FASP: A machine learning approach to functional astrocyte phenotyping from time-lapse calcium imaging data

Yinxue Wang, Guilai Shi, David J. Miller, Yizhi Wang, Gerard Broussard, Yue Wang, Lin Tian, Guoqiang Yu

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

    3 Scopus citations

    Abstract

    We propose a machine learning approach to characterize the functional status of astrocytes, the most abundant cells in human brain, based on time-lapse Ca2+ imaging data. The interest in analyzing astrocyte Ca2+ dynamics is evoked by recent discoveries that astrocytes play proactive regulatory roles in neural information processing, and is enabled by recent technical advances in modern microscopy and ultrasensitive genetically encoded Ca2+ indicators. However, current analysis relies on eyeballing the time-lapse imaging data and manually drawing regions of interest, which not only limits the analysis throughput but also at risk to miss important information encoded in the big complex dynamic data. Thus, there is an increased demand to develop sophisticated tools to dissect Ca2+ signaling in astrocytes, which is challenging due to the complex nature of Ca2+ signaling and low signal to noise ratio. We develop Functional AStrocyte Phenotyping (FASP) to automatically detect functionally independent units (FIUs) and extract the corresponding characteristic curves in an integrated way. FASP is data-driven and probabilistically principled, flexibly accounts for complex patterns and accurately controls false discovery rates. We demonstrate the effectiveness of FASP on both synthetic and real data sets.

    Original languageEnglish (US)
    Title of host publication2016 IEEE International Symposium on Biomedical Imaging
    Subtitle of host publicationFrom Nano to Macro, ISBI 2016 - Proceedings
    PublisherIEEE Computer Society
    Pages351-354
    Number of pages4
    ISBN (Electronic)9781479923502
    DOIs
    StatePublished - Jun 15 2016
    Event2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016 - Prague, Czech Republic
    Duration: Apr 13 2016Apr 16 2016

    Publication series

    NameProceedings - International Symposium on Biomedical Imaging
    Volume2016-June
    ISSN (Print)1945-7928
    ISSN (Electronic)1945-8452

    Other

    Other2016 IEEE 13th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2016
    CountryCzech Republic
    CityPrague
    Period4/13/164/16/16

    All Science Journal Classification (ASJC) codes

    • Biomedical Engineering
    • Radiology Nuclear Medicine and imaging

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