[FieldTrip] (no subject)

[Joram van Driel]

Dear Kaelasha,
I recently came across the exact same problem in my Neuromag source
analysis: mislocalization of motor beta over M1 (it was too posterior in my
case). I used LCMV, so my analyses are quite similar to yours.
Do you use the Elekta Maxfilter routine of *tSSS *(a preprocessing step to
remove non-brain noise)? There, I had the option *-trans* enabled, which
transforms the sensor data of every subject to a common space, so that
subjects become better comparable *in sensor space*. But this should not be
used if you do source analysis, because the forward model uses the
coregistered MRI, taking into account how that subject was positioned in
the scanner. When I redid tSSS without the -trans option, and recomputed
the forward model, motor beta suppression was right where it should be.
As for the coregistration, we used a polhemus point method of digitized 3D
points, which allows for continuous tracking of head position in the Elekta
scanner. With those points, there are built-in ways to coregister the MRI
using the Elekta software (somebody else helped me with that, so I don't
know the details how to do this).
With the coregistered MRIs, I just followed the fieldtrip tutorial up to
ft_prepare_leadfield, and everything went fine.

Best,
Joram



On Wed, Nov 5, 2014 at 2:50 PM, 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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>



-- 
Joram van Driel, MSc.
PhD student @ University of Amsterdam
Brain & Cognition @ Department of Psychology
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