Identifying connectome module patterns via new balanced multi-graph normalized cut

Hongchang Gao; Chengtao Cai; Jingwen Yan; Lin Yan; Joaquin Goni Cortes; Yang Wang; Feiping Nie; John West; Andrew Saykin; Li Shen; Heng Huang

doi:10.1007/978-3-319-24571-3_21

Identifying connectome module patterns via new balanced multi-graph normalized cut

Hongchang Gao, Chengtao Cai, Jingwen Yan, Lin Yan, Joaquin Goni Cortes, Yang Wang, Feiping Nie, John West, Andrew Saykin, Li Shen, Heng Huang

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

5 Scopus citations

Abstract

Computational tools for the analysis of complex biological networks are lacking in human connectome research. Especially, how to discover the brain network patterns shared by a group of subjects is a challenging computational neuroscience problem. Although some single graph clustering methods can be extended to solve the multi-graph cases, the discovered network patterns are often imbalanced, e.g. isolated points. To address these problems, we propose a novel indicator constrained and balanced multi-graph normalized cut method to identify the connectome module patterns from the connectivity brain networks of the targeted subject group. We evaluated our method by analyzing the weighted fiber connectivity networks.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings
Editors	Joachim Hornegger, Alejandro F. Frangi, William M. Wells, Alejandro F. Frangi, Nassir Navab, Joachim Hornegger, Nassir Navab, William M. Wells, William M. Wells, Alejandro F. Frangi, Joachim Hornegger, Nassir Navab
Publisher	Springer Verlag
Pages	169-176
Number of pages	8
ISBN (Print)	9783319245706, 9783319245706, 9783319245706
DOIs	https://doi.org/10.1007/978-3-319-24571-3_21
State	Published - 2015
Externally published	Yes
Event	18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany Duration: 5 Oct 2015 → 9 Oct 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9350
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
Country/Territory	Germany
City	Munich
Period	5/10/15 → 9/10/15

Access to Document

10.1007/978-3-319-24571-3_21

Cite this

Gao, H., Cai, C., Yan, J., Yan, L., Cortes, J. G., Wang, Y., Nie, F., West, J., Saykin, A., Shen, L., & Huang, H. (2015). Identifying connectome module patterns via new balanced multi-graph normalized cut. In J. Hornegger, A. F. Frangi, W. M. Wells, A. F. Frangi, N. Navab, J. Hornegger, N. Navab, W. M. Wells, W. M. Wells, A. F. Frangi, J. Hornegger, & N. Navab (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings (pp. 169-176). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9350). Springer Verlag. https://doi.org/10.1007/978-3-319-24571-3_21

Gao, Hongchang ; Cai, Chengtao ; Yan, Jingwen et al. / Identifying connectome module patterns via new balanced multi-graph normalized cut. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings. editor / Joachim Hornegger ; Alejandro F. Frangi ; William M. Wells ; Alejandro F. Frangi ; Nassir Navab ; Joachim Hornegger ; Nassir Navab ; William M. Wells ; William M. Wells ; Alejandro F. Frangi ; Joachim Hornegger ; Nassir Navab. Springer Verlag, 2015. pp. 169-176 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{48ef8b44acd04654bc0e623f696ec5cc,

title = "Identifying connectome module patterns via new balanced multi-graph normalized cut",

abstract = "Computational tools for the analysis of complex biological networks are lacking in human connectome research. Especially, how to discover the brain network patterns shared by a group of subjects is a challenging computational neuroscience problem. Although some single graph clustering methods can be extended to solve the multi-graph cases, the discovered network patterns are often imbalanced, e.g. isolated points. To address these problems, we propose a novel indicator constrained and balanced multi-graph normalized cut method to identify the connectome module patterns from the connectivity brain networks of the targeted subject group. We evaluated our method by analyzing the weighted fiber connectivity networks.",

author = "Hongchang Gao and Chengtao Cai and Jingwen Yan and Lin Yan and Cortes, {Joaquin Goni} and Yang Wang and Feiping Nie and John West and Andrew Saykin and Li Shen and Heng Huang",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 ; Conference date: 05-10-2015 Through 09-10-2015",

year = "2015",

doi = "10.1007/978-3-319-24571-3_21",

language = "英语",

isbn = "9783319245706",

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

publisher = "Springer Verlag",

pages = "169--176",

editor = "Joachim Hornegger and Frangi, {Alejandro F.} and Wells, {William M.} and Frangi, {Alejandro F.} and Nassir Navab and Joachim Hornegger and Nassir Navab and Wells, {William M.} and Wells, {William M.} and Frangi, {Alejandro F.} and Joachim Hornegger and Nassir Navab",

booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings",

}

Gao, H, Cai, C, Yan, J, Yan, L, Cortes, JG, Wang, Y, Nie, F, West, J, Saykin, A, Shen, L & Huang, H 2015, Identifying connectome module patterns via new balanced multi-graph normalized cut. in J Hornegger, AF Frangi, WM Wells, AF Frangi, N Navab, J Hornegger, N Navab, WM Wells, WM Wells, AF Frangi, J Hornegger & N Navab (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9350, Springer Verlag, pp. 169-176, 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, Munich, Germany, 5/10/15. https://doi.org/10.1007/978-3-319-24571-3_21

Identifying connectome module patterns via new balanced multi-graph normalized cut. / Gao, Hongchang; Cai, Chengtao; Yan, Jingwen et al.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings. ed. / Joachim Hornegger; Alejandro F. Frangi; William M. Wells; Alejandro F. Frangi; Nassir Navab; Joachim Hornegger; Nassir Navab; William M. Wells; William M. Wells; Alejandro F. Frangi; Joachim Hornegger; Nassir Navab. Springer Verlag, 2015. p. 169-176 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9350).

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

TY - GEN

T1 - Identifying connectome module patterns via new balanced multi-graph normalized cut

AU - Gao, Hongchang

AU - Cai, Chengtao

AU - Yan, Jingwen

AU - Yan, Lin

AU - Cortes, Joaquin Goni

AU - Wang, Yang

AU - Nie, Feiping

AU - West, John

AU - Saykin, Andrew

AU - Shen, Li

AU - Huang, Heng

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - Computational tools for the analysis of complex biological networks are lacking in human connectome research. Especially, how to discover the brain network patterns shared by a group of subjects is a challenging computational neuroscience problem. Although some single graph clustering methods can be extended to solve the multi-graph cases, the discovered network patterns are often imbalanced, e.g. isolated points. To address these problems, we propose a novel indicator constrained and balanced multi-graph normalized cut method to identify the connectome module patterns from the connectivity brain networks of the targeted subject group. We evaluated our method by analyzing the weighted fiber connectivity networks.

AB - Computational tools for the analysis of complex biological networks are lacking in human connectome research. Especially, how to discover the brain network patterns shared by a group of subjects is a challenging computational neuroscience problem. Although some single graph clustering methods can be extended to solve the multi-graph cases, the discovered network patterns are often imbalanced, e.g. isolated points. To address these problems, we propose a novel indicator constrained and balanced multi-graph normalized cut method to identify the connectome module patterns from the connectivity brain networks of the targeted subject group. We evaluated our method by analyzing the weighted fiber connectivity networks.

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U2 - 10.1007/978-3-319-24571-3_21

DO - 10.1007/978-3-319-24571-3_21

M3 - 会议稿件

C2 - 26525952

AN - SCOPUS:84951097681

SN - 9783319245706

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

SP - 169

EP - 176

BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings

A2 - Hornegger, Joachim

A2 - Frangi, Alejandro F.

A2 - Wells, William M.

A2 - Frangi, Alejandro F.

A2 - Navab, Nassir

A2 - Hornegger, Joachim

A2 - Navab, Nassir

A2 - Wells, William M.

A2 - Frangi, Alejandro F.

A2 - Hornegger, Joachim

A2 - Navab, Nassir

PB - Springer Verlag

T2 - 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015

Y2 - 5 October 2015 through 9 October 2015

ER -

Gao H, Cai C, Yan J, Yan L, Cortes JG, Wang Y et al. Identifying connectome module patterns via new balanced multi-graph normalized cut. In Hornegger J, Frangi AF, Wells WM, Frangi AF, Navab N, Hornegger J, Navab N, Wells WM, Wells WM, Frangi AF, Hornegger J, Navab N, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings. Springer Verlag. 2015. p. 169-176. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-24571-3_21

Identifying connectome module patterns via new balanced multi-graph normalized cut

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