Classification of Normal and Pathological Brain Networks Based on Similarity of Graph Partitions

Student: Kurmukov Anvar

Supervisor: Leonid E Zhukov

Educational Programme: Data Science (Master)

Final Grade: 10

Year of Graduation: 2017

I consider a task of predicting normal and pathological phenotypes from macroscale human brain networks. These networks, called connectomes, represent connections between predefined brain regions. Thus brain regions becomes a network nodes, and aggregated neural pathways translates into edges of a such network. The set of brain regions is the same across different brains and the set of edges varies from subject to subject. I make use of the former property and assume that connectomes obtained from normal brains differs from those obtained from pathological in how brain regions cluster into communities. I demonstrate how dissimilarity in community structure could be used in a classification task on three real data sets.

Full text (added May 30, 2017)

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