Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-task Regression

Tuo Zhang; Zhibin He; Xi Jiang; Lei Guo; Xiaoping Hu; Tianming Liu; Lei Du

doi:10.1007/978-3-030-59728-3_10

Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-task Regression

Tuo Zhang, Zhibin He, Xi Jiang, Lei Guo, Xiaoping Hu, Tianming Liu, Lei Du

School of Automation

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

1 Scopus citations

Abstract

Comparative studies across species, such as primates in this work, reveal the shared structural or functional patterns that might be inherited from the common ancestors and the specific ones that might be related to individualized evolution strategies. Both the shared or specific patterns could help promote the understanding of mechanisms of brain structural and functional architectures and brain dynamics. Many previous studies can be found to report the comparative results on species pairs. However, very few studies were found to perform a comparison of large-scale connectomes across multiple species via a data-driven method. To this end, we construct brain connectomes for three primates, macaque, chimpanzee and human, by using Brodmann areas as graphic nodes and diffusion MRI derived white matter fibers to define edges and edge weights. On these connectomes, a novel dirty multi-task regression method is developed in the attempt to automatically identified the species-shared and -specific connections. The concordance of the findings via our method and previous reports demonstrate the effectiveness and the promise of this framework.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
Editors	Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	94-103
Number of pages	10
ISBN (Print)	9783030597276
DOIs	https://doi.org/10.1007/978-3-030-59728-3_10
State	Published - 2020
Event	23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru Duration: 4 Oct 2020 → 8 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12267 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Country/Territory	Peru
City	Lima
Period	4/10/20 → 8/10/20

Keywords

Diffusion MRI
Large-scale connectome
Species comparison

Access to Document

10.1007/978-3-030-59728-3_10

Cite this

Zhang, T., He, Z., Jiang, X., Guo, L., Hu, X., Liu, T., & Du, L. (2020). Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-task Regression. In A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu, & L. Joskowicz (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings (pp. 94-103). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12267 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59728-3_10

Zhang, Tuo ; He, Zhibin ; Jiang, Xi et al. / Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-task Regression. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. editor / Anne L. Martel ; Purang Abolmaesumi ; Danail Stoyanov ; Diana Mateus ; Maria A. Zuluaga ; S. Kevin Zhou ; Daniel Racoceanu ; Leo Joskowicz. Springer Science and Business Media Deutschland GmbH, 2020. pp. 94-103 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-task Regression",

abstract = "Comparative studies across species, such as primates in this work, reveal the shared structural or functional patterns that might be inherited from the common ancestors and the specific ones that might be related to individualized evolution strategies. Both the shared or specific patterns could help promote the understanding of mechanisms of brain structural and functional architectures and brain dynamics. Many previous studies can be found to report the comparative results on species pairs. However, very few studies were found to perform a comparison of large-scale connectomes across multiple species via a data-driven method. To this end, we construct brain connectomes for three primates, macaque, chimpanzee and human, by using Brodmann areas as graphic nodes and diffusion MRI derived white matter fibers to define edges and edge weights. On these connectomes, a novel dirty multi-task regression method is developed in the attempt to automatically identified the species-shared and -specific connections. The concordance of the findings via our method and previous reports demonstrate the effectiveness and the promise of this framework.",

keywords = "Diffusion MRI, Large-scale connectome, Species comparison",

author = "Tuo Zhang and Zhibin He and Xi Jiang and Lei Guo and Xiaoping Hu and Tianming Liu and Lei Du",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 ; Conference date: 04-10-2020 Through 08-10-2020",

year = "2020",

doi = "10.1007/978-3-030-59728-3_10",

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isbn = "9783030597276",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "94--103",

editor = "Martel, {Anne L.} and Purang Abolmaesumi and Danail Stoyanov and Diana Mateus and Zuluaga, {Maria A.} and Zhou, {S. Kevin} and Daniel Racoceanu and Leo Joskowicz",

booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings",

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Zhang, T, He, Z, Jiang, X, Guo, L, Hu, X, Liu, T & Du, L 2020, Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-task Regression. in AL Martel, P Abolmaesumi, D Stoyanov, D Mateus, MA Zuluaga, SK Zhou, D Racoceanu & L Joskowicz (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12267 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 94-103, 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, Peru, 4/10/20. https://doi.org/10.1007/978-3-030-59728-3_10

Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-task Regression. / Zhang, Tuo; He, Zhibin; Jiang, Xi et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. ed. / Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov; Diana Mateus; Maria A. Zuluaga; S. Kevin Zhou; Daniel Racoceanu; Leo Joskowicz. Springer Science and Business Media Deutschland GmbH, 2020. p. 94-103 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12267 LNCS).

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

TY - GEN

T1 - Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-task Regression

AU - Zhang, Tuo

AU - He, Zhibin

AU - Jiang, Xi

AU - Guo, Lei

AU - Hu, Xiaoping

AU - Liu, Tianming

AU - Du, Lei

PY - 2020

Y1 - 2020

N2 - Comparative studies across species, such as primates in this work, reveal the shared structural or functional patterns that might be inherited from the common ancestors and the specific ones that might be related to individualized evolution strategies. Both the shared or specific patterns could help promote the understanding of mechanisms of brain structural and functional architectures and brain dynamics. Many previous studies can be found to report the comparative results on species pairs. However, very few studies were found to perform a comparison of large-scale connectomes across multiple species via a data-driven method. To this end, we construct brain connectomes for three primates, macaque, chimpanzee and human, by using Brodmann areas as graphic nodes and diffusion MRI derived white matter fibers to define edges and edge weights. On these connectomes, a novel dirty multi-task regression method is developed in the attempt to automatically identified the species-shared and -specific connections. The concordance of the findings via our method and previous reports demonstrate the effectiveness and the promise of this framework.

AB - Comparative studies across species, such as primates in this work, reveal the shared structural or functional patterns that might be inherited from the common ancestors and the specific ones that might be related to individualized evolution strategies. Both the shared or specific patterns could help promote the understanding of mechanisms of brain structural and functional architectures and brain dynamics. Many previous studies can be found to report the comparative results on species pairs. However, very few studies were found to perform a comparison of large-scale connectomes across multiple species via a data-driven method. To this end, we construct brain connectomes for three primates, macaque, chimpanzee and human, by using Brodmann areas as graphic nodes and diffusion MRI derived white matter fibers to define edges and edge weights. On these connectomes, a novel dirty multi-task regression method is developed in the attempt to automatically identified the species-shared and -specific connections. The concordance of the findings via our method and previous reports demonstrate the effectiveness and the promise of this framework.

KW - Diffusion MRI

KW - Large-scale connectome

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DO - 10.1007/978-3-030-59728-3_10

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SN - 9783030597276

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

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A2 - Martel, Anne L.

A2 - Abolmaesumi, Purang

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A2 - Zuluaga, Maria A.

A2 - Zhou, S. Kevin

A2 - Racoceanu, Daniel

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PB - Springer Science and Business Media Deutschland GmbH

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Zhang T, He Z, Jiang X, Guo L, Hu X, Liu T et al. Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-task Regression. In Martel AL, Abolmaesumi P, Stoyanov D, Mateus D, Zuluaga MA, Zhou SK, Racoceanu D, Joskowicz L, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 94-103. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-59728-3_10

Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-task Regression

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Keywords

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