[FieldTrip] beamforming pipeline & comparing sources between groups of participants?
Es, M.W.J. van (Mats)
M.vanEs at donders.ru.nl
Thu May 2 13:03:39 CEST 2019
Dear Ioanna,
Indeed, these types of analysis are exciting, but complex! I have some suggestions for you. They might not solve your problem per se, but at least it should give you some insight in your analysis.
1) As Vladimir suggested, make sure your headmodel and sourcemodel align and are of the same unit. Also, it might be better to look at source contrasts due to the center of the head bias (see http://www.fieldtriptoolbox.org/tutorial/beamformer/#source-analysis-without-contrasting-condition)
- Your frequency analysis looks correct. Since dics sourceanalysis is done on a single frequency, you can use smoothing to still encompass an entire frequency range.
- This means that in the sourceanalysis you don't have to specify the [12 32] but could enter the center frequency: [22 22], since the estimate at this frequency encompasses the entire range.
- In your sourceanalysis you don't have to specify cfg.latency. Your frequency estimate is based on this window already. The cfg.latency option is used when your frequency data is time-resolved (cfg.method = 'mtmconvol'/'wavelet' in ft_freqanalysis).
- In your frequency analysis, there's no need to keep trial information for sourceanalysis. As default, sourceanalysis will average over trials anyway. (trial information is only required if you use some kind of resampling procedure (like cfg.jackknife, cfg.bootstrap etc). (If you'd want to do single-trial analysis, you could multiply the spatial filter (from sourceanalysis) with channel-level single-trial frequency estimates. )
2) Yes, you should do sourceanalysis separately for every participant.
3) Take a look at ft_sourcegrandaverage
4) Very good question, without a straightforward answer.
- If you've already found a statistically significant effect at the channel level, there's no need to do statistics at the source level anymore; you've already rejected the null-hypothesis. You use source analysis only to visualize and interpret the data.
- If you think you can gain statistical sensitivity by going to the source level, you should think of some way to reduce the dimensionality of your data. If you would statistically test power at every grid point, you will probably lose any effect when doing multiple comparison correction. For example, you might know (from the literature) that if there would be an effect, it will most likely be in a specific ROI. Then you could choose to average power in this ROI and test this between groups.
Alternatively, you could define your own ROI based on for example the induced power effect over all subjects: find out which region has the highest power increase (i.e. from baseline) in this task in general (thus irrespective of condition). Use this region the then test differences between conditions.
Hope this helps!
All the best,
Mats
PhD candidate
Dynamic Connectivity
Donders Institute for Brain,
Cognition and Behaviour
e:
m.vanes at donders.ru.nl<mailto:m.vanes at donders.ru.nl>
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Kapittelweg 29, 6525 EN Nijmegen
p:
+31(0)24 36 68291
From: Ioanna Zioga <i.zioga at qmul.ac.uk>
Sent: woensdag 1 mei 2019 16:48
To: litvak.vladimir at gmail.com
Cc: fieldtrip at science.ru.nl
Subject: [FieldTrip] beamforming pipeline & comparing sources between groups of participants?
Dear Vladimir,
For anyone interested, I used SPM to make a template head model which I'm going to use to do beamforming in FT.
I have queries with regards to the source analysis pipeline I'm using in FT, as the results I get don't seem right.
Vladimir, following our previous email exchange (and thanks to your suggestions), I have now managed to compute the lead field matrix in FT (by removing eeglab and fieldtrip directories from the path, and adding spm's).
I want to contrast the sources of two groups of participants (high- vs. low-learners) in two frequency bands (low: 2.5-4.5 Hz; high: 12-32 Hz), from 0.20-1 sec post stimulus onset.
Here is my pipeline:
% Calculate the cross spectral density matrix (e.g., for the 12-32 Hz frequency band)
cfg = [];
cfg.method = 'mtmfft';
cfg.output = 'powandcsd';
cfg.foilim = [22 22];
cfg.taper = 'dpss';
cfg.tapsmofrq = 10;
cfg.keeptrials = 'yes';
cfg.keeptapers = 'no';
cfg.toi = 0.6;
cfg.t_ftimwin = 0.4;
freq = ft_freqanalysis(cfg, EEGft);
% Source analysis using DICS beamformer
cfg = [];
cfg.headmodel = vol1;
cfg.grad = sens1;
cfg.senstype = 'eeg';
cfg.grid = grid;
cfg.method = 'dics';
cfg.frequency = [12 32];
cfg.latency = [0.2 1];
cfg.dics.projectnoise = 'yes';
cfg.dics.lambda = 0;
cfg.dics.keepfilter = 'yes';
cfg.dics.realfilter = 'yes';
sourceA = ft_sourceanalysis(cfg, freq);
% Plot sources
cfg = [];
cfg.method = 'slice';
cfg.funparameter = 'pow';
ft_sourceplot(cfg, sourceA);
=> The source plots look weird though - they scale from 0 to x10^45, and are totally black.
1) Am I doing something wrong in the freqanalysis or the sourceanalysis?
2) Do I need to do the ft_sourceanalysis separately for each participant?
3) Is there a way to average the results of the source analysis over groups of participants?
4) Could you give any direction/resource on how to statistically compare the sources of different groups?
As you all know this is a very exciting but also not trivial analysis.. so I'd be extremely grateful for any help at this point, it'd be very much appreciated! Thanks so much in advance!
Best,
Ioanna
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