[FieldTrip] Source statistics on spatio-temporal source reconstruction data (MNE)

[Nicolai Mersebak]

Dear all,

I have a question concerning the usage of ft_sourcegrandaverage and
ft_sourcestatistics.

After using ft_sourceanalysis (method: MNE), I get spatio-temporal source
reconstructed data in source.avg.pow (4050 x 897): 4050 sources and 897
time points.

Now I would like to use the cluster-based permutation test on my source
reconstructed data. However it seems like ft_sourcegrandaverage and
ft_sourcestatistics
don't support source level time courses. E.g when I am using
ft_sourcegrandaverage
I am getting the following error:

Error in ft_sourcegrandaverage (line 158)
  dat(:,i) = tmp(:);

Looking into the code:

  for i=1:Nsubject

    tmp = getsubfield(varargin{i}, parameterselection(cfg.parameter,
varargin{i}));

    dat(:,i) = tmp(:);

    tmp = getsubfield(varargin{i}, 'inside');

    inside(tmp,i) = 1;

  end

I see that "tmp" are getting the structure [N_sources x timepoints] from
source.avg.pow for one subject, where "dat" requires the structure
[N_sources x 1].

I seached the mailing list for similar issues and found this thread:

http://mailman.science.ru.nl/pipermail/fieldtrip/2010-September/003122.html

Since I am interested in using the temporal dimension in my statistics, I
would like to know if it is still not possible to use spatio-temporal
source reconstructed data in ft_sourcestatistics and ft_sourcegrandaverage ?

Or if any have succeeded in using the cluster-based permutation test on
source level also including the temporal dimension ?

Alternative I was thinking that I might could use ft_timelockstatistics,
where I substituted the channels with sources, e.g instead of having 64
channels, I would now have 4050 "channels".
If so I need to calculate a label structure and an appropriate neighbor
structure, which I guess is possible as I have all the 3D coordinates for
each source, e.g in leadfield.pos ?
I know this is a work around solution, but have anyone tried or have any
experience using such an approach ?

Best,

Nicolai
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