title: Parcellation of the primary cerebral cortices based on local connectivity profiles.
abstract: Connectivity-based parcellation using diffusion MRI has been extensively used to parcellate subcortical areas and the association cortex. Connectivity profiles are vital for connectivity-based parcellation. Two categories of connectivity profiles are generally utilized, including global connectivity profiles, in which the connectivity information is from the seed to the whole brain, and long connectivity profiles, in which the connectivity information is from the seed to other brain regions after excluding the seed. However, whether global or long connectivity profiles should be applied in parcellating the primary cortex utilizing connectivity-based parcellation is unclear. Many sources of evidence have indicated that the primary cerebral cortices are composed of structurally and functionally distinct subregions. Because the primary cerebral cortices are rich in local anatomic hierarchical connections and possess high degree of local functional connectivity profiles, we proposed that local connectivity profiles, that is the connectivity information within a seed region of interest, might be used for parcellating the primary cerebral cortices. In this study, the global, long, and local connectivity profiles were separately used to parcellate the bilateral M1, A1, S1, and V1. We found that results using the three profiles were all quite consistent with reported cytoarchitectonic evidence. More importantly, the results using local connectivity profiles showed less inter-subject variability than the results using the other two, a finding which suggests that local connectivity profiles are superior to global and long connectivity profiles for parcellating the primary cerebral cortices. This also implies that, depending on the characteristics of specific areas of the cerebral cortex, different connectivity profiles may need to be adopted to parcellate different areas.
authors.affiliation: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China.
authors.forename: Qiaojun
authors.initials: Q
authors.lastname: Li
authors.affiliation: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China.
authors.forename: Ming
authors.initials: M
authors.lastname: Song
authors.affiliation: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China.
authors.forename: Lingzhong
authors.initials: L
authors.lastname: Fan
authors.affiliation: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Brainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China ; CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of Sciences Beijing, China ; The Queensland Brain Institute, University of Queensland, Brisbane QLD, Australia.
authors.forename: Tianzi
authors.initials: T
authors.lastname: Jiang
keywords: connectivity-based parcellation; diffusion tensor imaging; local connectivity profiles; primary cerebral cortices; tractography