[FieldTrip] seed based SOURCE connectivity analysis betweenGroups - GA, plotting and statistics
cornelia.quaedflieg at uni-hamburg.de
Wed Oct 24 23:42:54 CEST 2018
Thank you for your quick reply.
@1R Indeed ft_connectivityanalysis on individual dipole positions (PCC). Cfg.refindx’ seems not to do anything, are there other options to run a seed-based connectivity analysis?
@R2 We indeed performed source-reconstruction for each subject on a subject-specific grid, that maps onto a template grid in spatially normalized space.
I constructed a GA of the individual data and would like to plot these on a standard cortical sheet / brain surface.
Van: Schoffelen, J.M. (Jan Mathijs)
Verzonden: woensdag 24 oktober 2018 10:16
Aan: FieldTrip discussion list
Onderwerp: Re: [FieldTrip] seed based SOURCE connectivity analysis betweenGroups - GA, plotting and statistics
You are a bit short on the details, so it is hard to give to-the-point feedback.
@1 this depends on the type of connectivity metric you have in mind. You mention the ‘cfg.refindx’ so I assume that you want to use ft_connectivityanalysis. Also, do you use ‘parcellated’ source data, or is the data defined on individual dipole positions?
@2 this works best if your individual subject source models can be easily compared, e.g. according to
This allows for averaging/statistics at the low-number-of-sources (before interpolation) and saves a lot of memory.
On 23 Oct 2018, at 00:18, Quaedflieg, Conny (PSYCHOLOGY) <conny.quaedflieg at maastrichtuniversity.nl> wrote:
I’m trying to compare 2 groups (n=40 per group) on SOURCE connectivity data using a seed (based on an atlas).
Based on the help function this should be possible using cfg.refindx.
Though, the analysis looks exactly the same with and without the refindx specified.
I would be really grateful with help on the following
1. Ideas how to run a seed based source analysis
2. How can I combine source data of several pp’s and plot the grand averages and run statistics over it?
I tried this with ft_sourcegandaverage though when interpolating the data to the MRI I get error messages that the data is too large and that it wil take too long.
Conny Quaedflieg, PhD
Department of Clinical Psychological Science
Faculty of Psychology and Neuroscience
UNS 40, Room A3731a
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