[FieldTrip] Calculate activation time-course in sourcespace for ERF-data using ft_sourceanalysis

[christian.merkel at med.ovgu.de]

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.