CoCoNest: A Continuous Structural Connectivity-based Nested Parcellation of the Human Cerebral Cortex

Abstract

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.