Functional organization of the human posterior cingulate cortex, revealed by multiple connectivity-based parcellation methods

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Abstract

Based on cytoarchitecture, the posterior cingulate cortex (PCC) is thought to be comprised of two distinct functional subregions: the dorsal and ventral PCC (dPCC and vPCC). However, functional subregions do not completely match anatomical boundaries in the human brain. To understand the relationship between the functional organization of regions and anatomical features, it is necessary to apply parcellation algorithms based on functional properties. We therefore defined functionally informed subregions in the human PCC by parcellation of regions with similar patterns of functional connectivity in the resting brain. We used various patterns of functional connectivity, namely local, whole-brain and diffuse functional connections of the PCC, and various clustering methods, namely hierarchical, spectral, and k-means clustering to investigate the subregions of the PCC. Overall, the approximate anatomical boundaries and predicted functional regions were highly overlapped to each other. Using hierarchical clustering, the PCC could be clearly separated into two anatomical subregions, namely the dPCC and vPCC, and further divided into four subregions segregated by local functional connectivity patterns. We show that the PCC could be separated into two (dPCC and vPCC) or four subregions based on local functional connections and hierarchical clustering, and that subregions of PCC display differential global functional connectivity, particularly along the dorsal-ventral axis. These results suggest that differences in functional connectivity between dPCC and vPCC may be due to differences in local connectivity between these functionally hierarchical subregions of the PCC. Hum Brain Mapp 38:2808–2818, 2017. © 2017 Wiley Periodicals, Inc.

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Cha, J., Jo, H. J., Gibson, W. S., & Lee, J. M. (2017). Functional organization of the human posterior cingulate cortex, revealed by multiple connectivity-based parcellation methods. Human Brain Mapping, 38(6), 2808–2818. https://doi.org/10.1002/hbm.23570

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