Brain network decomposition by Auto Encoder (AE) and graph auto encoder (GAE)

Myungwon Choi; Pilsub Lee; Daegyeom Kim; Suji Lee; Hyun Chul Youn; Hyun-Ghang Jeong; Cheol E. Han

doi:10.1007/978-3-030-36708-4_47

Brain network decomposition by Auto Encoder (AE) and graph auto encoder (GAE)

Myungwon Choi
, Pilsub Lee
, Daegyeom Kim
, Suji Lee
, Hyun Chul Youn
, Hyun-Ghang Jeong
, Cheol E. Han^*

^*Corresponding author for this work

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

Abstract

Brain networks consist of nodes that are anatomically defined brain regions, and edges that connect a pair of brain regions. The diffusion-weighted magnetic resonance images and the advances in computer-aided tractography algorithms showed that human brain networks are strongly associated with cognitive functions. Brain regions dedicated to a specific cognitive function are spatially clustered and efficiently connected each other; this is called local functional segregation. However, it is not well known that such a local segregation is associated with sub-networks which may act as building blocks of brain networks. In this work, we used machine learning techniques to analyze brain networks. Specifically, using an auto-encoder and a graph auto-encoder, we decomposed brain networks into several essential building blocks, and compared their results through various measures of decomposition quality. We observed that the graph auto-encoder out-performed the auto-encoder, and that its results showed significant correlation with cognitive deterioration in Alzheimer’s disease.

Original language	English
Title of host publication	Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings
Editors	Tom Gedeon, Kok Wai Wong, Minho Lee
Publisher	Springer
Pages	568-579
Number of pages	12
ISBN (Print)	9783030367077
DOIs	https://doi.org/10.1007/978-3-030-36708-4_47
Publication status	Published - 2019
Event	26th International Conference on Neural Information Processing, ICONIP 2019 - Sydney, Australia Duration: 2019 Dec 12 → 2019 Dec 15

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11953 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	26th International Conference on Neural Information Processing, ICONIP 2019
Country/Territory	Australia
City	Sydney
Period	19/12/12 → 19/12/15

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

Keywords

Alzheimer’s disease
Brain networks
Graph auto-encoder
Graph convolutional neural network

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-36708-4_47

Cite this

Choi, M., Lee, P., Kim, D., Lee, S., Youn, H. C., Jeong, H.-G., & Han, C. E. (2019). Brain network decomposition by Auto Encoder (AE) and graph auto encoder (GAE). In T. Gedeon, K. W. Wong, & M. Lee (Eds.), Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings (pp. 568-579). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11953 LNCS). Springer. https://doi.org/10.1007/978-3-030-36708-4_47

Brain network decomposition by Auto Encoder (AE) and graph auto encoder (GAE). / Choi, Myungwon; Lee, Pilsub; Kim, Daegyeom et al.
Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings. ed. / Tom Gedeon; Kok Wai Wong; Minho Lee. Springer, 2019. p. 568-579 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11953 LNCS).

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

Choi, M, Lee, P, Kim, D, Lee, S, Youn, HC, Jeong, H-G & Han, CE 2019, Brain network decomposition by Auto Encoder (AE) and graph auto encoder (GAE). in T Gedeon, KW Wong & M Lee (eds), Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11953 LNCS, Springer, pp. 568-579, 26th International Conference on Neural Information Processing, ICONIP 2019, Sydney, Australia, 19/12/12. https://doi.org/10.1007/978-3-030-36708-4_47

Choi M, Lee P, Kim D, Lee S, Youn HC, Jeong HG et al. Brain network decomposition by Auto Encoder (AE) and graph auto encoder (GAE). In Gedeon T, Wong KW, Lee M, editors, Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings. Springer. 2019. p. 568-579. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-36708-4_47

Choi, Myungwon ; Lee, Pilsub ; Kim, Daegyeom et al. / Brain network decomposition by Auto Encoder (AE) and graph auto encoder (GAE). Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings. editor / Tom Gedeon ; Kok Wai Wong ; Minho Lee. Springer, 2019. pp. 568-579 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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AU - Jeong, Hyun-Ghang

AU - Han, Cheol E.

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AB - Brain networks consist of nodes that are anatomically defined brain regions, and edges that connect a pair of brain regions. The diffusion-weighted magnetic resonance images and the advances in computer-aided tractography algorithms showed that human brain networks are strongly associated with cognitive functions. Brain regions dedicated to a specific cognitive function are spatially clustered and efficiently connected each other; this is called local functional segregation. However, it is not well known that such a local segregation is associated with sub-networks which may act as building blocks of brain networks. In this work, we used machine learning techniques to analyze brain networks. Specifically, using an auto-encoder and a graph auto-encoder, we decomposed brain networks into several essential building blocks, and compared their results through various measures of decomposition quality. We observed that the graph auto-encoder out-performed the auto-encoder, and that its results showed significant correlation with cognitive deterioration in Alzheimer’s disease.

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Brain network decomposition by Auto Encoder (AE) and graph auto encoder (GAE)

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this