[FieldTrip] Calculate activation time-course in sourcespace for ERF-data using ft_sourceanalysis
christian.merkel at med.ovgu.de
christian.merkel at med.ovgu.de
Fri Jul 7 09:19:06 CEST 2017
Hallo to all,
My name is Christian Merkel and im using fieldtrip to estimate sources within the visual cortex using high-res sourcemodels from freesurfer. We use an elekta system in our lab.
I have a question regarding the time course of activation within the sourcespace once i calculated the spatial filter with ft_sourceanalysis.
Here the code for ft_sourceanalysis:
cfg = [];
cfg.method = 'lcmv';
cfg.channel = sens_aligned.label(strcmp(sens_aligned.chantype,'megplanar'));
cfg.grid = lead field_grad;
cfg.vol = bem;
cfg.projectnoise = 'yes';
cfg.keepfilter = 'yes';
cfg.lambda = 1;
data_erf_source = ft_sourceanalysis(cfg, data_cond_erf_bl_avg);
The filter looks good and everything works fine. However I have a conceptual question about the timecourse in sourcespace: As far as I understand, the field avg.mom of data_erf_source contains the activation time_course. So to plot one specific sourcedistribution at one time sample I use:
pow = squeeze(sort(sum(cat(3,data_erf_source.avg.mom{:}).^2,1)))'; % i use the abs norm of the 3 orientation vectors as a value of source strength
figure; ft_plot_mesh(sourcespace, 'vertexcolor', pow(:,time_of_interest));
One other way to get the source distribution at that sample is to convolve the filter with the underlying erf-data:
pow = squeeze(sort(sum(cat(3,data_erf_source.avg.filter{:}).^2,1)))';
figure; ft_plot_mesh(sourcespace, 'vertexcolor', pow * data_cond_erf_bl_avg.avg(:,time_of_interest));
But those to distributions look different. Not completely different, but substantially.
Why are those two ways of calculating the source-distribution different. I thought that ft_sourceanalysis calculates the field 'mom' by basically convolving the erf_data with the spatial filter and therefore should result in the same distribution as the second version. What is the 'right' way in this case and what do you recommend I use.
I thank you for your help.
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