CoCoNest: A continuous structural connectivity-based nested family of parcellations of the human cerebral cortex

Despite the widespread exploration and availability of parcellations for the functional connectome, parcellations designed for the structural connectome are comparatively limited. Current research suggests that there may be no single “correct” parcellation and that the human brain is intrinsically a multiresolution entity. In this work, we propose the Continuous Structural Connectivitity-based, Nested (CoCoNest) family of parcellations—a fully data-driven, multiresolution family of parcellations derived from structural connectome data. The CoCoNest family is created using agglomerative (bottom-up) clustering and error-complexity pruning, which strikes a balance between the complexity of each parcellation and how well it preserves patterns in vertex-level, high-resolution connectivity data. We draw on a comprehensive battery of internal and external evaluation metrics to show that the CoCoNest family is competitive with or outperforms widely used parcellations in the literature. Additionally, we show how the CoCoNest family can serve as an exploratory tool for researchers to investigate the multiresolution organization of the structural connectome.

In this work, we derive a family of structural connectivity-based parcellations, called the CoCoNest family, based on a continuous representation of structural connectivity. This family is created using agglomerative clustering to grow a fully binary tree and then error-complexity pruning to greedily derive subtrees that balance complexity and goodness of fit. The CoCoNest family is evaluated with a comprehensive collection of internal and external evaluation metrics across two independent datasets and compared with widely used parcellations. Our results show that members of the CoCoNest family are competitive with other parcellations and often achieve superior performance. We show that the CoCoNest family can serve as an exploratory tool for investigations into the multiscale nature of the structural connectome.