TY - JOUR
T1 - Mapping population-based structural connectomes
AU - Zhang, Zhengwu
AU - Descoteaux, Maxime
AU - Zhang, Jingwen
AU - Girard, Gabriel
AU - Chamberland, Maxime
AU - Dunson, David
AU - Srivastava, Anuj
AU - Zhu, Hongtu
N1 - Publisher Copyright:
© 2018 Elsevier Inc.
PY - 2018/5/15
Y1 - 2018/5/15
N2 - 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.
AB - 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.
KW - Brain connectome
KW - Diffusion MRI imaging
KW - Functional principal component analysis
KW - Human connectome project
KW - Population-based structural connectome
KW - Streamline variation decomposition
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U2 - 10.1016/j.neuroimage.2017.12.064
DO - 10.1016/j.neuroimage.2017.12.064
M3 - Article
C2 - 29355769
AN - SCOPUS:85043326621
SN - 1053-8119
VL - 172
SP - 130
EP - 145
JO - NeuroImage
JF - NeuroImage
ER -