Group-wise cortical parcellation based on structural connectivity and hierarchical clustering

• Main Authors: Molina, Joaquín, Mendoza, Cristóbal, Román, Claudio, Houenou, Josselin, Poupon, Cyril, Mangin, Jean François, El-Deredy, Wael, Hernández, Cecilia, Guevara, Pamela
• Format: Conference Proceeding
• Language: English
• Published: SPIE 06.03.2023
• ISBN: 9781510662544; 1510662545
• ISSN: 0277-786X
• DOI: 10.1117/12.2670138

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.