Multi-way regression reveals backbone of macaque structural brain connectivity in longitudinal datasets

Tuo Zhang; Xiao Li; Lin Zhao; Xintao Hu; Tianming Liu; Lei Guo

doi:10.1007/978-3-319-66182-7_49

Multi-way regression reveals backbone of macaque structural brain connectivity in longitudinal datasets

Tuo Zhang, Xiao Li, Lin Zhao, Xintao Hu, Tianming Liu, Lei Guo

School of Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Brain development has been an intriguing window to study the dynamic formation of brain features at a variety of scales, such as cortical convolution and white matter wiring diagram. However, recent studies only focused on a few specific fasciculus or several region. Very few studies focused on the development of macro-scale wiring diagrams due to the lack of longitudinal datasets and associated methods. In this work, we take the advantage of recently released longitudinal macaque MRI and DTI datasets and develop a novel multi-way regression method to model such datasets. By this method, we extract the backbone of structural connectome of macaque brains and study the trajectory of its development over time. Graphic statistics of these backbone connectomes demonstrate their soundness. Our findings are consistent with the reports in the literatures, suggesting the effectiveness and promise of this framework.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
Editors	Maxime Descoteaux, Simon Duchesne, Alfred Franz, Pierre Jannin, D. Louis Collins, Lena Maier-Hein
Publisher	Springer Verlag
Pages	424-432
Number of pages	9
ISBN (Print)	9783319661810
DOIs	https://doi.org/10.1007/978-3-319-66182-7_49
State	Published - 2017
Event	20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada Duration: 11 Sep 2017 → 13 Sep 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10433 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
Country/Territory	Canada
City	Quebec City
Period	11/09/17 → 13/09/17

Keywords

Longitudinal study
Macaque brain connectivity
Multiway regression

Access to Document

10.1007/978-3-319-66182-7_49

Cite this

Zhang, T., Li, X., Zhao, L., Hu, X., Liu, T., & Guo, L. (2017). Multi-way regression reveals backbone of macaque structural brain connectivity in longitudinal datasets. In M. Descoteaux, S. Duchesne, A. Franz, P. Jannin, D. L. Collins, & L. Maier-Hein (Eds.), Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings (pp. 424-432). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10433 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-66182-7_49

Zhang, Tuo ; Li, Xiao ; Zhao, Lin et al. / Multi-way regression reveals backbone of macaque structural brain connectivity in longitudinal datasets. Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. editor / Maxime Descoteaux ; Simon Duchesne ; Alfred Franz ; Pierre Jannin ; D. Louis Collins ; Lena Maier-Hein. Springer Verlag, 2017. pp. 424-432 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Brain development has been an intriguing window to study the dynamic formation of brain features at a variety of scales, such as cortical convolution and white matter wiring diagram. However, recent studies only focused on a few specific fasciculus or several region. Very few studies focused on the development of macro-scale wiring diagrams due to the lack of longitudinal datasets and associated methods. In this work, we take the advantage of recently released longitudinal macaque MRI and DTI datasets and develop a novel multi-way regression method to model such datasets. By this method, we extract the backbone of structural connectome of macaque brains and study the trajectory of its development over time. Graphic statistics of these backbone connectomes demonstrate their soundness. Our findings are consistent with the reports in the literatures, suggesting the effectiveness and promise of this framework.",

keywords = "Longitudinal study, Macaque brain connectivity, Multiway regression",

author = "Tuo Zhang and Xiao Li and Lin Zhao and Xintao Hu and Tianming Liu and Lei Guo",

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Zhang, T, Li, X, Zhao, L, Hu, X, Liu, T & Guo, L 2017, Multi-way regression reveals backbone of macaque structural brain connectivity in longitudinal datasets. in M Descoteaux, S Duchesne, A Franz, P Jannin, DL Collins & L Maier-Hein (eds), Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10433 LNCS, Springer Verlag, pp. 424-432, 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, Quebec City, Canada, 11/09/17. https://doi.org/10.1007/978-3-319-66182-7_49

Multi-way regression reveals backbone of macaque structural brain connectivity in longitudinal datasets. / Zhang, Tuo; Li, Xiao; Zhao, Lin et al.
Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. ed. / Maxime Descoteaux; Simon Duchesne; Alfred Franz; Pierre Jannin; D. Louis Collins; Lena Maier-Hein. Springer Verlag, 2017. p. 424-432 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10433 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Multi-way regression reveals backbone of macaque structural brain connectivity in longitudinal datasets

AU - Zhang, Tuo

AU - Li, Xiao

AU - Zhao, Lin

AU - Hu, Xintao

AU - Liu, Tianming

AU - Guo, Lei

PY - 2017

Y1 - 2017

N2 - Brain development has been an intriguing window to study the dynamic formation of brain features at a variety of scales, such as cortical convolution and white matter wiring diagram. However, recent studies only focused on a few specific fasciculus or several region. Very few studies focused on the development of macro-scale wiring diagrams due to the lack of longitudinal datasets and associated methods. In this work, we take the advantage of recently released longitudinal macaque MRI and DTI datasets and develop a novel multi-way regression method to model such datasets. By this method, we extract the backbone of structural connectome of macaque brains and study the trajectory of its development over time. Graphic statistics of these backbone connectomes demonstrate their soundness. Our findings are consistent with the reports in the literatures, suggesting the effectiveness and promise of this framework.

AB - Brain development has been an intriguing window to study the dynamic formation of brain features at a variety of scales, such as cortical convolution and white matter wiring diagram. However, recent studies only focused on a few specific fasciculus or several region. Very few studies focused on the development of macro-scale wiring diagrams due to the lack of longitudinal datasets and associated methods. In this work, we take the advantage of recently released longitudinal macaque MRI and DTI datasets and develop a novel multi-way regression method to model such datasets. By this method, we extract the backbone of structural connectome of macaque brains and study the trajectory of its development over time. Graphic statistics of these backbone connectomes demonstrate their soundness. Our findings are consistent with the reports in the literatures, suggesting the effectiveness and promise of this framework.

KW - Longitudinal study

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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A2 - Descoteaux, Maxime

A2 - Duchesne, Simon

A2 - Franz, Alfred

A2 - Jannin, Pierre

A2 - Collins, D. Louis

A2 - Maier-Hein, Lena

PB - Springer Verlag

T2 - 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017

Y2 - 11 September 2017 through 13 September 2017

ER -

Zhang T, Li X, Zhao L, Hu X, Liu T, Guo L. Multi-way regression reveals backbone of macaque structural brain connectivity in longitudinal datasets. In Descoteaux M, Duchesne S, Franz A, Jannin P, Collins DL, Maier-Hein L, editors, Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings. Springer Verlag. 2017. p. 424-432. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-66182-7_49

Multi-way regression reveals backbone of macaque structural brain connectivity in longitudinal datasets

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Keywords

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