title: Group-wise consistent cortical parcellation based on connectional profiles.
abstract: For decades, seeking common, consistent and corresponding anatomical/functional regions across individual brains via cortical parcellation has been a longstanding challenging problem. In our opinion, two major barriers to solve this problem are determining meaningful cortical boundaries that segregate homogeneous regions and establishing correspondences among parcellated regions of multiple brains. To establish a corresponding system across subjects, we recently developed the Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) system which possesses group-wise consistent white matter fiber connection patterns across individuals and thus provides a dense map of corresponding cortical landmarks. Despite this useful property, however, the DICCCOL landmarks are still far from covering the whole cerebral cortex and do not provide clear structural/functional cortical boundaries. To address the above limitation while leveraging the advantage of DICCCOL, in this paper, we present a novel approach for group-wise consistent parcellation of the cerebral cortex via a hierarchical scheme. In each hierarchical level, DICCCOLs are used as corresponding samples to automatically determine the cluster number so that other cortical surface vertices are iteratively classified into corresponding clusters across subjects within a group-wise classification framework. Experimental results showed that this approach can achieve consistent fine-granularity cortical parcellation with intrinsically-established structural correspondences across individual brains. Besides, comparisons with resting-state and task-based fMRI datasets demonstrated that the group-wise parcellation boundaries segregate functionally homogeneous areas.
authors.affiliation: School of Automation and Brain Decoding Research Center, Northwestern Polytechnical University, Xi'an 710072, China; Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA 30605, USA.
authors.forename: Tuo
authors.initials: T
authors.lastname: Zhang
authors.affiliation: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA 30605, USA.
authors.forename: Dajiang
authors.initials: D
authors.lastname: Zhu
authors.affiliation: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA 30605, USA.
authors.forename: Xi
authors.initials: X
authors.lastname: Jiang
authors.affiliation: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA 30605, USA.
authors.forename: Shu
authors.initials: S
authors.lastname: Zhang
authors.affiliation: Departments of Biomedical Engineering and Radiology, Wayne State University, Detroit, MI 48201, USA.
authors.forename: Zhifeng
authors.initials: Z
authors.lastname: Kou
authors.affiliation: School of Automation and Brain Decoding Research Center, Northwestern Polytechnical University, Xi'an 710072, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Guo
authors.affiliation: Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA 30605, USA. Electronic address: tliu@uga.edu.
authors.forename: Tianming
authors.initials: T
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000000981:diagnostic imaging*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results
keywords: Connectivity; Cortical parcellation; Group-wise; dMRI