correction. For both conditions.
> I would then look at the distribution of your baseline beta, i.e. only
> your baseline. For both conditions.
> You should satisfy yourself that the results make sense, i.e. nothing too
> drastic, focal, etc., and some density of beta power around the central
> sulcus (?).
>
> Now I understand your contrast better, I have to ask why you do the
> baseline correction as you do. In this way you are using a separate BL for
> each condition, meaning that after BL-correction you are actually also
> comparing BL as much as you are comparing post-BL activity - after
> BL-correction any differences in BL become difference in your active
> period.
> If you assume differences in BL you might want to compare those separately
> before you 'add' them to your active period. If you have a randomized
> design, you might not want to do any BL correction in your contrast at all.
> Also, if there are no differences in BL, then it would make no difference...
> So, when you have a good sense of the absolute values and source
> distributions, you might want to try just comparing active period A vs B,
> relative (/) or absolute (-), and forget about the BL. Sometimes the BL is
> more noisy than the active period even.
>
> Anyway, your script looks fine to me, but it's been a while. However, I
> suspect that one of your datasets (A, A_BL, B, or B_BL) has some artefact
> that is introduced in the contrast.
>
> Cheers,
> Stephen
>
>
>
>
>
>
>
>
>
>
>
> Op do 19 nov. 2020 om 13:27 schreef Patrick Wiegel <
> patrick.wiegel at sport.uni-freiburg.de>:
>
> Dear fieldtrip community,
>
> first of all, thank you for your valuable responses @Tzvetan and Stephen.
> They were both very helpful and got me to dig deeper into the analysis.
>
> You were absolutely right in describing my approach for my Morlet wavelet
> TF analysis. The data were baseline corrected (in db). When contrasting the
> 2 conditions of interest, I receive the attached figure (dif_morlet). After
> your responses, I took the same approach for the mtmfft data
> with cfg.tapsmofrq = 8 (center frequency at 28 Hz) to reproduce the effect
> that I found between 20-35 Hz in the morlet wavelet analyses. I
> calculated frequency analysis (mtmfft) for both conditions (data_1, data_0)
> and their baseline periods (data_1_bas, data_0_bas) separately and then
> „baseline corrected“ the data from both conditions (also in db, see
> dif_mtmfft). The differences between conditions are now very similar
> between both approaches (morlet vs mtmfft).
>
> In the next step. I followed your advice and performed source analyses of
> frequency_data for all 4 conditions separately (pow_1, pow_0,
> pow_1_baseline, pow_0_baseline) as well as from all_data from
> ft_appenddata. For the source_analyses from all 4 conditions, I am using
> the common filter from all_data as well as the source model
> (ft_prepare_leadfieldds) from all_data. After performing the
> ft_source_analysis function for all 4 conditions, I am trying to replicate
> what I have done with the power data ( in db). For this reason, I am using
> ft_math with source_1_dif = 10*log10 (source_1/source_1_baseline) and the
> same for source_0. After this, I am subtracting source_0_dif
> from source_1_dif as done during the frequency analysis yielding my final
> source data.
>
> After interpolating the source differences, I am using ft_volumenormalise.
> The result from this approach is attached in dif_sources.
>
> I am a bit puzzled since I would not expect these source data based on my
> power mtmfft data. I made sure that the coordinate systems and units are
> the same across models (volume_conduction_model, electrodes, MRI) but I am
> wondering whether I am doing something wrong in performing the
> source_analysis or whether the source results are reasonable.
>
> Again, I uploaded the script and the data such that my analysis pipeline
> could be reproduced by everyone who is interested (
> https://github.com/PatrickWiegel/EEG-Beamformer-Source-localisation).
> Below, I am attaching the code for the source_analyses steps. Any comments
> on my results or approach are welcome.
>
> All the Best from Germany,
> Patrick
>
>
>
> .
>
>
>
>
>
>
> cfg = [];
> cfg.method = 'dics';
> cfg.headmodel = vol;
> cfg.elec = elec_aligned;
> cfg.dics.projectnoise = 'yes';
> cfg.dics.lambda = '5%';
> cfg.dics.keepfilter = 'yes';
> cfg.dics.realfilter = 'yes';
> cfg.sourcemodel = sourcemodel_beta;
> source_beta_1 = ft_sourceanalysis(cfg, data_beta_1);
> source_beta_0 = ft_sourceanalysis(cfg, data_beta_0);
> source_beta_1_bas = ft_sourceanalysis(cfg, data_beta_1_bas);
> source_beta_0_bas = ft_sourceanalysis(cfg, data_beta_0_bas);
>
>
> %%
> cfg = [];
> cfg.parameter = 'pow';
> cfg.operation = '10*log10(x1/x2)';
> sourceDiff1_beta = ft_math(cfg, source_beta_1,source_beta_1_bas);
> sourceDiff0_beta = ft_math(cfg, source_beta_0,source_beta_0_bas);
>
> sourceDiff_beta_dif = sourceDiff1_beta;
> sourceDiff_beta_dif.pow = sourceDiff1_beta.pow - sourceDiff0_beta.pow;
>
> %%
> cfg = [];
> cfg.downsample = 2;
> cfg.parameter = 'pow';
> sourceDiffInt_beta_dif = ft_sourceinterpolate(cfg, sourceDiff_beta_dif ,
> mri_realigned);
>
> %%
> maxval_beta = max(sourceDiffInt_beta_dif.pow);
> %%
> cfg = [];
> cfg.nonlinear = 'no';
>
> sourceDiffIntNorm_beta_dif = ft_volumenormalise(cfg,
> sourceDiffInt_beta_dif);
>
> %%
> cfg = [];
> cfg.method = 'ortho';
> cfg.interactive = 'yes';
> cfg.funparameter = 'pow';
> cfg.maskparameter = cfg.funparameter;
> cfg.funcolorlim = [0.0 maxval_beta];
> cfg.opacitylim = [0.0 maxval_beta];
> cfg.opacitymap = 'rampup';
> ft_sourceplot(cfg, sourceDiffIntNorm_beta_dif);
> %%
>
>
> *Patrick Wiegel*
> Department of Sport and Sport Science
>
> University of Freiburg
> Sandfangweg 4
> 79117 Freiburg i. Br .
>
> phone: +49 (0)761/ 203-4550
> email: patrick.wiegel at sport.uni-freiburg.de
> <patrick.wiegel at sport.uni-freiburg.de>
> web: www.sport.uni-freiburg.de
>
> Am 13.11.2020 um 12:48 schrieb Stephen Whitmarsh <
> stephen.whitmarsh at gmail.com>:
>
> Hi Patrick, (and Tzvetan - who responded in the meantime),
>
> I agree that you should start with a good correspondence between your
> initial results (wavelets), and the power difference you are trying to
> localize (with mtmfft).
>
> Wavelets and fft methods can result in very similar - practically
> identical - results, but that depends on a number of parameters, especially
> concerning frequency-smoothing; Wavelets are typically defined in terms of
> nr. of cycles, while fft methods are defined in terms of time-windows.
> Wavelets defined in nr. of cycles will have a decreasing time-windows with
> increasing frequency, while with mtmfft time windows will remain the same.
> Because of this, frequency smoothing will increase with frequency with
> wavelets, and stay the same with a constant time window. Of course, you can
> make the fft time window frequency-dependent, as is described in the
> frequency-analyses tutorial, to make them more comparable. Secondly, the
> fft method allows you to specify frequency smoothing when you use Sleppian
> multitapers (cfg.taper = 'dpss'). To be clear, you should be able to
> recognize these differences in frequency smoothing/resolution when looking
> at the different TFR plots. Currently they look too different, probably
> because of something else (baseline?) than a wavelet-fft difference.
>
> I would advise making a 'full' TFR with mtmfft similar as your wavelet
> results so that you can properly compare them. This allows you to also set
> the best parameters for smoothing/timeperiods. In fact, you can use the
> multitaper approach to more accurately extract the power in a certain
> frequency band. Only then compare the topo's, and only then do the
> beamformer, so you will pick up any unexpected results.
>
> Because of different contrasts/baseline correction, I cannot compare the
> topo's / beamformer results.
>
> Bon courage,
> Stephen
>
>
>
>
>
>
>
>
>
> Op vr 13 nov. 2020 om 11:30 schreef Patrick Wiegel <
> patrick.wiegel at sport.uni-freiburg.de>:
>
> Dear Field-trip community,
>
> I am working on EEG data from a motor learning task. in the first part of
> my analysis, I performed a time-frequency analysis (Morlet wavelet method)
> and cluster-based permutation test. I compared two different conditions
> (correct & incorrect movements) within subjects and found a significant
> difference in the beta band (20-35 Hz) when subjects were provided with
> feedback. I attached the topographical plot from one subject that
> illustrates the difference between the conditions at the selected time
> point and frequency (dif_morlet). As you can see, beta power was higher in
> one condition in lateral frontal electrodes.
>
> Now, I would like to better spatially locate the effect for this single
> subject. For this purpose, I followed the Beamformer tutorial on the
> website. In the first step, I selected the raw data of interest (at the
> time where the effect was seen) and performed frequency analyses for the
> 20-35 Hz range (mtmfft, contrast between conditions shown in dif_mtmfft).
> There is an apparent difference between dif_morlet and dif_mtmfft and I am
> wondering why this is the case? One apparent reason is that the
> time-frequency data were baseline corrected and the frequency data from
> mtmfft not.
>
> Using the power and CSD data from mtmfft, I performed the source analysis
> with a standard MRI and head model. The source was located in the left
> hemisphere (source plot), which is in stark contrast to the time-frequency
> data (dif_morlet). I am aware that the source analysis is based on a lot of
> assumptions (conductivity etc.) that influence the calculations but it is
> very difficult to make sense of such discrepancy.
> Maybe it is necessary to also baseline correct the mtmfft data before
> inputting them to the source analysis? Or doing the contrasting source
> localization betwee a single condition and the corresponding baseline data
> before contrasting the 2 conditions?
>
> I am providing all necessary files and scripts (TF_analysis & BEAMFORMER)
> if anyone is interested to reproduce the results that I am showing and
> reporting for this single subject (
> https://github.com/PatrickWiegel/EEG-Beamformer-Source-localisation).
>
> I would appreciate any help and discussion on that.
>
> All the best,
> Patrick
>
>
>
>
>
> *Patrick Wiegel*
> Department of Sport and Sport Science
>
> University of Freiburg
> Sandfangweg 4
> 79117 Freiburg i. Br .
>
> phone: +49 (0)761/ 203-4550
> email: patrick.wiegel at sport.uni-freiburg.de
> <patrick.wiegel at sport.uni-freiburg.de>
> web: www.sport.uni-freiburg.de
>
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