[FieldTrip] how to map grid coordinates to destrieux atlas or AAL

[Schoffelen, J.M. (Jan Mathijs)]

Hi Evan,

I guess your question pertains to whether there’s an easy way to map  coordinates from one known space into another known space?

Yes, this is relatively straightforward if 1) this mapping is a linear one (affine transformation), and 2) if you know the mapping matrix.

So the solution boils down to finding out how the coordinate system in which the electrodes etc are expressed relates to the coordinate system of the atlas.

Once you have this, you can use ft_warp_apply (or ft_transform_geometry) to toggle back and forth between the different coordinate systems.

Best wishes,
Jan-Mathijs


On 26 Aug 2022, at 03:51, Evan Hutcheon via fieldtrip <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>> wrote:


 Hi everyone,

I have added my code below for source reconstruction on a grid based on the standard_bem headmodel, and am wondering if there is an easy way to map my grid coordinates to the destrieux atlas, or another atlas (like AAL).



%% create a head model || vol is standard bem from FT
        load('/rcg/bcni/users/Evan/fieldtrip-20190912/template/headmodel/standard_bem.mat')
        headmodel        =  vol;
        headmodel = ft_convert_units(headmodel, 'm');


        cfg=[];
        cfg.resolution = .015;  %headmodel resolution(was 0.008)
        cfg.headmodel = headmodel;
        source_model = ft_prepare_sourcemodel(cfg);

        %% align electrodes to head model
        elec = ft_read_sens('standard_1020.elc');
        elec = ft_convert_units(elec, 'm');

        scalp_index = 1; %scalp is 1

        cfg = [];
        cfg.method = 'project'; % onto scalp surface
        cfg.headshape = headmodel.bnd(scalp_index); % scalp surface
        elec_realigned = ft_electroderealign(cfg,elec);

   % end

    %% align electrodes
    % for subject
    [v, ix, ixx] = intersect(elec_realigned.label, interp.label);
    elecICA = elec_realigned;
    elecICA.chanpos = elecICA.chanpos(ix,:);
    elecICA.chantype = elecICA.chantype(ix,:);
    elecICA.chanunit = elecICA.chanunit(ix,:);
    elecICA.elecpos = elecICA.elecpos(ix,:);
    elecICA.label = elecICA.label(ix);
    elecICA.tra = elecICA.tra(ix, ix); %cant find

    %% leadfield

    cfg = [];
    cfg.sourcemodel = source_model;    %% where are the sources?
    cfg.headmodel   = headmodel;      %% how do currents spread?
    cfg.elec        = elecICA;% how do sources and sensors connect?
    cfg.normalize   = 'yes'; %was no
    leadfield = ft_prepare_leadfield(cfg,interp);



    %% source estimation via LCMV
    cfg = [];
    cfg.trials              = 'all';
    cfg.channel             = 'eeg';
    cfg.covariance          = 'yes';
    cfg.covariancewindow    = 'all';
    cfg.keeptrials          = 'yes';
    cfg.removemean          = 'yes';
    timelock                = ft_timelockanalysis(cfg, interp);

    %LCMV BEAMFORMER to create spatial filter
    cfg = [];
    cfg.method              = 'lcmv';
    cfg.grid                = leadfield;
    cfg.headmodel           = headmodel;
    cfg.elec                = elecICA;
    cfg.lcmv.lambda         = '5%';
    cfg.lcmv.projectmom     = 'yes';
    cfg.lcmv.keepfilter     = 'yes';
    cfg.lcmv.keepori        = 'yes';
    cfg.lcmv.projectnoise   = 'yes';
    sfilter                 = ft_sourceanalysis(cfg, timelock); % use this for your source reconstruction

    %% Source analysis

    cfg = [];
    cfg.trials              = 'all';
    cfg.trials = any(interp.trialinfo == [5],2);%
    cfg.covariance          = 'yes';
    cfg.covariancewindow    = 'all';
    cfg.keeptrials          = 'yes';
    cfg.removemean          = 'yes';
    timelock_trl_left          = ft_timelockanalysis(cfg, interp);

       cfg = [];
    cfg.trials              = 'all';
    cfg.trials = any(interp.trialinfo == [6],2);%
    cfg.covariance          = 'yes';
    cfg.covariancewindow    = 'all';
    cfg.keeptrials          = 'yes';
    cfg.removemean          = 'yes';
    timelock_trl_right         = ft_timelockanalysis(cfg, interp);


    % no need at this point to generate and save the time series
    cfg = [];
    cfg.method              = 'lcmv';
    cfg.grid                = leadfield;
    cfg.headmodel           = headmodel;
    cfg.elec                = elecICA;
    cfg.lcmv.lambda         = '5%';
    cfg.lcmv.projectmom     = 'yes';

    cfg.rawtrial            = 'yes';
    cfg.grid.filter         = sfilter.avg.filter; %sfilter taken from before
    cfg.lcmv.projectnoise   = 'yes';  % noise estimation from the single value decomposition
    cfg.lcmv.keepmom        = 'yes';  % saves the time series
    cfg.lcmv.fixedori       = 'yes';
    cfg.keeptrials          = 'yes'; %'no' or 'yes
    cfg.keepleadfield       = 'yes';
    cfg.keepfilter          = 'no';
    source_left                  = ft_sourceanalysis(cfg,timelock_trl_left);
    source_right                 = ft_sourceanalysis(cfg,timelock_trl_right)


Cheers,
Evan

Evan Hutcheon
PhD Candidate|Dr. Doesburg laboratory
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