[FieldTrip] Head model for EEG source localization - Possible bug in BEMCP?
Debora Desideri
deb.desideri at gmail.com
Sat Mar 12 15:06:38 CET 2016
Dear all,
I am working on EEG source localization of a very simple fist clenching
task (right hand, 10 seconds of contraction alternated to 10 seconds of
relaxation to be used as contrast condition) using DICS beamformer
(frequency 10 Hz). I have the individial MRI and EEG electrode positions of
my participants, so I wrote a script to create the individual forward model
following the tutorials on the fieldtrip webpage and some useful
discussions on the mailing list. Below I report the full code used for the
forward model and the code used for the source analysis. Attached, you
find the results of the source localization and
some
figures of different steps in the computation of the forward model.
According to my task, I expect to identify a desynchronization on the left
motor cortex, but what I am getting are very weird results(see attachment
s)
. I tryed to use the standard MRI in template\headmodel\standard_mri, and
I get similar results.
After a full day of self-debugging, I managed to get the result I expect
(attached) using the method "concentricspheres" instead of "bemcp" in the
computation of the head model, both with the MRI of my participants and
with the standard MRI.
I assume there must be something wrong with "bemcp", also becasue when I
use the standard bem headmodel (template\headmodel\standard_bem) created
with the method "dipoli", everything looks fine again.
Did anybody experienced a similar issue before? Is there anything wrong I
am doing in the pipeline?
Thanks a lot in advace for your support!
Best,
Debora
Fieldtrip version: 20150828
Matlab version: R2015a (64 bit)
OS: Windows 8 (64 bit)
%%%% --------------------- Code for Forward Model ---------------- %%%
%% Read MRI data (DICOM)
mri = ft_read_mri(experiment.mri); % experiment.mri contains the path to
individual MRIs
%% Convert anatomical MRI to MNI coordinate system (= RAS)
cfg = [];
cfg.method = 'interactive';
cfg.coordsys = 'spm'; % MNI coordinate system (=RAS)
cfg.viewmode = 'ortho';
mri_spm = ft_volumerealign(cfg,mri);
%% Re-slice the MRI volume so that all the 3 dimensions have the same
number of voxels
cfg = [];
cfg.resolution = 1; % 1 mm thick slices
cfg.dim = [256 256 256];
mri_spm = ft_volumereslice(cfg,mri_spm);
%% Visualize MRI data to check that everything is ok
cfg = [];
cfg.method = 'ortho';
ft_sourceplot(cfg,mri_spm)
%% Segment the anatomical data
cfg = [];
cfg.output = {'brain' 'skull' 'scalp'};
cfg.brainsmooth = 2;
cfg.brainthreshold = 0.25;
cfg.scalpthreshold = 0.025;
mri_seg = ft_volumesegment(cfg, mri_spm);
%% Check segmentation brain
cfg = [];
cfg.method = 'ortho';
cfg.funparameter = 'brain';
ft_sourceplot(cfg,mri_seg)
%% Check segmentation skull
cfg = [];
cfg.method = 'ortho';
cfg.funparameter = 'skull';
ft_sourceplot(cfg,mri_seg)
%% Check segmentation scalp
cfg = [];
cfg.method = 'ortho';
cfg.funparameter = 'scalp';
ft_sourceplot(cfg,mri_seg)
%% Prepare mesh: creates surfaces at the boarders of the different
tissue-types
cfg = [];
cfg.method = 'projectmesh';
cfg.tissue = {'brain' 'skull' 'scalp'};
cfg.numvertices = [6000 4000 2000];
bnd = ft_prepare_mesh(cfg, mri_seg);
%% Visualize surfaces
figure
ft_plot_mesh(bnd(1),'facecolor','none') % brain
figure
ft_plot_mesh(bnd(2),'facecolor','none') % skull
figure
ft_plot_mesh(bnd(3),'facecolor','none') % scalp
%% Visualize full mesh
figure
ft_plot_mesh(bnd(3), 'facecolor',[0.2 0.2 0.2], 'facealpha', 0.3,
'edgecolor', [1 1 1], 'edgealpha', 0.05);
hold on;
ft_plot_mesh(bnd(2),'edgecolor','none','facealpha',0.4);
hold on;
ft_plot_mesh(bnd(1),'edgecolor','none','facecolor',[0.4 0.6 0.4]);
%% Prepare head model (volume conduction model)
cfg = [];
cfg.method = 'bemcp';
headmodel = ft_prepare_headmodel(cfg,bnd);
%% % --------------------- Align the EEG electrodes!!!-------------------%%%
%% (1) Read electrode position from Localizer file
elec = ft_read_sens(experiment.electrodes); % experiment.electrodes
contains the path to the individual electrode positions
%% (2) Check if alignment is necessary
figure
%plot scalp
ft_plot_mesh(headmodel.bnd(3),'edgecolor','none','facealpha',0.8,'facecolor',[0.6
0.6 0.8])
hold on
% plot electrodes
ft_plot_sens(elec,'style','sk')
%% (3) If the electrodes are not aligned to the head surface, interactively
align them
cfg = [];
cfg.method = 'interactive';
cfg.elec = elec;
cfg.headshape = headmodel.bnd(3); % scalp
elec_aligned = ft_electroderealign(cfg);
%% Load hdr and order elec_aligned accordign to original order of signal
acquisition
%%% This is necessary because the eeg elctrod eposition shave been acquired
%%% in a different order with respect to theorder of the signal recording
load([experiment.path '\Data'],'hdr') % hdr contains some information about
the original order of the channels and so on...
[~, original_order] = ismember(hdr.elec.label,elec_aligned.label);
elec_aligned.chanpos = elec_aligned.chanpos(original_order,:);
elec_aligned.elecpos = elec_aligned.elecpos(original_order,:);
elec_aligned.label = elec_aligned.label(original_order);
%% Check if the alignment is ok
figure
%plot scalp
ft_plot_mesh(headmodel.bnd(3),'edgecolor','none','facealpha',0.8,'facecolor',[0.6
0.6 0.8])
hold on
% plot electrodes
ft_plot_sens(elec_aligned,'style','sk')
%% -------------------------------- Source model -------------------------%%
%% Make the individual subject's grid
cfg = [];
cfg.grid.warpmni = 'yes';
cfg.resolution = 6; % resolution of the template grid in mm
cfg.grid.nonlinear = 'yes'; % use non linear normalization
cfg.mri = mri_spm; % use subject's mri in mni coordinates
cfg.grid.unit = 'mm';
sourcemodel = ft_prepare_sourcemodel(cfg);
%% Plot the single subject'e head model and grid positions
figure
hold on
ft_plot_vol(headmodel,'edgecolor','none','facealpha',0.3);
ft_plot_mesh(sourcemodel.pos(sourcemodel.inside,:));
%% Double check that source model is aligned to the segmented volume and to
the electrodes
figure
hold on
plot3(sourcemodel.pos(sourcemodel.inside,1),sourcemodel.pos(sourcemodel.inside,2),sourcemodel.pos(sourcemodel.inside,3),'.');
ft_plot_vol(headmodel,'facecolor','brain','edgecolor','none','facealpha',0.3);
ft_plot_sens(elec_aligned,'label','label','style','y*','Markersize',10);
%% ---------------------- FORWARD SOLUTION ------------------------ %%
%% Prepare leadfield
cfg = [];
cfg.elec = elec_aligned;
cfg.grid = sourcemodel;
cfg.headmodel = headmodel;
leadfield = ft_prepare_leadfield(cfg);
%% Test sourc elocalization
load([experiment.path '\DATA'], 'preexperiment_freqsource')
% preexperiment_freqsource contains the output of ft_freqanalysis
cfg = [];
cfg.elec = elec_aligned;
cfg.method = 'dics';
cfg.frequency = 10;
cfg.grid = leadfield;
cfg.headmodel = headmodel;
cfg.dics.keepfilter = 'yes';
cfg.dics.lambda = '10%';
src = ft_sourceanalysis(cfg,
preexperiment_freqsource);
filter = src.avg.filter;
markers = [224 192]; % markers are some number in the trialinfo,
224 is related to fist clenching, 192 to relaxation
for mm = 1:length(markers)
cfg = [];
cfg.trials = find(preexperiment_freqsource.trialinfo ==
markers(mm));
data = ft_selectdata(cfg,preexperiment_freqsource);
%%% DICS
cfg = [];
cfg.elec = elec_aligned;
cfg.method = 'dics';
cfg.frequency = 10;
cfg.grid = leadfield;
cfg.headmodel = headmodel;
cfg.dics.projectnoise = 'yes';
cfg.dics.lambda = '10%';
cfg.grid.filter = filter;
Source{mm} = ft_sourceanalysis(cfg, data);
end
%% Interpolate results to anatomy
source = Source{1};
source1 = Source{1}; %clenching
source2 = Source{2}; % release
source.avg.cont = (source2.avg.pow./source1.avg.pow)-1;
cfg = [];
cfg.parameter = 'cont';
source = ft_sourceinterpolate(cfg, source, mri_spm);
%% Plot surface
cfg = [];
cfg.method = 'surface';
cfg.funparameter = 'cont';
cfg.funcolormap = 'jet';
ft_sourceplot(cfg, source)
--------------------------------------------------------------------
Debora Desideri, PhD Student
BNP Lab - Neurology Department
University Hospital Tübingen
Eberhard Karls University Tübingen
Hoppe-Seyler Str. 3
D-72076 Tübingen
Tel: +49 7071/29 80478
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