Multi-Level Parcellation of the Cerebral Cortex Using Resting-State fMRI

Cortical parcellation is one of the core steps for identifying the functional architecture of the human brain. Despite the increasing number of attempts at developing parcellation algorithms using resting-state fMRI, there still remain challenges to be overcome, such as generating reproducible parcellations at both single-subject and group levels, while sub-dividing the cortex into functionally homogeneous parcels. To address these challenges, we propose a three-layer parcellation framework which deploys a different clustering strategy at each layer. Initially, the cortical vertices are clustered into a relatively large number of supervertices, which constitutes a high-level abstraction of the rs-fMRI data. These supervertices are combined into a tree of hierarchical clusters to generate individual subject parcellations, which are, in turn, used to compute a groupwise parcellation in order to represent the whole population. Using data collected as part of the Human Connectome Project from 100 healthy subjects, we show that our algorithm segregates the cortex into distinctive parcels at different resolutions with high reproducibility and functional homogeneity at both single-subject and group levels, therefore can be reliably used for network analysis.