Fusion of high-order and low-order effective connectivity networks for MCI classification

Yang Li, Jingyu Liu, Ke Li, Pew Thian Yap, Minjeong Kim, Chong Yaw Wee, Dinggang Shen

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Citation (Scopus)

    Abstract

    Functional connectivity network derived from resting-state fMRI data has been found as effective biomarkers for identifying patients with mild cognitive impairment from healthy elderly. However, the ordinary functional connectivity network is essentially a low-order network with the assumption that the brain is static during the entire scanning period, ignoring the temporal variations among correlations derived from brain region pairs. To overcome this weakness, we proposed a new type of high-order network to more accurately describe the relationship of temporal variations among brain regions. Specifically, instead of the commonly used undirected pairwise Pearson’s correlation coefficient, we first estimated the low-order effective connectivity network based on a novel sparse regression algorithm. By using the similar approach, we then constructed the high-order effective connectivity network from low-order connectivity to incorporate signal flow information among the brain regions. We finally combined the low-order and the high-order effective connectivity networks using two decision trees for MCI classification and experimental results obtained demonstrate the superiority of the proposed method over the conventional undirected low-order and high-order functional connectivity networks, as well as the low-order and high-order effective connectivity networks when they were used separately.

    Original languageEnglish
    Title of host publicationMachine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings
    EditorsYinghuan Shi, Heung-Il Suk, Kenji Suzuki, Qian Wang
    PublisherSpringer Verlag
    Pages307-315
    Number of pages9
    ISBN (Print)9783319673882
    DOIs
    Publication statusPublished - 2017
    Event8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada
    Duration: 2017 Sept 102017 Sept 10

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10541 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
    Country/TerritoryCanada
    CityQuebec City
    Period17/9/1017/9/10

    Bibliographical note

    Publisher Copyright:
    © 2017, Springer International Publishing AG.

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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