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2018 | OriginalPaper | Buchkapitel

Connectivity-Driven Brain Parcellation via Consensus Clustering

verfasst von : Anvar Kurmukov, Ayagoz Musabaeva, Yulia Denisova, Daniel Moyer, Boris Gutman

Erschienen in: Connectomics in NeuroImaging

Verlag: Springer International Publishing

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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.

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Metadaten
Titel
Connectivity-Driven Brain Parcellation via Consensus Clustering
verfasst von
Anvar Kurmukov
Ayagoz Musabaeva
Yulia Denisova
Daniel Moyer
Boris Gutman
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-030-00755-3_13