[FieldTrip] Computing source-level ROI EEG connectivity

Michael Glassen - Biomedical Engineer MGlassen at kesslerfoundation.org
Wed May 6 22:34:23 CEST 2020

Hi all,

I am having issues using fieldtrip for source reconstruction and connectivity analysis with preprocessed EEG data. I believe the issue is occurring when I try to project my sources to specific ROIs before getting the connectivity. I have outlined my pipeline below:

-Because I do not have subject MRIs, I am using a template headmodel and sourcemodel that come included with fieldtrip
               - headmodel = standard_bem.mat
               -sourcemodel = cortex_8196.surf.gii

-After importing EEG data, I project the EEG electrodes to the scalp using the code below:
               if strcmp(headmodel.type, 'bemcp')
             scalp_index = 3;
elseif strcmp(headmodel.type, 'dipoli')
             scalp_index = 1;
cfg = [];
cfg.method = 'project';
cfg.headshape = headmodel.bnd(scalp_index);

eegData.elec = ft_electroderealign(cfg, eegData.elec);

-I then calculate covariance on a trial basis and calculate the leadfield matrix:

               cfg = [];
cfg.covariance = 'yes';
cfg.covariancewindow = [-inf 0];
cfg.keeptrials = 'yes';
tlckEeg = ft_timelockanalysis(cfg, eegData);
cfg         = [];
cfg.elec    = tlckEeg.elec;   % sensor information
cfg.channel = tlckEeg.label;  % the used channels
cfg.grid    = sourcemodel;   % source points
cfg.headmodel = headmodel;   % volume conduction model
cfg.singleshell.batchsize = 5000; % speeds up the computation
leadfield   = ft_prepare_leadfield(cfg);

-Now with the leadfield calculated, I calculate sources on a trial basis here:

cfg               = [];
cfg.method        = 'mne';
cfg.sourcemodel   = leadfield;
cfg.headmodel     = headmodel;
cfg.mne.prewhiten = 'yes';
cfg.mne.lambda    = 0;
cfg.mne.scalesourcecov = 'yes';
cfg.rawtrial      = 'yes';
sourceEEG          = ft_sourceanalysis(cfg,tlckEeg);
               sourceEEG =

                              struct with fields:

                              time: [1×357 double]
                              inside: [8196×1 logical]
                              pos: [8196×3 double]
                               tri: [16384×3 double]
                              method: 'rawtrial'
                              trial: [1×400 struct]
                              df: 400
                              cfg: [1×1 struct]

               sourceEEG.trial =

                              1×400 struct array with fields:



ans =

                                             8196         357

-So at this point I have 400 trials, and for each of these trials I have a time course of source power at 8196 voxels and 357 time points for each of those voxels.
-I then import two atlases, one that includes broddman areas and one that includes left and right hemispheres. I interpolate these atlases onto my source model and get the following:
               broddman =
struct with fields:

                                      pos: [8196×3 double]
                                       tri: [16384×3 double]
                                        unit: 'm'
                                       tissue: [8196×1 double] (each of these is a value 1-70, corresponding to which atlas entry the voxel is located in)
                                       tissuelabel: {1×70 cell}(ROI labels for the atlas, includes broddman areas not separated by hemisphere)
                                      cfg: [1×1 struct]
hemis2 =
struct with fields:
pos: [8196×3 double]
                                      tri: [16384×3 double]
                                        unit: 'm'
                                        tissue: [8196×1 double] (each of these is a value 1-7, corresponding to which atlas entry the voxel is located in)

                                         tissuelabel: {1×7 cell} (ROI Labels for hemisphere atlas, contains 3 Left areas, 3 Right Areas and one inter-hemispheric)
                                      cfg: [1×1 struct]

Now I create a binary mask for each left and right broddman area. For each ROI in the broddman area atlas(1-70), I find all voxels that are located within. This gives me a list of voxels for each broddman area. I then take each of these lists and find which are in the left hemisphere and which are in the right to get left and right broddman areas.
After these steps I am left with 140 binary masks, (size 1x8196), which equal 1 if the voxel is contained in the specific ROI and 0 if it is not. Using these masks, I extract the time course power for each ROI by taking the mean power of every voxel in the roi at each time point:
                    roiData(i,i3,i2) = mean(sourceEEG.trial(i2).pow(Masks{i},i3));

where i = roi number,

       i2 = trial number,

       i3 = time point

In order to get this data into fieldtrip format, I put ROI data into an EEGLAB structure along with 6 emg channels that were recorded at the same time and use eeglab2fieldtrip function to have a fieldtrip structure containing the broddman area sources as channels. (contained in header)

After this I perform the final step of computing dtf connectivity between each of the source rois and the emg channels with the code below:

cfg           = [];
cfg.method    = 'mtmfft';
cfg.taper     = 'dpss';
cfg.output    = 'fourier';
cfg.tapsmofrq = 2;
freq          = ft_freqanalysis(cfg, header);
cfg           = [];
cfg.method    = 'dtf';
dtf           = ft_connectivityanalysis(cfg, freq);

The values I get in DTF are extremely low, 10^-20. I am not sure why but I believe it has to do with the way I am projecting source activations onto specific ROIs. Any help or advice on how to change my pipeline is appreciated.

Michael Glassen

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