Title: Connectivity-Driven Brain Parcellation via Consensus Clustering,
Abstract: We present two related methods for deriving connectivity-based brain atlases
from individual connectomes. The proposed methods exploit a previously proposed
dense connectivity representation, termed continuous connectivity, by first
performing graph-based hierarchical clustering of individual brains, and
subsequently aggregating the individual parcellations into a consensus
parcellation. The search for consensus minimizes the sum of cluster membership
distances, effectively estimating a pseudo-Karcher mean of individual
parcellations. We assess the quality of our parcellations using (1)
Kullback-Liebler and Jensen-Shannon divergence with respect to the dense
connectome representation, (2) inter-hemispheric symmetry, and (3) performance
of the simplified connectome in a biological sex classification task. We find
that the parcellation based-atlas computed using a greedy search at a
hierarchical depth 3 outperforms all other parcellation-based atlases as well
as the standard Dessikan-Killiany anatomical atlas in all three assessments.