Mapping population-based structural connectomes

• Published in: NeuroImage (Orlando, Fla.) Vol. 172; pp. 130 - 145
• Main Authors: Zhang, Zhengwu, Descoteaux, Maxime, Zhang, Jingwen, Girard, Gabriel, Chamberland, Maxime, Dunson, David, Srivastava, Anuj, Zhu, Hongtu
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.05.2018; Elsevier Limited
• Subjects: Algorithms; Brain - diagnostic imaging; Brain connectome; Brain research; Connectome - methods; Cortex; Data processing; Diffusion MRI imaging; Functional principal component analysis; Heritability; Human connectome project; Humans; Image Processing, Computer-Assisted - methods; Methods; NMR; Nuclear magnetic resonance; Population; Population-based structural connectome; Streamline variation decomposition; Substantia alba; Substantia grisea; Brain connectome; Population-based structural connectome; Streamline variation decomposition; Human connectome project; Diffusion MRI imaging; Functional principal component analysis
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2017.12.064

Advances in understanding the structural connectomes of human brain require improved approaches for the construction, comparison and integration of high-dimensional whole-brain tractography data from a large number of individuals. This article develops a population-based structural connectome (PSC) mapping framework to address these challenges. PSC simultaneously characterizes a large number of white matter bundles within and across different subjects by registering different subjects’ brains based on coarse cortical parcellations, compressing the bundles of each connection, and extracting novel connection weights. A robust tractography algorithm and streamline post-processing techniques, including dilation of gray matter regions, streamline cutting, and outlier streamline removal are applied to improve the robustness of the extracted structural connectomes. The developed PSC framework can be used to reproducibly extract binary networks, weighted networks and streamline-based brain connectomes. We apply the PSC to Human Connectome Project data to illustrate its application in characterizing normal variations and heritability of structural connectomes in healthy subjects. •Construction, comparison and integration of high-dimensional whole-brain tractographic data.•Extract binary networks, weighted networks and streamline-based connectivity representations of brain connectomes.•Relating structural connectomes to demographic and behavioral measures.•A test-retest dataset for validation.•A comprehensive Human Connectome Project (HCP) data analysis results.•The package for PSC, along with its documentation, is freely accessible from the nitric and bigs2 websites.