Abstract
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 multi-resolution entity. In this work, we propose the CoCoNest family of parcellations - a fully data-driven, multi-resolution family of parcellations constructed from structural connectome data. The CoCoNest family is constructed using agglomerative (bottom-up) clustering and error-complexity pruning, which strikes a balance between the complexity of the 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 multi-resolution organization of the structural connectome.
Author Summary
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 by 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 multi-scale nature of the structural connectome.
Author notes
Handling Editor: Marcus Kaiser
