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Thank you for your response!

I'll describe the file in a bit more detail and then I'd be happy to take further discussion offline. 

My main aim is to compute and visualize network parameters from this file, which is essentially an edge file concatenated across several subjects.

Hope that helps. 
Best, 
Kevin




On Thu, Jan 14, 2016 at 8:44 PM, Juergen Pfeffer <[log in to unmask]> wrote:

Hi Kevin,

I've looked at the data and ... I don't understand them. :) Especially, could you describe what these columns are? Are these correlated activities of different brain regions? If so, then I guess the heading of a column encodes the two areas, i.e. LR_TG_mul1_LR_EC = LR_TG + LR_EC. If this is still right, then you have a matrix per subject stored in a line. And what do you want to do with the lines, aggregate them by summing them up for each column?

Feel free to start a non-list email for details.

Best,

Jürgen

 

 

From: Social Networks Discussion Forum [mailto:[log in to unmask]] On Behalf Of Kevin Bickart
Sent: Thursday, January 14, 2016 11:12 PM
To: [log in to unmask]
Subject: [SOCNET] resting-state brain networks

 

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Hi all,

 

I'm a neuroscientist studying genetic drivers and behavioral targets of resting-state brain networks. I'm looking for help computing network parameters from resting-state functional connectivity MRI data, with the aim of using those parameters as variables in regression analyses with gene and behavior data.

 

Currently I have correlation matrices containing a row for every subject with columns of node-to-node correlation values, or strength of functional connectivity, representing weighted edges (see link). I would like to use this file to visualize and compute such parameters as degree, density, betweenness centrality, closeness, etc. Could anyone help me transform such a file into one usable for Gephi or another program to visualize and compute network properties for multiple subjects in one step or relatively efficiently? Alternative strategies/ideas also also welcome.

Thanks so much in advance. 

Best,


--
Kevin Bickart, MD/PhD
Anatomy & Neurobiology
Medicine intern, CPMC 2015-16
Neurology resident, Stanford 2016-19

 

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--
Kevin Bickart MD, PhD 
Anatomy & Neurobiology
Medicine intern, CPMC 2015-16
Neurology resident, Stanford 2016-19


_____________________________________________________________________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.