Modeling the brain morphology distribution in the general aging population

W. Huizinga, D. H J Poot, G. Roshchupkin, E. E. Bron, M. A. Ikram, M. W. Vernooij, D. Rueckert, W. J. Niessen, S. Klein

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

    3 Citations (Scopus)
    61 Downloads (Pure)

    Abstract

    Both normal aging and neurodegenerative diseases such as Alzheimer's disease cause morphological changes of the brain. To better distinguish between normal and abnormal cases, it is necessary to model changes in brain morphology owing to normal aging. To this end, we developed a method for analyzing and visualizing these changes for the entire brain morphology distribution in the general aging population. The method is applied to 1000 subjects from a large population imaging study in the elderly, from which 900 were used to train the model and 100 were used for testing. The results of the 100 test subjects show that the model generalizes to subjects outside the model population. Smooth percentile curves showing the brain morphology changes as a function of age and spatiotemporal atlases derived from the model population are publicly available via an interactive web application at agingbrain.bigr.nl.

    Original languageEnglish
    Title of host publicationMedical Imaging 2016
    Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
    EditorsBarjor Gimi, Andrzej Krol
    PublisherSPIE
    Pages1-7
    Volume9788
    ISBN (Electronic)978-1-510600232
    DOIs
    Publication statusPublished - 2016
    EventMedical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging - San Diego, United States
    Duration: 1 Mar 20163 Mar 2016

    Publication series

    NameProceedings of SPIE
    Volume9788
    ISSN (Electronic)1605-7422

    Conference

    ConferenceMedical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging
    CountryUnited States
    CitySan Diego
    Period1/03/163/03/16

    Keywords

    • LMS method
    • Non-rigid groupwise registration
    • Partial least squares regression
    • Spatiotemporal atlas
    • Statistical modeling

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