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

[smoratti at psi.ucm.es]

Dear Nicolai,

Indeed I have used ft_timelockstatistics for minimum norm source data. The trick is to put the source level data into a ERF structure. Determining the neighbors of a source surface with vertices is not trivial. However I used tess_vertconn.m from the BrainStorm toolbox to get the connectivity matrix that tells you who is a neighbor. This you can feed into timelockstats.

Hope that helps,

Stephan

________________________________________________________
Stephan Moratti, PhD

see also: http://web.me.com/smoratti/

Universidad Complutense de Madrid
Facultad de Psicología
Departamento de Psicología Básica I
Campus de Somosaguas
28223 Pozuelo de Alarcón (Madrid)
Spain

and

Center for Biomedical Technology
Laboratory for Cognitive and Computational Neuroscience
Parque Científico y Tecnológico de la Universidad Politecnica de Madrid
Campus Montegancedo
28223 Pozuelo de Alarcón (Madrid)
Spain


email: smoratti at psi.ucm.es
Tel.:    +34 679219982

El 12/06/2013, a las 15:44, Nicolai Mersebak escribió:

> 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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