[FieldTrip] ft_sourcestatistics

[Julian Keil]

Dear Eduardo,

@1: Do you mean normalize across the group? What you could do is interpolate the source-level data to an MRI and then use ft_volumenormalise to normalize all images to a common standard MRI. Check the FT site here: http://www.fieldtriptoolbox.org/tutorial/sourcemodel?s[]=warp <http://www.fieldtriptoolbox.org/tutorial/sourcemodel?s%5B%5D=warp>
Alternatively, you could take care that a common source grid is warped to the individual headmodel prior to the source analysis. In this case, you don’t need to normalize, as all subjects have the same number of sources. Check the tutorials on the FT-website for hints (e.g. http://www.fieldtriptoolbox.org/tutorial/salzburg?s[]=warp <http://www.fieldtriptoolbox.org/tutorial/salzburg?s%5B%5D=warp> or http://www.fieldtriptoolbox.org/tutorial/beamformingextended?s[]=warp <http://www.fieldtriptoolbox.org/tutorial/beamformingextended?s%5B%5D=warp>)

@2: Did you build your own headmodel? It might be that the elements of the headmodel intersect. I would go back and double check all steps leading up to the leadfield computation for errors.

@3: I have no idea.

@4: When does this happen?

Good luck,

Julian

> Am 12.12.2017 um 14:38 schrieb Uri Eduardo Ramírez Pasos <urieduardo at gmail.com>:
> 
> Dear fieldtrippers,
> 
> I have a couple questions regarding source reconstruction that I hope you can help me with.
> 
> 1. What is the best way to 'normalize' the position values in each forward model across my subjects so that I can run ft_sourcestatistics with cfg.statistic = 'ft_statfun_depsamplesT' ?
> 
> 2. For one of my subjects, their leadfield keeps containing only NaNs. What could be the source (no pun intended) of the problem?
> 
> 3. If my experiment's design has three 'levels' (say A1, A2, A3) for one 'factor', is it valid to subtract source values (obtained using method 'dics') for a comparison (e.g. A1-A3 vs A2-A3)?
> 
> 4. What could have gone wrong when i get the warning “matrix is singular, close to singular or badly scaled. Results may be inaccurate.” How does one go about solving this?
> 
> Best regards,
> Eduardo Ramírez, PhD candidate
> University of Würzburg
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