[FieldTrip] Source localization of MEG resting state data

최미경 cmk0803 at hbf.re.kr
Thu Jun 11 03:21:38 CEST 2015


Hi, Tzvetan.

Thank you for your first response.

The weird output that you asked in first response is first attached figure
1.
I thought It was weird because the sensor level output is so different form
the source level output.

I revised the script regarding your first advise and following tutorial.
The output is second figure.
(
http://www.fieldtriptoolbox.org/example/read_neuromag_mri_and_create_single-subject_grids_in_individual_head_space_that_are_all_aligned_in_mni_space
)
It is better than first output. However,* the sensor level output
correspond with the source level output, exactly.*

Is it anything that need to revise?
If the data is plotted at the source and sensor level, What the difference
between two plots can emerge in general?

I really appreciate for your help in advance.

Best regards,
MK CHOE

P.S. The revised script is as follows.

%% Calculating the cross spectral density matrix
cfg             = [];
cfg.output      = 'powandcsd';
cfg.method      = 'mtmfft';
cfg.foilim      = [10 10]; % alpha [8 12]
cfg.tapsmofrq   = 2;
cfg.channel     = {'MEG'};
cfg.keeptrials  = 'yes';
fft_rest = ft_freqanalysis(cfg, data_clean);

%% Computing the leadfield matrices
cfg             = [];
cfg.grad        = fft_rest.grad;
cfg.vol         = hdm;
cfg.grid        = grid;
cfg.keeptrials  = 'yes';
cfg.normalize   = 'yes';
cfg.channel     = {'MEG'};
rest_grid  = ft_prepare_leadfield(cfg);

%% Source analysis
cfg             = [];
cfg.frequency   = 'all';
cfg.grad        = fft_rest.grad;
cfg.method      = 'dics';
cfg.grid        = rest_grid;
cfg.vol         = hdm;
cfg.keeptrials  = 'yes';
cfg.keepfilter  = 'yes';
cfg.keepcsd     = 'yes';
cfg.feedback    = 'textbar';
cfg.dics.lambda = 0.1
cfg.dics.projectnoise = 'yes';
rest_source = ft_sourceanalysis(cfg, fft_rest);

%% sourcedescriptive
cfg             = [];
rest_source.dim      = rest_grid.dim;
descri_source = ft_sourcedescriptives(cfg, rest_source);
descri_source.pos = t1grid.pos;

%% sourceinterpolating_nai
cfg                = [];
cfg.parameter      = 'nai';
cfg.voxelcoord     = 'no';
cfg.interpmethod   = 'linear';
sdint      = ft_sourceinterpolate(cfg, descri_source, norm_mri);

%% plot fft results
cfgp = [];
cfgp.layout       = 'neuromag306mag_rev.lay';
cfgp.interactive='yes';
colorbar;
ft_topoplotER(cfgp, fft_rest);

%% source plot _nai
cfg              = [];
cfg.method       = 'slice';
cfg.funparameter = 'nai';
cfg.surfdownsample = 2;
cfg.projmethod     = 'project';
cfg.funcolormap    = 'jet';
ft_sourceplot(cfg, sdint);

-- 
Mi Kyung CHOE
Department of Brain & Cognitive Sciences, Seoul National University
Human Brain Function Laboratory
E-mail : cmk0803 at hbf.re.kr, cmk0803 at meg.re.kr
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