Optimizing Connectivity-Driven Brain Parcellation Using Ensemble Clustering
| Autor: | Paul M. Thompson, Anvar Kurmukov, Neda Jahanshad, Boris A. Gutman, Yulia I. Denisova, Daniel Moyer, Ayagoz Mussabaeva |
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| Rok vydání: | 2020 |
| Předmět: |
Adult
Male structural brain connectivity Computer science Neuroimaging ensemble clustering 050105 experimental psychology diffusion MRI Young Adult 03 medical and health sciences Atlases as Topic 0302 clinical medicine Image Interpretation Computer-Assisted Prior probability Connectome Image Processing Computer-Assisted Humans 0501 psychology and cognitive sciences Cluster analysis human connectome Human Connectome Project Quantitative Biology::Neurons and Cognition Statistics::Applications business.industry General Neuroscience 05 social sciences Brain atlas connectivity-based parcellation Brain Human Connectome Pattern recognition Original Articles brain atlas Diffusion Magnetic Resonance Imaging Graph (abstract data type) Female Artificial intelligence Nerve Net business 030217 neurology & neurosurgery Diffusion MRI |
| Zdroj: | Brain Connectivity |
| ISSN: | 2158-0022 2158-0014 |
| DOI: | 10.1089/brain.2019.0722 |
| Popis: | This work addresses the problem of constructing a unified, topologically optimal connectivity-based brain atlas. The proposed approach aggregates an ensemble partition from individual parcellations without label agreement, providing a balance between sufficiently flexible individual parcellations and intuitive representation of the average topological structure of the connectome. The methods exploit a previously proposed dense connectivity representation, first performing graph-based hierarchical parcellation of individual brains, and subsequently aggregating the individual parcellations into a consensus parcellation. The search for consensus—based on the hard ensemble (HE) algorithm—approximately minimizes the sum of cluster membership distances, effectively estimating a pseudo-Karcher mean of individual parcellations. Computational stability, graph structure preservation, and biological relevance of the simplified representation resulting from the proposed parcellation are assessed on the Human Connectome Project data set. These aspects are assessed using (1) edge weight distribution divergence with respect to the dense connectome representation, (2) interhemispheric symmetry, (3) network characteristics' stability and agreement with respect to individually and anatomically parcellated networks, and (4) performance of the simplified connectome in a biological sex classification task. Ensemble parcellation was found to be highly stable with respect to subject sampling, outperforming anatomical atlases and other connectome-based parcellations in classification as well as preserving global connectome properties. The HE-based parcellation also showed a degree of symmetry comparable with anatomical atlases and a high degree of spatial contiguity without using explicit priors. |
| Databáze: | OpenAIRE |
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