[FieldTrip] EEG source reconstruction using DICS method

["Jens Klinzing, Uni Tübingen"]

Dear Elena,

Question 1:
I assume the reason it takes so long is that you are using a FEM-based 
headmodel. For FEM, ft_sourceanalysis computes a huge transfer matrix 
on-the-fly (by calling prepare_headmodel - ft_prepare_vol_sens - 
sb_transfer) every time you do a source reconstruction. For more 
information see http://bugzilla.fieldtriptoolbox.org/show_bug.cgi?id=1967 .

You can circumvent the issue by computing the transfer matrix beforehand 
and changing the code in a way that it doesnt compute a new transfer 
matrix if you have already provided one.

Alternatively you could use a BEM headmodel (dipoli if possible since 
bemcp seems to have issues 
http://bugzilla.fieldtriptoolbox.org/show_bug.cgi?id=2817).

Question 2:
Did you check your segmentation, the headmodel and the coregistration of 
electrodes/head every step of the way? If you fill all inside voxels 
with 1s and plot that, do you see an obvious shift/rotation of the 
"activity" in relation to the MRI?

Best,
Jens
> Elena Krugliakova <mailto:krugliakova.es at gmail.com>
> Montag, 9. Januar 2017 12:48
> Dear Fieldtrip community,
>
> I have a question regarding source reconstruction using the 'dics' 
> method applied to EEG data.
> I have two problems: first, even with 32GB of RAM it takes 9 hours to 
> call one ft_sourceanalysis. Maybe, there is a way to optimise the 
> procedure somehow?
>
> Second, at the end of analysis I obtain a very strange figure, on 
> which I see activity localised outside the mri scan. Mistake on which 
> step of analysis might cause this problem?
>
> As a template I used http://www.fieldtriptoolbox.org/tutorial/beamformer
> Please, find my script below.
>
> Thank you in advance!
>
> Kind Regards,
> Elena
>
>
> Script:
>
> % freqanalysis
> cfg = [];
> cfg.toilim  = [-0.5 -0.1]; % prestimulus
> Pre         = ft_redefinetrial(cfg, MyData);
> cfg = [];
> cfg.toilim  = [0.9 1.3]; % poststimulus
> Post        = ft_redefinetrial(cfg,  MyData);
> cfg = [];
> dataAll     = ft_appenddata([], Pre, Post);
>
> cfg = [];
> cfg.method       = 'mtmfft';
> cfg.output       = 'powandcsd'
> cfg.keeptrials   = 'no';
> cfg.taper        = 'dpss';
> cfg.foi          = 35;
> cfg.tapsmofrq    = 4;
>
> freq_Pre         = ft_freqanalysis(cfg,  Pre);
> freq_Post        = ft_freqanalysis(cfg,  Post);
> freq_PrePost     = ft_freqanalysis(cfg, dataAll);
>
> %% headmodel preparation --- with standard brain
> mri     = ft_read_mri('Subject01.mri');
> cfg = [];
> cfg.dim = mri.dim;
> mri     = ft_volumereslice(cfg,mri);
>
> cfg = [];
> cfg.output          = {'gray','white','csf','skull','scalp'}
> segmentedmri        = ft_volumesegment(cfg, mri);
>
> cfg = [];
> cfg.shift           = 0.3;
> cfg.method        = 'hexahedral';
> cfg.tissue          = {'gray','white','csf','skull','scalp'}
> cfg.numvertices     = [800, 800, 800, 400, 200];
> cfg.unit                = segmentedmri.unit
> bndFEM              = ft_prepare_mesh(cfg,segmentedmri);
>
> cfg = [];
> cfg.method          ='simbio';
> cfg.conductivity    = [0.33 0.14 1.79 0.01 0.43];
> vol_simbio_lowresol = ft_prepare_headmodel(cfg, bndFEM);
>
> %% loading aligned electrodes
> load elec_aligned    % 109 EEG electrodes
>
> %% leadfield preparation
> cfg = [];
> cfg.elec                = elec_aligned;
> cfg.vol                 = vol_simbio_lowresol;
> cfg.channel             = 'all';
> cfg.reducerank          = 3;     % 3 for eeg
> cfg.grid.unit           = 'mm';
> cfg.grid.resolution     = 10;
> leadfield_FEM_lowresol  = ft_prepare_leadfield(cfg);
>
> %% sourceanalysis
> cfg = [];
> cfg.frequency           = 35;
> cfg.vol                 = vol_simbio_lowresol;
> cfg.grid                = leadfield_FEM_lowresol
> cfg.projectnoise        = 'yes';
> cfg.method              = 'dics';
> cfg.dics.projectnoise   = 'yes';
> cfg.dics.lambda         = '5%';
> cfg.dics.keepfilter     = 'yes';
> cfg.dics.realfilter     = 'yes';
> sourceAll               = ft_sourceanalysis(cfg, freq_PrePost);
> cfg.grid.filter         = sourceAll.avg.filter;
>
> sourcePre_con           = ft_sourceanalysis(cfg, freq_Pre);
>
> sourcePost_con          = ft_sourceanalysis(cfg, freq_Post);
>
> sourceDiff              = sourcePost_con;
> sourceDiff.avg.pow      = (sourcePost_con.avg.pow - 
> sourcePre_con.avg.pow) ./ sourcePre_con.avg.pow;
>
> %% sourceplot
> cfg = [];
> cfg.downsample = 2;
> cfg.parameter  = 'pow';
> sourceDiffInt  = ft_sourceinterpolate(cfg, sourceDiff, mri);
>
> cfg = [];
> sourceDiffIntNorm = ft_volumenormalise(cfg, sourceDiffInt);
>
> cfg = [];
> cfg.method        = 'glassbrain';
> cfg.funparameter  = 'pow';
> cfg.maskparameter = cfg.funparameter;
> ft_sourceplot(cfg, sourceDiffIntNorm);
>
>
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