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

• Published in: Human brain mapping Vol. 42; no. 8; pp. 2477 - 2489
• Main Authors: Cheng, Hewei, Liu, Jie
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.06.2021
• Subjects: Adult; Attention; Brain; Brain architecture; Brain mapping; Cluster Analysis; Clustering; connectional topography mapping; Connectome - methods; co‐clustering; Female; fMRI; functional connectivity; Functional magnetic resonance imaging; Graph theory; Hemispheres; Humans; Image Processing, Computer-Assisted - methods; Information processing; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Mapping; Medical imaging; Neural networks; Neuroimaging; Reproducibility of Results; thalamocortical; Thalamus; Topography; Young Adult; co-clustering; fMRI; functional connectivity; connectional topography mapping; thalamocortical
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.25381

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. A novel method was proposed 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. The proposed the co‐clustering‐based method not only concurrently parcellates two interconnected brain regions of interest (ROIs) under consideration into functionally homogenous subregions, but also estimates the connectivity between these subregions from the two brain ROIs. Validation results have demonstrated that our method could yield more meaningful, interpretable, and homogenous connectional topography than existing methods.