Connectivity-Driven Brain Parcellation via Consensus Clustering

• Published in: Connectomics in NeuroImaging Vol. 11083; pp. 117 - 126
• Main Authors: Kurmukov, Anvar, Musabaeva, Ayagoz, Denisova, Yulia, Moyer, Daniel, Gutman, Boris
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Cluster-based Similarity Partitioning Algorithm; Consensus Clustering; Gender Classification Task; Individual Parcellations; Jensen-Shannon Divergence
• ISBN: 3030007545; 9783030007546
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-00755-3_13

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.