Mapping population-based structural connectomes

Zhengwu Zhang; Maxime Descoteaux; Jingwen Zhang; Gabriel Girard; Maxime Chamberland; David Dunson; Anuj Srivastava; Hongtu Zhu

doi:10.1016/j.neuroimage.2017.12.064

Mapping population-based structural connectomes

Zhengwu Zhang
, Maxime Descoteaux
, Jingwen Zhang
, Gabriel Girard
, Maxime Chamberland
, David Dunson
, Anuj Srivastava
, Hongtu Zhu

Research output: Contribution to journal › Article › Academic › peer-review

70 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	130-145
Number of pages	16
Journal	Neuroimage
Volume	172
DOIs	https://doi.org/10.1016/j.neuroimage.2017.12.064
Publication status	Published - 15 May 2018
Externally published	Yes

Bibliographical note

Funding Information:
This material was based on work partially supported by the NSF grant DMS-1127914 to the Statistical and Applied Mathematical Science Institute. Dr. Zhu's work was partially supported by NIH grants MH086633 and MH092335 , NSF grants SES-1357666 and DMS-1407655 , and a grant from the Cancer Prevention Research Institute of Texas grants RR150054 . Dr. Descoteaux's research is partially supported by NSERC and his institutional research chair in NeuroInformatics at Université de Sherbrooke. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or any other funding agency. Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657). We also thank Kevin Whittingstall and the Sherbrooke Molecular Imaging Center for the acquisition of the test-retest data.

Funding Information:
This material was based on work partially supported by the NSF grant DMS-1127914 to the Statistical and Applied Mathematical Science Institute. Dr. Zhu's work was partially supported by NIH grants MH086633 and MH092335, NSF grants SES-1357666 and DMS-1407655, and a grant from the Cancer Prevention Research Institute of Texas grants RR150054. Dr. Descoteaux's research is partially supported by NSERC and his institutional research chair in NeuroInformatics at Université de Sherbrooke. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or any other funding agency. Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657). We also thank Kevin Whittingstall and the Sherbrooke Molecular Imaging Center for the acquisition of the test-retest data.

Publisher Copyright:
© 2018 Elsevier Inc.

Funding

This material was based on work partially supported by the NSF grant DMS-1127914 to the Statistical and Applied Mathematical Science Institute. Dr. Zhu's work was partially supported by NIH grants MH086633 and MH092335 , NSF grants SES-1357666 and DMS-1407655 , and a grant from the Cancer Prevention Research Institute of Texas grants RR150054 . Dr. Descoteaux's research is partially supported by NSERC and his institutional research chair in NeuroInformatics at Université de Sherbrooke. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or any other funding agency. Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657). We also thank Kevin Whittingstall and the Sherbrooke Molecular Imaging Center for the acquisition of the test-retest data. This material was based on work partially supported by the NSF grant DMS-1127914 to the Statistical and Applied Mathematical Science Institute. Dr. Zhu's work was partially supported by NIH grants MH086633 and MH092335, NSF grants SES-1357666 and DMS-1407655, and a grant from the Cancer Prevention Research Institute of Texas grants RR150054. Dr. Descoteaux's research is partially supported by NSERC and his institutional research chair in NeuroInformatics at Université de Sherbrooke. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or any other funding agency. Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657). We also thank Kevin Whittingstall and the Sherbrooke Molecular Imaging Center for the acquisition of the test-retest data.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

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

Access to Document

10.1016/j.neuroimage.2017.12.064

Cite this

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title = "Mapping population-based structural connectomes",

abstract = "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{\textquoteright} 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.",

keywords = "Brain connectome, Diffusion MRI imaging, Functional principal component analysis, Human connectome project, Population-based structural connectome, Streamline variation decomposition",

author = "Zhengwu Zhang and Maxime Descoteaux and Jingwen Zhang and Gabriel Girard and Maxime Chamberland and David Dunson and Anuj Srivastava and Hongtu Zhu",

note = "Funding Information: This material was based on work partially supported by the NSF grant DMS-1127914 to the Statistical and Applied Mathematical Science Institute. Dr. Zhu's work was partially supported by NIH grants MH086633 and MH092335 , NSF grants SES-1357666 and DMS-1407655 , and a grant from the Cancer Prevention Research Institute of Texas grants RR150054 . Dr. Descoteaux's research is partially supported by NSERC and his institutional research chair in NeuroInformatics at Universit{\'e} de Sherbrooke. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or any other funding agency. Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657). We also thank Kevin Whittingstall and the Sherbrooke Molecular Imaging Center for the acquisition of the test-retest data. Funding Information: This material was based on work partially supported by the NSF grant DMS-1127914 to the Statistical and Applied Mathematical Science Institute. Dr. Zhu's work was partially supported by NIH grants MH086633 and MH092335, NSF grants SES-1357666 and DMS-1407655, and a grant from the Cancer Prevention Research Institute of Texas grants RR150054. Dr. Descoteaux's research is partially supported by NSERC and his institutional research chair in NeuroInformatics at Universit{\'e} de Sherbrooke. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or any other funding agency. Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657). We also thank Kevin Whittingstall and the Sherbrooke Molecular Imaging Center for the acquisition of the test-retest data. Publisher Copyright: {\textcopyright} 2018 Elsevier Inc.",

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AU - Zhang, Zhengwu

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AU - Zhang, Jingwen

AU - Girard, Gabriel

AU - Chamberland, Maxime

AU - Dunson, David

AU - Srivastava, Anuj

AU - Zhu, Hongtu

N1 - Funding Information: This material was based on work partially supported by the NSF grant DMS-1127914 to the Statistical and Applied Mathematical Science Institute. Dr. Zhu's work was partially supported by NIH grants MH086633 and MH092335 , NSF grants SES-1357666 and DMS-1407655 , and a grant from the Cancer Prevention Research Institute of Texas grants RR150054 . Dr. Descoteaux's research is partially supported by NSERC and his institutional research chair in NeuroInformatics at Université de Sherbrooke. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or any other funding agency. Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657). We also thank Kevin Whittingstall and the Sherbrooke Molecular Imaging Center for the acquisition of the test-retest data. Funding Information: This material was based on work partially supported by the NSF grant DMS-1127914 to the Statistical and Applied Mathematical Science Institute. Dr. Zhu's work was partially supported by NIH grants MH086633 and MH092335, NSF grants SES-1357666 and DMS-1407655, and a grant from the Cancer Prevention Research Institute of Texas grants RR150054. Dr. Descoteaux's research is partially supported by NSERC and his institutional research chair in NeuroInformatics at Université de Sherbrooke. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or any other funding agency. Data were provided in part by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657). We also thank Kevin Whittingstall and the Sherbrooke Molecular Imaging Center for the acquisition of the test-retest data. Publisher Copyright: © 2018 Elsevier Inc.

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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.

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KW - Diffusion MRI imaging

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KW - Streamline variation decomposition

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DO - 10.1016/j.neuroimage.2017.12.064

M3 - Article

C2 - 29355769

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VL - 172

SP - 130

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JF - Neuroimage

ER -

Mapping population-based structural connectomes

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

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