Connectivity-Driven Brain Parcellation via Consensus Clustering
This work addresses the need for improved brain parcellation methods in neuroscience, offering incremental advancements over existing approaches.
The authors tackled the problem of deriving connectivity-based brain atlases from individual connectomes by proposing two methods that aggregate individual parcellations into a consensus parcellation, finding that their greedy search method at hierarchical depth 3 outperformed other atlases in assessments including divergence, symmetry, and a sex classification task.
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.