Dear colleagues.
Hello, I am currently studying brain connectivity in the disease group.
Recently, I constructed a connectivity matrix from each participants' neuroimaging data (diffusion tensor imaging), then ran the edge-wise correlational analysis with the neuropsychological score using a tool similar to Network-based statistics (a.k.a NBS; Zalesky et al., 2010).
As a result, I got an edge-level network consisting of 10 edges with 19 nodes.
Those significantly denoted edges are not connected to each other but identified as single edges.
Conventionally, I used graph theoretical measures such as a degree or betweenness centrality for defining hub node (i.e. hub region = betweenness centrality + 1SD within the nodes of the network).
However, in this case, I have an edge-level network that is hard to say is clustered or connected but consisted of multiple single edges.
From here, I want to specifically emphasize more significant edges or nodes within the identified network as a hub region (well it is hard to say it is a hub, but at least for easy comprehension), but I am quite struggling with what approach to take.
All discussion and suggestions are welcomed here.
Or if I am misunderstanding any, please give me feedback or comments.
Thank you in advance!
Jean
For example, you could invent a measure based on cluster coefficients.
https://en.wikipedia.org/wiki/Clustering_coefficient
You could consider all vertices that lie in a shortest edge distance up to a certain maximum, and count all edge in it.
Also, it can be interesting to determine the number of cliques a vertex takes part in. Or the distribution of the number of vertices in these cliques. Or the overlapping percentages of any pair of cliques a vertex is in.
Regards,
Joachim
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