Garth John Thompson1,2, Matthew Magnuson1,2,
Shella Dawn Keilholz1,2
1Biomedical Engineering, Georgia
Institute of Technology, Atlanta, GA, United States; 2Biomedical
Engineering, Emory University, Atlanta, GA, United States
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.