In this tutorial you will find a description of all the steps necessary to obtain sources localisation of continuous (non averaged) data using a beamformer approach in FieldTrip for a group of subjects using the MNI template.
Prerequisites
- Read the previous tutorials (MEG EEG pre-processing, MEG sensors realignment, filter, baseline correction)
- Pre process the T1 MRI data or choose a template
- Download the latest version of FieldTrip - Install it in your software folder - Take a look at the FieldTrip tutorials!
Input data
MEG signals (MEG)
FieldTrip can import the fif files coming from our MEG system and those pre processed with our in-house tools. If you acquired simultaneous MEG+EEG data, FieldTrip will import both of them.
MRI
If you have the individual anatomy of each of your subjects (T1 MRI), you should process it with FieldTrip as described below.
If you don't have the individual anatomy, then you have to choose a template in FieldTrip.
Needed software
For MEG fif files pre processed outside FieldTrip: You will need two in-house scripts to read the marker files we add to the MEG fif files (put them in your matlab path):
A short script to pre process the MRI in FieldTrip: pre_process_mri_for_ft.m
A simple script to visualize beamformer results: visuBF.m
D.I.C.S. analysis with FieldTrip
Overview of the process:
Suggested data organisation
The mat files generated by FieldTrip can be stored together with the pre-processed data. You may want to create a specific sub folder "FT" to store them in each subject.
MRI processing and grid normalisation to the MNI space
This step should be done only one time. The results (aligned and segmented MRI, head model and grid) will be loaded when needed.
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mri=ft_read_mri(dicom_file_name);
%% Construire la grille du beamformer en d?formant le MNI sur le sujet
templatedir = 'fieldtrip-20160105/template/sourcemodel';
template = load(fullfile(templatedir, 'standard_sourcemodel3d8mm'));
%% Realign the MRI to the MEG coordinate
cfg=[];
cfg.method = 'interactive' ;
cfg.coordsys = 'neuromag' ;
mri_aligned = ft_volumerealign(cfg,mri) ;
save('mri_aligned.mat', 'mri_aligned');
%% Segmentation
cfg = [];
cfg.output = 'brain';
segmentedmri = ft_volumesegment(cfg, mri_aligned);
save('segmentedmri.mat', 'segmentedmri') ;
% construct volume conductor model (i.e. head model) for each subject
cfg = [];
cfg.method = 'singleshell';
vol = ft_prepare_headmodel(cfg, seg);
vol = ft_convert_units(vol, 'cm');
save('vol.mat', 'vol') ;
% create the subject specific grid, using the template grid that has just been created
cfg = [];
cfg.grid.warpmni = 'yes';
cfg.grid.template = template.grid;
cfg.grid.nonlinear = 'yes'; % use non-linear normalization
cfg.mri = mri_aligned ;
warped_grid = ft_prepare_sourcemodel(cfg);
save('warped_grid.m', 'warped_grid')
% make a figure of the single subject headmodel, and grid positions
figure;
ft_plot_vol(vol, 'edgecolor', 'none'); alpha 0.5;
ft_plot_mesh(grid.pos(grid.inside,:)); |
D.I.C.S. Analysis
The spatial filters are computed here. We then contrast the two conditions before saving the results. One very important step necessary to allow an easy visualisation of the results is to change the localisation of the grid points back to the original templates ones.
back to the original templates ones.
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%% Load the MRI data for visualisation purpose load('mri_aligned.mat') ; %% MRI data needed for vizualisation load('segmentedmri.mat') ; index = 1 ; conf = [] ; %% marker of interest markers = 'Move_OK' ; %% output label conf.label = 'WIKI_' ; %% Main Analysis for i=1:number_of_subjects %% Enter subject dir cd(list_sujets{i}{1}{:}) %% Load the head model created during the MRI pre processing load vol ; %% load head model located in the subject directory and obtained from the T1 MRI of the subject %% Load the warped MNI grid load warped_grid ; for exam=1:2 %% Is the subject/session really here? %% Enter subject dir if cd~isempty([list_sujets{i}{1+exam}{:}]) %% Load head model and source model enter exam subdirectory load hdm load sourcemodel for exam=1:2cd(list_sujets{i}{1+exam}{:}) %% Datasets to be scanned for the marker of interest datasets = {'B_cardiocor_blinkcor_run01_pre_tsss.fif' 'B_cardiocor_blinkcor_run02_pre_tsss.fif' 'B_cardiocor_blinkcor_run03_pre_tsss.fif'} ; %% if ~isempty([list_sujets{i}{1+exam}{:}]) %% enter exam subdirectory cd(list_sujets{i}{1+exam}{:}) conf.datasets = {'B_cardiocor_blinkcor_run01_pre_tsss.fif' 'B_cardiocor_blinkcor_run02_pre_tsss.fif' 'B_cardiocor_blinkcor_run03_pre_tsss.fif'} ; conf.tbm = 2 ; Read two runs, define trials around LEFT_MVT_CLEAN marker (-2s to 2s around the marker), read also BIO005 which is an EMG. [ trials ] = trial_definition_ft( datasets, markers, 2.0, 2.0, 'BIO005' ) ; % Select time window of interest cfg = []; cfg.toilim = active_time_window ; data_timewindow_active = ft_redefinetrial(cfg,trials); % Select time window of control cfg = []; cfg.toilim = reference_time_window ; data_timewindow_ref = ft_redefinetrial(cfg,trials); % Concatenate for common spatial filter computation data_all = ft_appenddata([], data_timewindow_active, data_timewindow_ref); % Trials with full window cfg = []; cfg.toilim = overall_time_window ; full_data_all = ft_redefinetrial(cfg,trials); conf.tam = 2 ; % Create leadfield grid cfg conf.active = [-0.5 0.5]; = conf.baseline = [-1.5 -1.0];[]; cfg.grid = warped_grid ; cfg.vol conf.hdm = hdm ; = hdm ; cfg.channel conf.sourcemodel = sourcemodelchannelsofinterest ; cfg.grad conf.template = template powcsd_active.grad; cfg.vol = vol ; cfg.dics.reducerank = 2; % default for MEG is 2, for %%EEG Readis and Prepare Data3 cfg.normalize = lead_field_depth_normalization ; %% Depth normalization [ prepared_data [grid] = ReadDataPrepareTrialsft_resampleprepare_AnalyseMNIleadfield( conf cfg); ; conf.data_timewindow_active = prepared_data.data_timewindow_activefor freq_index=1:number_of_fois foi = fois{freq_index}{1} ; conf.data_timewindow_ref wof = prepared_data.data_timewindow_reffois{freq_index}{2} ; %% Compute DICS filters for the concatenated data cfg conf.data_all = prepared_data.data_all ; = []; cfg.channel = channelsofinterest ; cfg.method for freq_index=1:number_of_fois 'dics'; cfg.frequency = freqofinterest ; cfg.grid = grid; cfg.headmodel = vol; cfg.senstype conf.foi = fois{freq_index}{1} ; = 'MEG'; cfg.dics.keepfilter = 'yes'; % We wish to use the calculated filter later conf.wof = fois{freq_index}{2} ; conf.output = [conf.label '_' conf.markers '_PRE_GRAD_' fois{freq_index}{3}] ; on cfg.dics.projectnoise = 'yes'; cfg.dics.lambda = beamformer_lambda_normalization; source_all = ft_sourceanalysis(cfg, powcsd_all); %% Apply computed filters to each active and reference windows cfg = conf.coi = {'MEG*2', 'MEG*3'} ;[]; cfg.channel = channelsofinterest ; cfg.method = [ sources sources_diff ] = AnalyseMNI( conf ) ; 'dics'; cfg.frequency = freqofinterest ; cfg.grid = grid; cfg.grid.filter = source_all.avg.filter; cfg.dics.projectnoise = 'yes'; cfg.headmodel = vol; cfg.dics.lambda %% %% %% MVT_OK MAG= beamformer_lambda_normalization ; cfg.senstype ='MEG' ; %% Source analysis active source_active = ft_sourceanalysis(cfg, powcsd_active); conf.output = [conf.label '_' conf.markers '_PRE_MAG_' fois{freq_index}{3}] ; conf.coi = {'MEG*1'} ; %% Source analysis reference source_reference = ft_sourceanalysis(cfg, powcsd_ref); %% Compute differences (power) between active and reference source_diff = source_active ; source_diff.avg.pow = (source_active.avg.pow - source_reference.avg.pow) ./ (source_active.avg.pow + source_reference.avg.pow); [sources sources_diff ] = AnalyseMNI( conf ) ; conf.output = [conf.label '_' conf.markers '_PRE_GRAD_' fois{freq_index}{3}] ; end cd conf..coi = {'MEG*2', 'MEG*3'} ; end end %% for exam cd .. fclose('all') ; end %% for subject %% Read two runs, define trials around LEFT_MVT_CLEAN marker (-2s to 2s around the marker), read also BIO005 which is an EMG. [ trials ] = trial_definition_ft( {'cardiocor_blinkcor_run03_tsss.fif' 'cardiocor_blinkcor_run04_tsss.fif'}, 'LEFT_MVT_CLEAN', 2.0, 2.0, 'BIO005' ) ; % Create leadfield grid cfg[ sources sources_diff ] = AnalyseMNI( conf ) ; %% %% %% MVT_OK MAG conf.output = [conf.label '_' conf.markers '_PRE_MAG_' fois{freq_index}{3}] ; cfg.grid conf.coi = warped_grid ; cfg.vol{'MEG*1'} ; = hdm ; cfg.channel [sources sources_diff ] = AnalyseMNI( conf ) ; = channelsofinterest ; cfg.grad end = powcsd_active.grad; cfg.vol cd .. = volend ; cfg.dics.reducerank = 2; %end default%% for MEG isexam 2, for EEG is 3cd cfg..normalize fclose('all') ; =end lead_field_depth_normalization ; %% Depth normalization [grid] = ft_prepare_leadfield(cfg);%% for subject %% Compute DICS filters for the concatenated data cfg = []; cfg.channel = channelsofinterest ; cfg.method = 'dics'; cfg.frequency = freqofinterest ; cfg.grid = grid; cfg.headmodel = vol; cfg.senstype = 'MEG'; cfg.dics.keepfilter = 'yes'; % We wish to use the calculated filter later on cfg.dics.projectnoise = 'yes'; cfg.dics.lambda = beamformer_lambda_normalization; source_all = ft_sourceanalysis(cfg, powcsd_all); %% Apply computed filters to each active and reference windows cfg = []; cfg.channel = channelsofinterest ; cfg.method = 'dics'; cfg.frequency = freqofinterest ; cfg.grid = grid; cfg.grid.filter = source_all.avg.filter; cfg.dics.projectnoise = 'yes'; cfg.headmodel = vol; cfg.dics.lambda = beamformer_lambda_normalization ; cfg.senstype ='MEG' ; %% Source analysis active source_active = ft_sourceanalysis(cfg, powcsd_active); %% Source analysis reference source_reference = ft_sourceanalysis(cfg, powcsd_ref); %% Compute differences (power) between active and reference source_diff = source_active ; source_diff.avg.pow = (source_active.avg.pow - source_reference.avg.pow) ./ (source_active.avg.pow + source_reference.avg.pow); %% Display the results visuBF( source_diff, 'title' ) %% MRI reslicing mri_resliced = ft_volumereslice([], mri_aligned); %% Display results superimposed on MRI cfg = []; cfg.parameter = 'avg.pow'; source_active_int = ft_sourceinterpolate(cfg, source_active, mri_resliced); source_reference_int = ft_sourceinterpolate(cfg, source_reference, mri_resliced); source_diff_int = source_active_int; source_diff_int.pow = (source_active_int.pow - source_reference_int.pow) ./ (source_active_int.pow + source_reference_int.pow); %% Change grid locations for display in MNI sources_exp.pos = template.sourcemodel.pos; sources_exp.dim = template.sourcemodel.dim; % sources_bsl.pos = template.sourcemodel.pos; sources_bsl.dim = template.sourcemodel.dim; %% Save results save([conf.output '_exp.mat'], 'sources_exp') save([conf.output '_bsl.mat'], 'sources_bsl') |
