Learning connectivity patterns via graph Kernels for fMRI-based depression diagnostics

Maksim Sharaev, Alexey Artemov, Ekaterina Kondrateva, Sergei Ivanov, Svetlana Sushchinskaya, Alexander Bernstein, Andrzej Cichocki, Evgeny Burnaev

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    7 Citations (Scopus)

    Abstract

    It has long been known that patients with depression exhibit abnormal brain functional connectivity patterns, that are often studied from a graph-theoretic perspective. However, while certain simpler graph features have been examined, little has been done in the direction of advanced feature learning methodologies such as network embeddings. Our work aims to extend the understanding of importance of graph-based features for medical applications by evaluating the recently proposed anonymous walk embeddings (AWE) in difficult depression classification problems. For two challenging datasets, we obtain performance gains and investigate the learned vector representations. Our results indicate that using AWE-based features is a promising new direction for medical applications.

    Original languageEnglish
    Title of host publicationProceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
    EditorsZhenhui Li, Jeffrey Yu, Feida Zhu, Hanghang Tong
    PublisherIEEE Computer Society
    Pages308-314
    Number of pages7
    ISBN (Electronic)9781538692882
    DOIs
    Publication statusPublished - 7 Feb 2019
    Event18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 - Singapore, Singapore
    Duration: 17 Nov 201820 Nov 2018

    Publication series

    NameIEEE International Conference on Data Mining Workshops, ICDMW
    Volume2018-November
    ISSN (Print)2375-9232
    ISSN (Electronic)2375-9259

    Conference

    Conference18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
    Country/TerritorySingapore
    CitySingapore
    Period17/11/1820/11/18

    Keywords

    • anonymous walk
    • depression
    • functional MRI
    • graph kernels
    • neuroimaging
    • random walk

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