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)03029743
    ISSN (Electronic)16113349

    Other

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

    Fingerprint

    Nonlinear Prediction
    Brain
    Population statistics
    Trajectories
    Trajectory
    Mixed Effects
    Prediction
    Nonlinear Effects
    Statistical Inference
    Modeling
    Predict
    Fixed Effects
    Anatomy
    Multiple Models
    Random Effects
    Progression
    Maximum likelihood
    Maximum Likelihood
    Imaging
    Statistics

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

    Cite this

    Sadeghi, N., Fletcher, P. T., Prastawa, M., Gilmore, J. H., & Gerig, G. (2014). Subject-specific prediction using nonlinear population modeling: Application to early brain maturation from DTI. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings (PART 3 ed., Vol. 8675 LNCS, pp. 33-40). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8675 LNCS, No. PART 3). Springer Verlag. https://doi.org/10.1007/978-3-319-10443-0_5

    Subject-specific prediction using nonlinear population modeling : Application to early brain maturation from DTI. / Sadeghi, Neda; Fletcher, P. Thomas; Prastawa, Marcel; Gilmore, John H.; Gerig, Guido.

    Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings. Vol. 8675 LNCS PART 3. ed. Springer Verlag, 2014. p. 33-40 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8675 LNCS, No. PART 3).

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

    Sadeghi, N, Fletcher, PT, Prastawa, M, Gilmore, JH & Gerig, G 2014, Subject-specific prediction using nonlinear population modeling: Application to early brain maturation from DTI. in Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings. PART 3 edn, vol. 8675 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 8675 LNCS, Springer Verlag, pp. 33-40, 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014, Boston, MA, United States, 9/14/14. https://doi.org/10.1007/978-3-319-10443-0_5
    Sadeghi N, Fletcher PT, Prastawa M, Gilmore JH, Gerig G. Subject-specific prediction using nonlinear population modeling: Application to early brain maturation from DTI. In Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings. PART 3 ed. Vol. 8675 LNCS. Springer Verlag. 2014. p. 33-40. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). https://doi.org/10.1007/978-3-319-10443-0_5
    Sadeghi, Neda ; Fletcher, P. Thomas ; Prastawa, Marcel ; Gilmore, John H. ; Gerig, Guido. / Subject-specific prediction using nonlinear population modeling : Application to early brain maturation from DTI. Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings. Vol. 8675 LNCS PART 3. ed. Springer Verlag, 2014. pp. 33-40 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
    @inproceedings{8d8bb6ba187242b2bd30d41198129a13,
    title = "Subject-specific prediction using nonlinear population modeling: Application to early brain maturation from DTI",
    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.",
    author = "Neda Sadeghi and Fletcher, {P. Thomas} and Marcel Prastawa and Gilmore, {John H.} and Guido Gerig",
    year = "2014",
    doi = "10.1007/978-3-319-10443-0_5",
    language = "English (US)",
    isbn = "9783319104423",
    volume = "8675 LNCS",
    series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
    publisher = "Springer Verlag",
    number = "PART 3",
    pages = "33--40",
    booktitle = "Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings",
    edition = "PART 3",

    }

    TY - GEN

    T1 - Subject-specific prediction using nonlinear population modeling

    T2 - Application to early brain maturation from DTI

    AU - Sadeghi, Neda

    AU - Fletcher, P. Thomas

    AU - Prastawa, Marcel

    AU - Gilmore, John H.

    AU - Gerig, Guido

    PY - 2014

    Y1 - 2014

    N2 - 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.

    AB - 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.

    UR - http://www.scopus.com/inward/record.url?scp=84906974683&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84906974683&partnerID=8YFLogxK

    U2 - 10.1007/978-3-319-10443-0_5

    DO - 10.1007/978-3-319-10443-0_5

    M3 - Conference contribution

    SN - 9783319104423

    VL - 8675 LNCS

    T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

    SP - 33

    EP - 40

    BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings

    PB - Springer Verlag

    ER -