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Concurrent brain parcellation and connectivity estimation via co‐clustering of resting state fMRI data: A novel approach

Connectional topography mapping has been gaining widespread attention in human brain imaging studies. However, existing methods might not effectively utilize the information from neuroimaging data, thus hindering the understanding of the underlying connectional organization in the brain and uncoveri...

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Autores principales: Cheng, Hewei, Liu, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8090776/
https://www.ncbi.nlm.nih.gov/pubmed/33615651
http://dx.doi.org/10.1002/hbm.25381
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author Cheng, Hewei
Liu, Jie
author_facet Cheng, Hewei
Liu, Jie
author_sort Cheng, Hewei
collection PubMed
description Connectional topography mapping has been gaining widespread attention in human brain imaging studies. However, existing methods might not effectively utilize the information from neuroimaging data, thus hindering the understanding of the underlying connectional organization in the brain and uncovering the optimal clustering number from the data. In this study, we propose a novel method for the automated construction of inherent functional connectivity topography in a data‐driven manner by leveraging the power of co‐clustering‐based on resting state fMRI (rs‐fMRI) data. We propose the co‐clustering‐based method not only for concurrently parcellating two interconnected brain regions of interest (ROIs) under consideration into functionally homogenous subregions, but also for estimating the connectivity between these subregions from the two brain ROIs. In particular, we first model the connectional topography mapping as a co‐clustering‐based bipartite graph partitioning problem for constructing the inherent functional connectivity topography between the two interconnected brain ROIs. We also adopt an objective criterion, that is, silhouette width index measuring clustering quality, for determining the optimal number of clusters. The proposed method has been validated for mapping thalamocortical connectional topography based on rs‐fMRI data of 57 subjects. Validation results have demonstrated that our method identified the optimal solution with five pairs of mutually connected subregions of the thalamocortical system from the rs‐fMRI data, and could yield more meaningful, interpretable, and homogenous connectional topography than existing methods. The proposed method was further validated by the high symmetry of the mapped connectional topography between two hemispheres.
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spelling pubmed-80907762021-05-10 Concurrent brain parcellation and connectivity estimation via co‐clustering of resting state fMRI data: A novel approach Cheng, Hewei Liu, Jie Hum Brain Mapp Research Articles Connectional topography mapping has been gaining widespread attention in human brain imaging studies. However, existing methods might not effectively utilize the information from neuroimaging data, thus hindering the understanding of the underlying connectional organization in the brain and uncovering the optimal clustering number from the data. In this study, we propose a novel method for the automated construction of inherent functional connectivity topography in a data‐driven manner by leveraging the power of co‐clustering‐based on resting state fMRI (rs‐fMRI) data. We propose the co‐clustering‐based method not only for concurrently parcellating two interconnected brain regions of interest (ROIs) under consideration into functionally homogenous subregions, but also for estimating the connectivity between these subregions from the two brain ROIs. In particular, we first model the connectional topography mapping as a co‐clustering‐based bipartite graph partitioning problem for constructing the inherent functional connectivity topography between the two interconnected brain ROIs. We also adopt an objective criterion, that is, silhouette width index measuring clustering quality, for determining the optimal number of clusters. The proposed method has been validated for mapping thalamocortical connectional topography based on rs‐fMRI data of 57 subjects. Validation results have demonstrated that our method identified the optimal solution with five pairs of mutually connected subregions of the thalamocortical system from the rs‐fMRI data, and could yield more meaningful, interpretable, and homogenous connectional topography than existing methods. The proposed method was further validated by the high symmetry of the mapped connectional topography between two hemispheres. John Wiley & Sons, Inc. 2021-02-21 /pmc/articles/PMC8090776/ /pubmed/33615651 http://dx.doi.org/10.1002/hbm.25381 Text en © 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research Articles
Cheng, Hewei
Liu, Jie
Concurrent brain parcellation and connectivity estimation via co‐clustering of resting state fMRI data: A novel approach
title Concurrent brain parcellation and connectivity estimation via co‐clustering of resting state fMRI data: A novel approach
title_full Concurrent brain parcellation and connectivity estimation via co‐clustering of resting state fMRI data: A novel approach
title_fullStr Concurrent brain parcellation and connectivity estimation via co‐clustering of resting state fMRI data: A novel approach
title_full_unstemmed Concurrent brain parcellation and connectivity estimation via co‐clustering of resting state fMRI data: A novel approach
title_short Concurrent brain parcellation and connectivity estimation via co‐clustering of resting state fMRI data: A novel approach
title_sort concurrent brain parcellation and connectivity estimation via co‐clustering of resting state fmri data: a novel approach
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8090776/
https://www.ncbi.nlm.nih.gov/pubmed/33615651
http://dx.doi.org/10.1002/hbm.25381
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