Subject-specific prediction using nonlinear population modeling: Application to early brain maturation from DTI

Neda Sadeghi, P. Thomas Fletcher, Marcel Prastawa, John H. Gilmore, Guido Gerig

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

    Abstract

    The term prediction implies expected outcome in the future, often based on a model and statistical inference. Longitudinal imaging studies offer the possibility to model temporal change trajectories of anatomy across populations of subjects. In the spirit of subject-specific analysis, such normative models can then be used to compare data from new subjects to the norm and to study progression of disease or to predict outcome. This paper follows a statistical inference approach and presents a framework for prediction of future observations based on past measurements and population statistics. We describe prediction in the context of nonlinear mixed effects modeling (NLME) where the full reference population's statistics (estimated fixed effects, variance-covariance of random effects, variance of noise) is used along with the individual's available observations to predict its trajectory. The proposed methodology is generic in regard to application domains. Here, we demonstrate analysis of early infant brain maturation from longitudinal DTI with up to three time points. Growth as observed in DTI-derived scalar invariants is modeled with a parametric function, its parameters being input to NLME population modeling. Trajectories of new subject's data are estimated when using no observation, only the first or the first two time points. Leave-one-out experiments result in statistics on differences between actual and predicted observations. We also simulate a clinical scenario of prediction on multiple categories, where trajectories predicted from multiple models are classified based on maximum likelihood criteria.

    Original languageEnglish (US)
    Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings
    PublisherSpringer Verlag
    Pages33-40
    Number of pages8
    Volume8675 LNCS
    EditionPART 3
    ISBN (Print)9783319104423
    DOIs
    StatePublished - 2014
    Event17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - Boston, MA, United States
    Duration: Sep 14 2014Sep 18 2014

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 3
    Volume8675 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
    Country/TerritoryUnited States
    CityBoston, MA
    Period9/14/149/18/14

    ASJC Scopus subject areas

    • General Computer Science
    • Theoretical Computer Science

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