In this tutorial, you will find a description of all the steps necessary to obtain sources localisation of continuous (non averaged) data using a D.I.C.S beamformer approach in FieldTrip for one subject.

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 F ieldTrip - 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

F ieldTrip

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

This step has to be done only one time. Provide the file name of you T1 MRI exam (can be nifty format for instance). The results will loaded as needed in the coming analysis.

MRI processing

mri=ft_read_mri(file_name);
%% 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') ;

General configuration

Here we load all the necessary stuff and we set the main parameters of the analysis:

time windows for active state and baseline
frequency of interest
beamformer configuration
MEG channels to be used in the analysis

Parameters

%% 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 MRI data for visualisation purpose
load('mri_aligned.mat') ; %% MRI data needed for vizualisation
load('segmentedmri.mat') ;

overall_time_window = [-2 2] ; %% if you modify this modify the trials def above ! Is this used below ???

%% Define time window for active state and baseline
active_time_window = [0.2 0.6] ;
reference_time_window = [-1.4 -1.0] ;

%% Basic configuration of the beamformer
beamformer_lambda_normalization = '10%' ;
lead_field_depth_normalization = 'column' ;

%% Frequency of interest and associated width (we will examine here the 16 Hz to 28 Hz band)
freqofinterest = 22 ;
freqhalfwin = 6 ;

%% Select the channel of interest
channelsofinterest = {'MEG*2', 'MEG*3'}; %% Gradiometers on an Elekta system
%% channelsofinterest = {'MEG*1'} ; %% Magnetometer on an Elekta system

Data preparation

Different sets of trials are prepared for the coming analysis

Data preparation

% 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 will full window
cfg = [];
cfg.toilim = overall_time_window ;
full_data_all = ft_redefinetrial(cfg,trials);

Time-Frequency analysis

Time-Frequency analysis

cfg              = [];
cfg.output       = 'pow';
cfg.channel      = 'MEG';
cfg.method       = 'mtmconvol';
cfg.taper        = 'hanning';
cfg.foi          = 7:2:40;                         % analysis 2 to 30 Hz in steps of 2 Hz 
cfg.t_ftimwin    = ones(length(cfg.foi),1).*0.5;   % length of time window = 0.5 sec
cfg.toi          = -2.0:0.05:2.0;                  % time window "slides" from -2.0 to 2.0 sec in steps of 0.05 sec (50 ms)
cfg.trials       = 'all';

TFRhann = ft_freqanalysis(cfg, full_data_all);
 
%% Visualization
cfg = [];
cfg.xlim = [0.1 0.2];                
cfg.ylim = [20 20];                  
cfg.zlim = [-1e-28 1e-28];           
cfg.baseline     = [-0.2 -0.0]; 
cfg.baselinetype = 'absolute';
cfg.layout       = 'neuromag306mag.lay';

figure; ft_topoplotTFR(cfg,TFRhann);

% for the multiple plots also:
cfg = [];
cfg.ylim = [20 20];                  
cfg.baseline     = [-0.2 -0.0]; 
cfg.baselinetype = 'absolute';
cfg.layout       = 'neuromag306mag.lay';
cfg.xlim = [-0.4:0.1:0.4];
cfg.comment = 'xlim';
cfg.commentpos = 'title';
figure; ft_topoplotTFR(cfg,TFRhann);

cfg = [];
cfg.baseline     = [-1.0 -0.8]; 
cfg.baselinetype = 'absolute'; 
%% cfg.zlim         = [-1e-27 1e-27];           
cfg.showlabels   = 'yes';   
cfg.layout       = 'neuromag306mag.lay';
figure; ft_multiplotTFR(cfg, TFRhann);

CSD Computation

CSD computation

cfg = [];
cfg.channel      = 'meg' ;
cfg.pad          = 'nextpow2';
cfg.method       = 'mtmfft';
cfg.output       = 'powandcsd';
cfg.keeptrials   = 'no';
cfg.foi          = freqofinterest ;
cfg.tapsmofrq    = freqhalfwin ;

%% Compute cross-sepctral density matrices
powcsd_all = ft_freqanalysis(cfg, data_all) ;
powcsd_active = ft_freqanalysis(cfg, data_timewindow_active) ;
powcsd_ref = ft_freqanalysis(cfg, data_timewindow_ref) ;

Forward operator

We compute the lead field based on the warped MNI grid.

Forward operator

% Create leadfield grid
cfg                 = [];
cfg.channel         = channelsofinterest ;
cfg.grad            = powcsd_active.grad;
cfg.vol             = vol ;
cfg.dics.reducerank = 2; % default for MEG is 2, for EEG is 3
cfg.grid.resolution = 1.0;   % use a 3-D grid with a 0.5 cm resolution
cfg.grid.unit       = 'cm';
cfg.grid.tight      = 'yes';
cfg.normalize       =  lead_field_depth_normalization ; %% Depth normalization
[grid] = ft_prepare_leadfield(cfg);

D.I.C.S. Analysis

The spatial filters are computed here. We then contrast the two conditions before saving the results.

%% 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);

%% Display the results
visuBF( source_diff_int, 'title' )

CENIR

Single trials - Sources analysis