Probabilistic source separation on resting-state fMRI and its use for early MCI identification

Eunsong Kang, Heung Il Suk

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

    3 Citations (Scopus)

    Abstract

    In analyzing rs-fMRI, blind source separation has been studied extensively and various machine-learning techniques have been proposed in the literature. However, to our best knowledge, most of the existing methods do not explicitly separate noise components that naturally corrupt the observed BOLD signals, thus hindering from the understanding of underlying functional mechanisms in a human brain. In this paper, we formulate the problem of latent source separation in a probabilistic manner, where we explicitly separate the observed signals into a true source signal and a noise component. As for the inference of the latent source distribution with respect to an input regional mean signal, we use a stochastic variational Bayesian inference and implement it in a neural network framework. Further, in order for identification of a subject with early mild cognitive impairment (eMCI) rs-fMRI, we also propose to use the relations of the inferred source signals as features, i.e., potential imaging-biomarkers. We presented the validity of the proposed methods by conducting experiments on the publicly available ADNI2 dataset and comparing with the existing methods.

    Original languageEnglish
    Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
    EditorsAlejandro F. Frangi, Christos Davatzikos, Gabor Fichtinger, Carlos Alberola-López, Julia A. Schnabel
    PublisherSpringer Verlag
    Pages275-283
    Number of pages9
    ISBN (Print)9783030009304
    DOIs
    Publication statusPublished - 2018
    Event21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
    Duration: 2018 Sept 162018 Sept 20

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11072 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
    Country/TerritorySpain
    CityGranada
    Period18/9/1618/9/20

    Bibliographical note

    Funding Information:
    Acknowledgement. This research was supported by the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIP(2016941946), and Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2017-0-01779, A machine learning and statistical inference framework for explainable artificial intelligence).

    Funding Information:
    This research was supported by the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIP(2016941946), and Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2017-0-01779, A machine learning and statistical inference framework for explainable artificial intelligence).

    Publisher Copyright:
    © Springer Nature Switzerland AG 2018.

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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