[FieldTrip] (no subject)

[Stephen Whitmarsh]

Hi Kaelasha,

It's hard to evaluate why the localization in not exactly how you would
expect it, but here are some idea you could try from the top of my head.
Note I never used LCMV beamformer myself, although I've done DICS.
Also, to me there seems to be not something obviously *wrong *in the code
or pictures, so I'm just suggesting some thoughts here.

- Beamformer performance is VERY dependent on the data. Check your sensor
level data, clean it more if necessary.
- Play around with including more or less data centered around the
component you are localizing for both the common filter as the data you
localize. Since I don't know the details of the data (nor do i have
experience with LCMV) your choices here are hard to evaluate.
- Plot the scalp as well to get a better idea whether your co-registration
is accurate. If you have additional headpoints e.g. by using a Polhemus,
that would help.
- Play around with some regularization - in ft_sourceanalysis use e.g.:
cfg.alpha = '10%';
- Evaluate your lcmv beamformer with a dipole fit.
- Check other subjects/recordings - other recordings might be better (or
worse...)

Finally, if you haven't already, take a look at the scripts we used in our
recent workshop using a Neuromag system (although as I said I see nothing
wrong with your script):
http://fieldtrip.fcdonders.nl/workshop/stockholm2014q3

The beamformer lecture there given by Robert on Beamforming is also online:
http://natmeg.se/wp/activities/natmeg-lectures/

Good luck!
Stephen




On 5 November 2014 14:50, Kaelasha Tyler <ktyler at swin.edu.au> wrote:

>  Dear Field trippers,
> And especially dear Neuromag users,
>
>  I have been going back over the analysis of my study, ironing out
> issues, and am finding that I am still having problems with source analysis
> coming up with some unexpected and probably inaccurate locations for
> clusters of significant effects.
>
>  I have decided to go back and look at a simple right handed button push,
> in one subject, to check if I was able to locate a realistic region in M1.
>
>  From the attached jpeg, you will see that the analysis is not accurately
> locating left hemisphere motor regions. I know I can't expect absolute
> accuracy, but I would be hoping for more than this.
>
>  I have included my code (below) and if any one has any suggestions about
> how to remedy this and produce more accurate results, please let me know!!!
>
>  I am wondering if I have realigned my mri to the neuromag system
> correctly, and have attached another jpeg of my volume conduction model,
> plotted alongside the neuromag sensors.
>
>  Neuromag users, does this look accurate to you?
>
>  As a general fix, I was wondering if any neuromag users would be happy
> to supply their FT code for use of mri, including realignment of mri to the
> neuromag system, segmentation and the creation of the volume conduction
> model?
>
>  As always, any help is much appreciated!
>
>  Here is the code as I have been currently using it:
>
>  %% Load MRI
>
>  mri = ft_read_mri('Subject1.nii');
>
>  %% Realigning
> cfg=[];
> cfg.coordsys = 'neuromag';
> mri = ft_volumerealign(cfg, mri);
>
> %% Segmentation.
>
>  cfg = [];
> cfg.coordsys='neuromag';
> cfg.units='mm';
> seg = ft_volumesegment(cfg, mri);
>
>  %% Prepare volumne conduction model
>
>  cfg = [];
> cfg.method = 'singleshell';
> vol = ft_prepare_headmodel(cfg, seg);
>
> %% Non warped grid for use in single subject comparison btw conditions
> cfg = [];
> cfg.grid.xgrid  = -20:1:20;
> cfg.grid.ygrid  = -20:1:20;
> cfg.grid.zgrid  = -20:1:20;
> cfg.grid.unit   = 'cm';
> cfg.grid.tight  = 'yes';
> cfg.vol        = vol;
> sourcemodel          = ft_prepare_sourcemodel(cfg);
>
>  %% Calculates leadfields
>
>  cfg                 = [];
> cfg.grid=sourcemodel;
> cfg.vol             = vol;
> cfg.channel={'MEGGRAD'};% For Planar gradiometers only
> cfg.grad=grad;%This needs to be edited to represent the MEG data set which
> has just been loaded
> grid = ft_prepare_leadfield(cfg, Condition1);
>
>  %% Append individuals data from two conditions.
> %This is so as to create a common filter, from the two conditions.
> cfg=[];
> cfg.appenddim = 'rpt';
> combined = ft_appendtimelock(cfg, Condition1, Condition2);
>
>  %% Calculate covariance and timelock. Input data should NOT have been
> timelocked before this.
>
>  cfg                  = [];
> cfg.keeptrials    = 'yes';
> cfg.covariance       = 'yes';
> cfg.covariancewindow = 'all';
> cfg.vartrllength     = 2;
> cfg.channel='MEGGRAD';
> tlckavgCond1          = ft_timelockanalysis(cfg, Condition1);
> tlckavgCombined           = ft_timelockanalysis(cfg, combined);
>
>  %% Create spatial filter using the lcmv beamformer
> cfg                  = [];
> cfg.grid             = grid; % leadfield, which has the grid information
> cfg.grad=grad;
> cfg.vol              = vol; % volume conduction model (headmodel)
> cfg.keepfilter       = 'yes';
> cfg.lcmv.fixedori    = 'yes'; % project on axis of most variance using SVD
> cfg.channel='MEGGRAD';
> sourceCombined = ft_sourceanalysis(cfg, tlckavgCombined);%
> %%
> cfg.grid.filter = sourceCombined.avg.filter;
> cfg.rawtrial='yes';
> sourceCond1=ft_sourceanalysis(cfg, tlckavgCond1);
>
>  %% The cluster stats at source space:
>
>  %%
> cfg=[];
> cfg.dim=sourceCond1.dim;
> cfg.method      = 'montecarlo';
> cfg.statistic   = 'depsamplesT';
> cfg.parameter   = 'pow';
> cfg.correctm    = 'cluster';
> cfg.numrandomization = 1000;
> cfg.alpha       = 0.05;
> cfg.tail        = 0;
> cfg.clusteralpha = 0.005;
> cfg.minnbchan = 8;
> cfg.correcttail = 'alpha';
> cfg.clusterstatistic = 'maxsum';
> Nsub = 84;
> cfg.design(1,1:2*Nsub)  = [ones(1,Nsub) 2*ones(1,Nsub)];
> cfg.design(2,1:2*Nsub)  = [1:Nsub 1:Nsub];
> cfg.ivar                = 1; % the 1st row in cfg.design contains the
> independent variable
> cfg.uvar                = 2; % the 2nd row in cfg.design contains the
> subject number
>
> stat = ft_sourcestatistics(cfg, sourceCond1, sourceCond2);
>
>  %% interpolate the t maps to the structural MRI of the subject %
> cfg = [];
> cfg.parameter = 'mask';
> statplot = ft_sourceinterpolate(cfg,stat, mri);
> %% plot the t values on the MRI %
>
>  cfg = [];
> cfg.method        = 'slice';
> cfg.funparameter  = 'mask';
> cfg.maskparameter = 'funparameter';
> figure
> ft_sourceplot(cfg, statplot);
> %%
>
>
>
>
>
>
>
>
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