Tractography-Based Parcellation of Cerebellar Dentate Nuclei via a Deep Nonnegative Matrix Factorization Clustering Method

As the largest human cerebellar nucleus, the dentate nucleus (DN) functions significantly in the communication between the cerebellum and the rest of the brain. Structural connectivity-based parcellation has the potential to reveal the topography of the DN and enable the study of its subregions. In...

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Bibliographic Details
Published inProceedings (International Symposium on Biomedical Imaging) pp. 1 - 4
Main Authors Xu, Xiao, Chen, Yuqian, Zekelman, Leo, Rathi, Yogesh, Makris, Nikos, Zhang, Fan, O'Donnell, Lauren J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.04.2023
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Cerebellum
Clustering algorithms
Clustering methods
deep learning
Dentate
diffusion MRI
fiber clusters
Magnetic resonance imaging
Optical fiber communication
Surfaces
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Summary:As the largest human cerebellar nucleus, the dentate nucleus (DN) functions significantly in the communication between the cerebellum and the rest of the brain. Structural connectivity-based parcellation has the potential to reveal the topography of the DN and enable the study of its subregions. In this paper, we investigate a deep nonnegative matrix factorization clustering method (DNMFC) for parcellation of the human DN based on its structural connectivity using diffusion MRI tractography. We propose to describe the connectivity of the DN using a set of curated tractography fiber clusters within the cerebellum. Experiments are conducted on the diffusion MRI data of 50 healthy adults from the Human Connectome Project. In comparison with state-of-the-art clustering methods, DN parcellations resulting from DNMFC show better quality and consistency of parcels across subjects.
ISSN:1945-8452
DOI:10.1109/ISBI53787.2023.10230528