DICS Localization Problem

[jan-mathijs schoffelen]

> Dear Nathan and Nathan,
>
> I'd like to add that I recently discovered some inconsistencies
> with respect to the units in which gradiometer positions/dipole
> positions etc. are expressed.
> We are in the process of looking into this and making it
> consistent. For now you should ensure yourself that the gradiometer
> positions and grid locations and the mri-transformation matrix have
> the same units (which is meters in our 4D-system). Moreover, the
> singleshell method somewhere has a hard-coded assumption that the
> units are centimeters (which is the default units for CTF systems)
> so the solution might not be entirely correct if you use meters.
>
> Yours,
> Jan-Mathijs
>
> On Aug 5, 2008, at 7:34 AM, Nathan Weisz wrote:
>
>> dear nathan,
>>
>> we see nice mu desynchronizations nicely localized to motor cortex
>> when subjects prepare responding to a target using a 4-D 2500 WH
>> (the one with 148 sensors).
>> Have you already done localizations with your MEG system using a
>> segmented brain? If not, then I suggest to check if you get the
>> same results using a more 'standard' headmodel, e.g. multisphere
>> (like in the tutorial). this assures you always are in the MEG-
>> coordinate system, and have no weird mixture between coordinate
>> systems. you need a headshape for that (pretty standard with 4D; i
>> can provide you with one if you don't have one). also you might
>> want to check if your volume / grid makes sense relative to the
>> sensor position using 'headmodelplot'.
>>
>> good luck,
>> yet-another-nathan
>>
>> On 05.08.2008, at 02:45, Nathan Dees wrote:
>>
>>> Dear Fieldtrippers,
>>>
>>> Have been struggling a long time to produce a viable image of
>>> beamforming
>>> analysis.   We are using a 4D-Magnes3600 Magnetometer MEG unit to
>>> record a
>>> prompted finger tapping motor task with variable ISI of 2.5 - 3.0
>>> seconds.
>>> Our time-frequency plots  show evident beta and alpha
>>> desynchronization at movement onset.  We are using the
>>> beamforming tutorial
>>> as a guide.  After calculating dataPre and dataPost, and
>>> verifying our mri
>>> has been properly segmented by SPM, our code is:
>>>
>>> cfg = [];
>>> cfg.method    = 'mtmfft';
>>> cfg.output    = 'powandcsd';
>>> cfg.tapsmofrq = 2;
>>> cfg.foilim    = [18 20];
>>> cfg.pad       = 0.5;
>>> freqPre = freqanalysis(cfg,dataPre);
>>>
>>> cfg = [];
>>> cfg.method    = 'mtmfft';
>>> cfg.output    = 'powandcsd';
>>> cfg.tapsmofrq = 2;
>>> cfg.foilim    = [18 20];segmentedmriF.transform = mri.transform;
>>> segmentedmriF.anatomy   = mri.anatomy;
>>> cfg.pad       = 0.5;
>>> freqPost = freqanalysis(cfg,dataPost);
>>>
>>> segmentedmri.transform = mri.transform;
>>> segmentedmri.anatomy   = mri.anatomy;
>>> [vol, cfg] = prepare_singleshell([], segmentedmri);
>>>
>>> cfg                       = [];
>>> cfg.grad                  = freqPre.grad;
>>> cfg.vol                   = vol;
>>> cfg.reducerank            = 2;
>>> cfg.channel               = {'MEG'};
>>> cfg.xgrid                 = -30:1:30;   % 'AUTO' does not work!
>>> cfg.ygrid                 = -30:1:30;   % 'AUTO' does not work!
>>> cfg.zgrid                 = -40:1:40;    % 'AUTO' does not work!
>>> [grid]                    = prepare_leadfield(cfg);
>>>
>>> cfg = [];
>>> cfg.frequency    = 20;
>>> cfg.method       = 'dics';
>>> cfg.projectnoise = 'yes';
>>> cfg.grid         = grid;
>>> cfg.vol          = vol;
>>> cfg.lambda       = 0;
>>>
>>> sourcePre  = sourceanalysis(cfg,freqPre );
>>> sourcePost = sourceanalysis(cfg,freqPost);
>>>
>>> %Plot of deep sources in the center of the brain looks reasonable.
>>>
>>> sourceDiff = sourcePost;
>>> sourceDiff.avg.pow = (sourcePre.avg.pow - sourcePost.avg.pow) ./
>>> sourcePost.avg.pow;
>>> cfg = [];
>>> cfg.downsample = 2;
>>> sourceDiffInt = sourceinterpolate(cfg, sourceDiff , mri);
>>>
>>> cfg = [];
>>> cfg.method = 'slice';
>>> cfg.funparameter = 'avg.pow';
>>> cfg.maskparameter = cfg.funparameter;
>>> cfg.funcolorlim = [0.0 .2];
>>> cfg.opacitylim = [0.0 .2];
>>> cfg.opacitymap = 'rampup';
>>> figure
>>> sourceplot(cfg,sourceDiffInt);
>>>
>>> We expect localization of desynchronization to be in the motor
>>> cortex,
>>> predominantly on the left side.  The result we get is diffusely
>>> localized,
>>> and with weak signal.  I have images to possibly help diagnose
>>> the problem,
>>> but for now, can anyone spot the errors?
>>>
>>> Thank you!!
>>>
>>> ----------------------------------
>>> The aim of this list is to facilitate the discussion between
>>> users of the FieldTrip  toolbox, to share experiences and to
>>> discuss new ideas for MEG and EEG analysis. See also http://
>>> listserv.surfnet.nl/archives/fieldtrip.html and http://www.ru.nl/
>>> fcdonders/fieldtrip.
>>
>> --------------------------------------------
>> Attention: New Contact Details
>> --------------------------------------------
>> Dr. Nathan Weisz
>>
>> OBOB-Lab
>> University of Konstanz
>> Department of Psychology
>> P.O. Box D23
>> 78457 Konstanz
>> Germany
>>
>> Tel: ++49 - (0)7531 - 88 45 84
>> Email: nathan.weisz at uni-konstanz.de
>> Homepage: coming soon
>>
>> Bob-wisdom: It's better to do one job well than two jobs... not so
>> well.
>>
>> ----------------------------------
>>
>> The aim of this list is to facilitate the discussion between users
>> of the FieldTrip toolbox, to share experiences and to discuss new
>> ideas for MEG and EEG analysis.
>>
>> http://listserv.surfnet.nl/archives/fieldtrip.html
>>
>> http://www.ru.nl/fcdonders/fieldtrip/
>>
>


----------------------------------
The aim of this list is to facilitate the discussion between users of the FieldTrip  toolbox, to share experiences and to discuss new ideas for MEG and EEG analysis. See also http://listserv.surfnet.nl/archives/fieldtrip.html and http://www.ru.nl/fcdonders/fieldtrip.
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