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

    Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
    Title of host publicationNeural Information Processing - 26th International Conference, ICONIP 2019, Proceedings
    EditorsTom Gedeon, Kok Wai Wong, Minho Lee
    PublisherSpringer
    Pages568-579
    Number of pages12
    ISBN (Print)9783030367077
    DOIs
    Publication statusPublished - 2019
    Event26th International Conference on Neural Information Processing, ICONIP 2019 - Sydney, Australia
    Duration: 2019 Dec 122019 Dec 15

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11953 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference26th International Conference on Neural Information Processing, ICONIP 2019
    Country/TerritoryAustralia
    CitySydney
    Period19/12/1219/12/15

    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

    Fingerprint

    Dive into the research topics of 'Brain network decomposition by Auto Encoder (AE) and graph auto encoder (GAE)'. Together they form a unique fingerprint.

    Cite this