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Neuroimage. 2018 May 15;172:130-145. doi: 10.1016/j.neuroimage.2017.12.064. Epub 2018 Feb 3.

Mapping population-based structural connectomes.

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Department of Biostatistics and Computational Biology, Rochester, NY, USA; Statistical and Applied Mathematical Sciences Institute, Durham, NC, USA.
Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada.
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Department of Statistical Science, Duke University, Durham, NC, USA.
Department of Statistics, Florida State University, Tallahassee, FL, USA.
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Electronic address:


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.


Brain connectome; Diffusion MRI imaging; Functional principal component analysis; Human connectome project; Population-based structural connectome; Streamline variation decomposition

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