Mapping Anatomical Connectivity Patterns of Human Cerebral Cortex Using In Vivo Diffusion Tensor Imaging Tractography

TLDR

The characterization of the topological architecture of complex networks underlying the structural and functional organization of the brain is a basic challenge in neuroscience, yet direct evidence for anatomical connectivity networks in the human brain remains scarce. We aimed to construct a macroscale anatomical network capturing the common connectivity pattern of the human cerebral cortex in 80 young adults using diffusion tensor imaging deterministic tractography and to analyze its topological properties with graph theory. The cortex was parcellated into 78 regions, with connections defined by a statistical threshold on fiber probability, and the resulting network was examined using graph theoretical metrics. The resulting binarized cortical network exhibited small‑world architecture with an exponentially truncated power‑law degree distribution, high resilience to localized damage, prominent association‑cortex hubs linked by long‑range bridges, and consistency with prior structural and functional network studies.

Abstract

The characterization of the topological architecture of complex networks underlying the structural and functional organization of the brain is a basic challenge in neuroscience. However, direct evidence for anatomical connectivity networks in the human brain remains scarce. Here, we utilized diffusion tensor imaging deterministic tractography to construct a macroscale anatomical network capturing the underlying common connectivity pattern of human cerebral cortex in a large sample of subjects (80 young adults) and further quantitatively analyzed its topological properties with graph theoretical approaches. The cerebral cortex was divided into 78 cortical regions, each representing a network node, and 2 cortical regions were considered connected if the probability of fiber connections exceeded a statistical criterion. The topological parameters of the established cortical network (binarized) resemble that of a "small-world" architecture characterized by an exponentially truncated power-law distribution. These characteristics imply high resilience to localized damage. Furthermore, this cortical network was characterized by major hub regions in association cortices that were connected by bridge connections following long-range white matter pathways. Our results are compatible with previous structural and functional brain networks studies and provide insight into the organizational principles of human brain anatomical networks that underlie functional states.