Conserved and variable architecture of human white matter connectivity

• Published in: NeuroImage (Orlando, Fla.) Vol. 54; no. 2; pp. 1262 - 1279
• Main Authors: Bassett, Danielle S., Brown, Jesse A., Deshpande, Vibhas, Carlson, Jean M., Grafton, Scott T.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.01.2011; Elsevier Limited
• Subjects: Architecture; Brain; Brain - anatomy & histology; Brain Mapping; Cognitive ability; Complex network analysis; Diffusion imaging; Diffusion Magnetic Resonance Imaging; Hierarchical modularity; Humans; Image Processing, Computer-Assisted - methods; Magnetic resonance imaging; Neural networks; Neural Pathways - anatomy & histology; Neuroimaging; Rentian scaling; Reproducibility; Software; Substantia alba; Young Adult; Young adults; Complex network analysis; Diffusion imaging; Hierarchical modularity; Rentian scaling; Reproducibility
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2010.09.006

Whole-brain network analysis of diffusion imaging tractography data is an important new tool for quantification of differential connectivity patterns across individuals and between groups. Here we investigate both the conservation of network architectural properties across methodological variation and the reproducibility of individual architecture across multiple scanning sessions. Diffusion spectrum imaging (DSI) and diffusion tensor imaging (DTI) data were both acquired in triplicate from a cohort of healthy young adults. Deterministic tractography was performed on each dataset and inter-regional connectivity matrices were then derived by applying each of three widely used whole-brain parcellation schemes over a range of spatial resolutions. Across acquisitions and preprocessing streams, anatomical brain networks were found to be sparsely connected, hierarchical, and assortative. They also displayed signatures of topo-physical interdependence such as Rentian scaling. Basic connectivity properties and several graph metrics consistently displayed high reproducibility and low variability in both DSI and DTI networks. The relative increased sensitivity of DSI to complex fiber configurations was evident in increased tract counts and network density compared with DTI. In combination, this pattern of results shows that network analysis of human white matter connectivity provides sensitive and temporally stable topological and physical estimates of individual cortical structure across multiple spatial scales. ► Uncover conserved architectural principles of human white matter connectivity. ► Describe relationships between topological and physical organization of connectome. ► Characterize reproducibility of network properties over multiple scanning sessions. ► Examine reproducibility as a function of acquisition, parcellation, and resolution.