Group-Wise Cortical Parcellation Based on Structural Connectivity and Hierarchical Clustering

This paper presents a new cortical parcellation method based on group-wise connectivity and hierarchical clustering. A preliminary sub-parcellation is performed using intra-subject and inter-subject fiber clustering to obtain representative bundles among subjects with similar shapes and trajectories. The sub-parcellation is obtained by intersecting fiber clusters with cortical meshes. Next, mean connectivity and mean overlap matrices are computed over the sub-parcels to obtain spatial and connectivity information. To hierarchize the information, we propose to weight both matrices, to obtain an affinity graph, and then a dendrogram to merge or divide parcels by their hierarchy. Finally, to obtain homogeneous parcels, the method computes morphological operations. By selecting a different number of clusters over the dendrogram, the method obtains a different number of parcels and a variation in the resulting parcel sizes, depending on the parameters used. We computed the coefficient of variation (CV) of the parcel size to evaluate the homogeneity of the parcels. Preliminary results suggest that the use of representative clusters and the integration of sub-parcel overlap and connectivity strength provide useful information to generate cortical parcellations at different levels of granularity. Even results are preliminary, this novel method allows researchers to add group-wise connectivity strength and spatial information for the construction of diffusion-based parcellations. Future work will include a detailed analysis of parameters, such as the matrix weights and the number of sub-parcel clusters, and the generation of hierarchical parcellations to improve the insight into the cortex subdivision and hierarchy among parcels.

Automated summary

This paper presents a new cortical parcellation method based on group-wise connectivity and hierarchical clustering. It utilizes fiber clustering and cortical meshes to obtain representative bundles and sub-parcels. Mean connectivity and overlap matrices are computed to hierarchize the information, and morphological operations are used to obtain homogenous parcels. The method allows for the generation of diffusion-based parcellations with different levels of granularity by adjusting parameters.