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 proceedingConference contribution

4 Citations (Scopus)

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 languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages169-176
Number of pages8
Volume9350
ISBN (Print)9783319245706, 9783319245706, 9783319245706
DOIs
StatePublished - 2015
Event18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: Oct 5 2015Oct 9 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9350
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
CountryGermany
CityMunich
Period10/5/1510/9/15

Fingerprint

Multigraph
Brain
Module
Computational Neuroscience
Graph Clustering
Network Connectivity
Biological Networks
Clustering Methods
Complex Networks
Fibers
Connectivity
Fiber

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Gao, H., Cai, C., Yan, J., Yan, L., Cortes, J. G., Wang, Y., ... Huang, H. (2015). Identifying connectome module patterns via new balanced multi-graph normalized cut. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9350, 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

Identifying connectome module patterns via new balanced multi-graph normalized cut. / Gao, Hongchang; Cai, Chengtao; Yan, Jingwen; Yan, Lin; Cortes, Joaquin Goni; Wang, Yang; Nie, Feiping; West, John; Saykin, Andrew; Shen, Li; Huang, Heng.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9350 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 proceedingConference contribution

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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9350, 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, 10/5/15. https://doi.org/10.1007/978-3-319-24571-3_21
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 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9350. Springer Verlag. 2015. p. 169-176. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-24571-3_21
Gao, Hongchang ; Cai, Chengtao ; Yan, Jingwen ; Yan, Lin ; Cortes, Joaquin Goni ; Wang, Yang ; Nie, Feiping ; West, John ; Saykin, Andrew ; Shen, Li ; Huang, Heng. / Identifying connectome module patterns via new balanced multi-graph normalized cut. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9350 Springer Verlag, 2015. pp. 169-176 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{d13daf490f0f4245a348990683662a51,
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",
year = "2015",
doi = "10.1007/978-3-319-24571-3_21",
language = "English (US)",
isbn = "9783319245706",
volume = "9350",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "169--176",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

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

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.

UR - http://www.scopus.com/inward/record.url?scp=84951097681&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84951097681&partnerID=8YFLogxK

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

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

M3 - Conference contribution

C2 - 26525952

AN - SCOPUS:84951097681

SN - 9783319245706

SN - 9783319245706

SN - 9783319245706

VL - 9350

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

SP - 169

EP - 176

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

PB - Springer Verlag

ER -