Hierarchical Clustering for Network Analysis in Functional Connectivity MRI

Garth John Thompson^1,2, Matthew Magnuson^1,2, Shella Dawn Keilholz^1,2

Functional connectivity MRI promises to elucidate networks in the healthy and diseased brain, but the large amounts of data collected prove difficult to analyze. To solve this problem a hierarchical clustering algorithm is proposed which requires neither manual definition of anatomical regions nor manual determination of correlation threshold. When this algorithm was run on data from anaesthetized rats, it was able to create groups that corresponded to bilateral primary somatosensory cortex, motor cortex and secondary somatosensory cortex in a majority of the rats. It was also able to flag merges between these groups without having prior knowledge of anatomical regions.