Coidentification of group-level hole structures in brain networks via hodge laplacian

Hyekyoung Lee; Moo K. Chung; Hyejin Kang; Hongyoon Choi; Seunggyun Ha; Youngmin Huh; Eunkyung Kim; Dong Soo Lee

doi:10.1007/978-3-030-32251-9_74

Coidentification of group-level hole structures in brain networks via hodge laplacian

Hyekyoung Lee
, Moo K. Chung
, Hyejin Kang
, Hongyoon Choi
, Seunggyun Ha
, Youngmin Huh
, Eunkyung Kim
, Dong Soo Lee

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

6 Scopus citations

Abstract

One of outstanding issues in brain network analysis is to extract common topological substructure shared by a group of individuals. Recently, methods to detect group-wise modular structure on graph Laplacians have been introduced. From the perspective of algebraic topology, the modules or clusters are the zeroth topology information of a topological space. Higher order topology information can be found in holes. In this study, we extend the concept of graph Laplacian to higher order Hodge Laplacian of weighted networks, and develop a group-level hole identification method via the Stiefel optimization. In experiments, we applied the proposed method to three synthetic data and Alzheimer’s disease neuroimaing initiative (ADNI) database. Experimental results showed that the coidentification of group-level hole structures helped to find the underlying topology information of brain networks that discriminate groups well.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
Editors	Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	674-682
Number of pages	9
ISBN (Print)	9783030322502
DOIs	https://doi.org/10.1007/978-3-030-32251-9_74
State	Published - 2019
Event	22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 13 Oct 2019 → 17 Oct 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11767 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Country/Territory	China
City	Shenzhen
Period	13/10/19 → 17/10/19

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

Keywords

ADNI
Group analysis
Hodge Laplacian
Hole structure
Stiefel optimization

Access to Document

10.1007/978-3-030-32251-9_74

Cite this

Lee, H., Chung, M. K., Kang, H., Choi, H., Ha, S., Huh, Y., Kim, E., & Lee, D. S. (2019). Coidentification of group-level hole structures in brain networks via hodge laplacian. In D. Shen, P.-T. Yap, T. Liu, T. M. Peters, A. Khan, L. H. Staib, C. Essert, & S. Zhou (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings (pp. 674-682). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11767 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-32251-9_74

Lee, Hyekyoung ; Chung, Moo K. ; Kang, Hyejin et al. / Coidentification of group-level hole structures in brain networks via hodge laplacian. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. editor / Dinggang Shen ; Pew-Thian Yap ; Tianming Liu ; Terry M. Peters ; Ali Khan ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou. Springer Science and Business Media Deutschland GmbH, 2019. pp. 674-682 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "One of outstanding issues in brain network analysis is to extract common topological substructure shared by a group of individuals. Recently, methods to detect group-wise modular structure on graph Laplacians have been introduced. From the perspective of algebraic topology, the modules or clusters are the zeroth topology information of a topological space. Higher order topology information can be found in holes. In this study, we extend the concept of graph Laplacian to higher order Hodge Laplacian of weighted networks, and develop a group-level hole identification method via the Stiefel optimization. In experiments, we applied the proposed method to three synthetic data and Alzheimer{\textquoteright}s disease neuroimaing initiative (ADNI) database. Experimental results showed that the coidentification of group-level hole structures helped to find the underlying topology information of brain networks that discriminate groups well.",

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author = "Hyekyoung Lee and Chung, \{Moo K.\} and Hyejin Kang and Hongyoon Choi and Seunggyun Ha and Youngmin Huh and Eunkyung Kim and Lee, \{Dong Soo\}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 17-10-2019",

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Lee, H, Chung, MK, Kang, H, Choi, H, Ha, S, Huh, Y, Kim, E & Lee, DS 2019, Coidentification of group-level hole structures in brain networks via hodge laplacian. in D Shen, P-T Yap, T Liu, TM Peters, A Khan, LH Staib, C Essert & S Zhou (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11767 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 674-682, 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, China, 13/10/19. https://doi.org/10.1007/978-3-030-32251-9_74

Coidentification of group-level hole structures in brain networks via hodge laplacian. / Lee, Hyekyoung; Chung, Moo K.; Kang, Hyejin et al.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. ed. / Dinggang Shen; Pew-Thian Yap; Tianming Liu; Terry M. Peters; Ali Khan; Lawrence H. Staib; Caroline Essert; Sean Zhou. Springer Science and Business Media Deutschland GmbH, 2019. p. 674-682 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11767 LNCS).

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

TY - GEN

T1 - Coidentification of group-level hole structures in brain networks via hodge laplacian

AU - Lee, Hyekyoung

AU - Chung, Moo K.

AU - Kang, Hyejin

AU - Choi, Hongyoon

AU - Ha, Seunggyun

AU - Huh, Youngmin

AU - Kim, Eunkyung

AU - Lee, Dong Soo

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

N2 - One of outstanding issues in brain network analysis is to extract common topological substructure shared by a group of individuals. Recently, methods to detect group-wise modular structure on graph Laplacians have been introduced. From the perspective of algebraic topology, the modules or clusters are the zeroth topology information of a topological space. Higher order topology information can be found in holes. In this study, we extend the concept of graph Laplacian to higher order Hodge Laplacian of weighted networks, and develop a group-level hole identification method via the Stiefel optimization. In experiments, we applied the proposed method to three synthetic data and Alzheimer’s disease neuroimaing initiative (ADNI) database. Experimental results showed that the coidentification of group-level hole structures helped to find the underlying topology information of brain networks that discriminate groups well.

AB - One of outstanding issues in brain network analysis is to extract common topological substructure shared by a group of individuals. Recently, methods to detect group-wise modular structure on graph Laplacians have been introduced. From the perspective of algebraic topology, the modules or clusters are the zeroth topology information of a topological space. Higher order topology information can be found in holes. In this study, we extend the concept of graph Laplacian to higher order Hodge Laplacian of weighted networks, and develop a group-level hole identification method via the Stiefel optimization. In experiments, we applied the proposed method to three synthetic data and Alzheimer’s disease neuroimaing initiative (ADNI) database. Experimental results showed that the coidentification of group-level hole structures helped to find the underlying topology information of brain networks that discriminate groups well.

KW - ADNI

KW - Group analysis

KW - Hodge Laplacian

KW - Hole structure

KW - Stiefel optimization

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DO - 10.1007/978-3-030-32251-9_74

M3 - Conference contribution

AN - SCOPUS:85075686104

SN - 9783030322502

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

SP - 674

EP - 682

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings

A2 - Shen, Dinggang

A2 - Yap, Pew-Thian

A2 - Liu, Tianming

A2 - Peters, Terry M.

A2 - Khan, Ali

A2 - Staib, Lawrence H.

A2 - Essert, Caroline

A2 - Zhou, Sean

PB - Springer Science and Business Media Deutschland GmbH

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ER -

Lee H, Chung MK, Kang H, Choi H, Ha S, Huh Y et al. Coidentification of group-level hole structures in brain networks via hodge laplacian. In Shen D, Yap PT, Liu T, Peters TM, Khan A, Staib LH, Essert C, Zhou S, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2019. p. 674-682. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-32251-9_74

Coidentification of group-level hole structures in brain networks via hodge laplacian

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Keywords

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