[FieldTrip] Warped Source plots and High Source Power Values

[Alex Williams]

Hello there FieldTrip Community,


  I'm Alex Williams, a neuroengineering graduate student at Florida
Institute of Technology. I'm relatively new to source reconstruction
methods and was wondering if there may be any pointers or advice that may
help guide me through the tutorials and documentation. I've been trying to
resolve an issue with the beamformer for some time. Currently, I'm using an
LCMV beamformer to source fit participant MEG data and compare those
results to source results that were obtained in SPM12.  Though I'm trying
to create a pipeline for analyzing this data set, the results I've obtained
seem to have discrepancies in source variance and virtual channel signal
alongside having warped sourceplots when plotting source variance.  The
meeg data, D, I'm using has been converted from SPM to fieldtrip using the
function. spm2fieldtrip(). The converted data itself has been defined, and
has undergone pre-processing and time-locking in SPM.

Steps I used:

1. The converted meeg data, D, I'm using has been converted from SPM to
fieldtrip using the function. spm2fieldtrip(). The converted data itself
has been defined, and has undergone pre-processing and epoching in SPM.
Since I wasn't able to find the covariance matrix within the D structure I
was using, I applied a time-lock analysis to the D structure I had and used
only the covariance matrix from those results as the cfg.cov parameter
within the D matrix pre-time lock.

2. ft_read_mri() used a .nifti MRI file and an empty configuration.
Fiducials were taken and used in the configuration to realign the MRI using
ft_volumerealign() with cfg.coordsys='neuromag'. After, segmentation was
done using ft_volumesegment(), again with an empty configuration.

3. Headmodel computed using following parameters:
cfg = [];
cfg.method = 'singleshell';
cfg.siunits = 'yes';
cfg.feedback = 'yes';
headmodel = ft_prepare_headmodel(cfg, seg);
headmodel = ft_convert_units(headmodel, 'mm');

3. Gradiometer array was defined with ft_read_sens using the path of the
meeg file. These were the parameters of the source model.

cfg = [];
cfg.grad = grad;
cfg.headmodel = headmodel;
cfg.resolution = 1;
cfg.inwardshift = 0;
sourcemodel = ft_prepare_sourcemodel(cfg);

Plotting leads to this segmented mri figure (segmented mri) and
headmodel-sensor model (headmodel, sourcemodel mesh and sensor array)


[image: image.png]

[image: Screenshot 2024-05-12 at 10.08.53 PM.png]
4. After the forward model was run with prepare_leadmatrix () with
following parameters:
cfg = [];
cfg.grad = grad;
cfg.headmodel = headmodel;
cfg.sourcemodel = sourcemodel;
cfg.channel = {'MEG'};
cfg.singleshell.batchsize = 2000;
lf = ft_prepare_leadfield(cfg,D);
5. And then the source model was run using an LCMV beamformer:
if isfield(D,'cov')
[~, sr, ~]=svd(D.cov);
clif=-diff(log10(diag(sr)));
kappa=find(clif==max(clif));
end
cfg = [];
cfg.method = 'lcmv';
cfg.headmodel = headmodel; % volume conduction model (headmodel)
cfg.sourcemodel = lf; % leadfield
cfg.lcmv.keepfilter = 'yes';
cfg.grad=D.grad;
cfg.keepleadfield = 'yes';
cfg.lcmv.fixedori = 'yes'; % project on axis of most variance using SVD
cfg.lcmv.kappa.      =kappa;
Which returns a source structure that seems to produce warped source
variance plots and blown-up source variance values after iusing source
interpolation using:                 ft_sourceinterpolate(cfg, source,
segmentedmri):

[image: Screenshot 2024-05-12 at 10.37.15 PM.png]

[image: Screenshot 2024-05-12 at 10.49.16 PM.png]
I'm currently trying different parameters within the beamformer including
using different weight normalization parameters to compensate for possible
depth bias alongside using different lambda values. However, based on the
final source plots, I wasn't too sure if there may have also been an issue
with the coregistration of the mri or perhaps with the final source model.

-Alex Williams
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