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Semi-metric Analysis of fMRI Connectivity Networks

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If you have a question about this talk, please contact Mikail Rubinov.

The majority of research on complex networks treats interactions as binary edges in graphs, even though interactions in real networks exhibit a wide range of intensities or strengths. Weighted real networks, such as the Human connectome network, fMRI connectivity networks, etc. have high number of transitivity violations. That is, two nodes in a network have a transitivity violation if they have an indirect path, which is stronger than the strength of the edge between them (direct path). We call these networks semi-metric. Transitivity can be studied as a general topological phenomenon in weighted graphs. Here, we present this type of analysis on fMRI connectivity networks from Autistic data.

This talk is part of the Brain Mapping Unit Networks Meeting and the Cambridge Connectome Consortium series.

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