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SfN 2007: Interactive visualization and graph analysis of CoCoMac's brain parcellation and white matter connectivity data

Today, Raghav Singh and I presented a poster at Society for Neuroscience (SfN 2007) conference in San Diego, CA.

TITLE
Interactive visualization and graph analysis of CoCoMac's brain parcellation and white matter connectivity data

ABSTRACT
We present a tool for visualizing and analyzing white matter brain connectivity data. The tool combines hierarchical mapping (brain parcellation) data with the connectivity data, thus providing structural organization to the complex connectivity graph. The mapping and connectivity data has been downloaded from the online CoCoMac database and algorithmically agglomerated. In order to create a hierarchical graph on the mapping data, we are interested only in identical, substructure and superstructure relationships. The important step in our algorithm is to create sets of maximal identical sites such that there are no contradictions, i.e., there are no cycles in the resulting hierarchy. Our final dataset contains 1014 brain sites and 9238 white matter connections.

The visualization tool organizes these brain sites as nodes on concentric circles, where the innermost node corresponds to the Brain and the outermost nodes correspond to the smallest subdivisions of the brain. Directed edges between nodes are the white matter connections. Besides standard GUI functions, the tool provides the capability to browse the data by selecting nodes and edges incident on nodes.

We have analyzed our dataset; the analysis includes metrics that Sporns proposed for white matter connectivity. Our results are very similar to his results; the connectivity graph is small world with a cluster index of 0.455, a diameter of 7 and about 33% of the total connections are reciprocal in nature. In comparison a random graph of the same dimensions, would result in cluster index of 0.019 and diameter of 6. We have also identified the hubs (node that is connecting to a lot of other nodes) and authorities (node that is being connected from a lot of other nodes) which gives a unique insight in that the parietal lobule is the best authority while the thalamus is the best hub in the graph. We are working on identifying the edges and nodes that are most susceptible to errors/attacks, and in laying out the connectivity graph such that it ``flows'' from the hubs to the authorities, and also evaluating the community structure of the graph.

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