Modeling of activation data in the BrainMapTM database: Detection of outliers

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    Abstract

    We describe a system for meta-analytical modeling of activation foci from functional neuroimaging studies. Our main vehicle is a set of density models in Talairach space capturing the distribution of activation foci in sets of experiments labeled by lobar anatomy. One important use of such density models is identification of novelty, i.e., low probability database events. We rank the novelty of the outliers and investigate the cause for 21 of the most novel, finding several outliers that are entry and transcription errors or infrequent or non-conforming terminology. We briefly discuss the use of atlases for outlier detection. Hum. Brain Mapping 15:146-156, 2002. © 2002 Wiley-Liss, Inc.
    Original languageEnglish
    JournalHuman Brain Mapping
    Volume15
    Issue number3
    Pages (from-to)146-156
    ISSN1065-9471
    DOIs
    Publication statusPublished - 2002

    Keywords

    • functional neuroimaging
    • BrainMap
    • Neuroinformatics
    • outliers
    • Parzen window
    • novelty

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