[FieldTrip] Results from source analysis with different fieldtrip versions differ!?

[Alexandra Steina]

Hi  Jan-Mathijs,

Thanks for the quick reply.
I've checked it for the april and october version and the 'freq' 
structures are the same.

Greetings,
Alexandra

Am 04.11.2020 um 14:09 schrieb Schoffelen, J.M. (Jan Mathijs):
> Hi Alexandra,
>
> Hmmm, that’s not good. We have recently tried to clean up the codebase that is doing various inverse calculations, which was indeed around the time at which you identified a change in figure quality. I don’t think this has to do with the leadfield computation.
>
> Just as a first step, could you check whether the ‘freq’ structures are the same in the different FieldTrip versions? Just to be sure that indeed the cause of this lies in ft_sourceanalysis or ft_inverse_dics.
>
> Best wishes,
> Jan-Mathijs
>
>
>
>> On 4 Nov 2020, at 13:38, Alexandra Steina <Alexandra.Steina at uni-duesseldorf.de> wrote:
>>
>> Hello everyone,
>>
>> I used the following pipeline to perform a source analysis on MEG-LFP data. I noticed different results when using different Fieldtrip Versions for the exact same data and analysis pipeline.
>> I read that there was a bug in the 'ft_prepare_leadfield' function in the release of June and it says that the bug was fixed, but I got some weird results when doing dics source reconstruction on the MEG data with the Fieldtrip releases from August on (I also just tried the release from yesterday). I attached some sourceplots (all from the exact same data) that are all done with a different Fieldtrip Version.
>> Does anyone know if there is still a bug in the newest fieldtrip releases or why these differences occur?
>>
>>     mri  = ft_read_mri(mri_path);
>>     mri.anatomy=double(mri.anatomy);
>>     fidu = importdata([fidu_path,'fiducial.txt']);
>>     cfg=[];
>>     cfg.coordsys='neuromag';
>>     cfg.method = 'fiducial';
>>     cfg.fiducial.nas    = fidu.data(2,:);
>>     cfg.fiducial.lpa    = fidu.data(3,:);
>>     cfg.fiducial.rpa    = fidu.data(1,:);
>>     real_mri=ft_volumerealign(cfg,mri);
>>
>>     cfg                = [];
>>     cfg.keepintermediate = 'no';
>>     cfg.write          = 'no';
>>     cfg.name          = [folder_intermediate,'spmtmp'];
>>     segmentedmri   = ft_volumesegment(cfg, real_mri);
>>
>>     cfg                = [];
>>     cfg.method = 'singleshell';
>>     hdm                = ft_prepare_headmodel(cfg,segmentedmri);
>>     hdm = ft_convert_units(hdm,'cm');
>>
>>     template = load(fullfile('fieldtrip/template/sourcemodel/standard_sourcemodel3d10mm'));
>>
>>     cfg                = [];
>>     cfg.grid.warpmni   = 'yes';
>>     cfg.grid.template  = template.sourcemodel;  % template_grid
>>     cfg.grid.nonlinear = 'yes';
>>     cfg.mri            = real_mri;
>>     cfg.grid.unit      ='cm';
>>     grid               = ft_prepare_sourcemodel(cfg);
>>
>>     cfg = [];
>>     cfg.dataset = dataset;
>>     cfg.dftfilter = 'yes';
>>     cfg.trl = trl;
>>     cfg.channel = ['MEG***2','MEG***3',Info_et.(subj).(cond).(file).bad_meg];
>>     meg = ft_preprocessing(cfg);
>>
>>
>>     cfg = [];
>>     cfg.dataset = dataset;
>>     cfg.dftfilter = 'yes';
>>     cfg.trl = trl;
>>     cfg.channel = Info_et.(subj).(cond).(file).old_lfp_labels;
>>     cfg.montage = Info_et.(subj).(cond).(file).montage;
>>     lfp = ft_preprocessing(cfg);
>>
>>    data = ft_appenddata([],meg,lfp);
>>
>>     cfg = [];
>>     cfg.length = 1;
>>     cfg.overlap = 0.5;
>>     data_seg = ft_redefinetrial(cfg,data);
>>
>>
>>     centerfreq = 12;
>>     chan = ‘LFP-right-1-2A’;
>>     bandwidth = 2;
>>
>>     cfg = [];
>>     cfg.method = 'mtmfft';
>>     cfg.output = 'powandcsd';
>>     cfg.foi = centerfreq;
>>     cfg.tapsmofrq = bandwidth;
>>     cfg.keeptrials = 'yes';
>>     freq = ft_freqanalysis(cfg,data_seg);
>>
>>     cfg=[];
>>     cfg.method='dics';
>>     cfg.dics.lambda='5%';
>>     cfg.headmodel=hdm;
>>     cfg.sourcemodel=grid;
>>     cfg.frequency = centerfreq;
>>     cfg.reducerank=2;
>>     cfg.refchan = chan;
>>     source=ft_sourceanalysis(cfg,freq);
>>     source.pos = template.sourcemodel.pos;
>>     source.dim = template.sourcemodel.dim;
>>
>>     [maxval, maxcohindx] = max(source.avg.coh);
>>     max_pos = source.pos(maxcohindx, :);
>>
>>      cfg = [];
>>      cfg.parameter = 'coh';
>>      cfg.interpmethod        = 'nearest';
>>      it = ft_sourceinterpolate(cfg,source,p); % p
>>
>>      cfg =  [];
>>      cfg.method = ‘ortho’;
>>      cfg.funparameter = 'coh';
>>      cfg.maskparameter = 'coh';
>>      ft_sourceplot(cfg,it);
>>
>> Thanks and greetings,
>> Alexandra Steina<ft_2019.png><ft_april2020.png><ft_june2020.png><ft_november2020.png><ft_october2020.png>_______________________________________________
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>> https://doi.org/10.1371/journal.pcbi.1002202
>
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