Spatiotemporal modeling of distribution-valued data applied to DTI tract evolution in infant neurodevelopment

Anuja Sharma, P. Thomas Fletcher, John H. Gilmore, Maria L. Escolar, Avantika Vardhan, Aditya Gupta, Martin Styner, Guido Gerig

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

    Abstract

    This paper proposes a novel method that extends spatiotemporal growth modeling to distribution-valued data. The method relaxes assumptions on the underlying noise models by considering the data to be represented by the complete probability distributions rather than a representative, single-valued summary statistics like the mean. When summarizing by the latter method, information on the underlying variability of data is lost early in the process and is not available at later stages of statistical analysis. The concept of 'distance' between distributions and an 'average' of distributions is employed. The framework quantifies growth trajectories for individuals and populations in terms of the complete data variability estimated along time and space. Concept is demonstrated in the context of our driving application which is modeling of age-related changes along white matter tracts in early neurodevelopment. Results are shown for a single subject with Krabbe's disease in comparison with a normative trend estimated from 15 healthy controls.

    Original languageEnglish (US)
    Title of host publicationISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro
    Pages684-687
    Number of pages4
    DOIs
    StatePublished - 2013
    Event2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 - San Francisco, CA, United States
    Duration: Apr 7 2013Apr 11 2013

    Other

    Other2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
    CountryUnited States
    CitySan Francisco, CA
    Period4/7/134/11/13

    Fingerprint

    Globoid Cell Leukodystrophy
    Probability distributions
    Statistical methods
    Trajectories
    Statistics
    Growth
    Noise
    Population
    White Matter

    Keywords

    • diffusion tensor imaging
    • distribution-valued data
    • early neurodevelopment
    • Mallow's distance
    • spatiotemporal growth trajectory

    ASJC Scopus subject areas

    • Biomedical Engineering
    • Radiology Nuclear Medicine and imaging

    Cite this

    Sharma, A., Fletcher, P. T., Gilmore, J. H., Escolar, M. L., Vardhan, A., Gupta, A., ... Gerig, G. (2013). Spatiotemporal modeling of distribution-valued data applied to DTI tract evolution in infant neurodevelopment. In ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro (pp. 684-687). [6556567] https://doi.org/10.1109/ISBI.2013.6556567

    Spatiotemporal modeling of distribution-valued data applied to DTI tract evolution in infant neurodevelopment. / Sharma, Anuja; Fletcher, P. Thomas; Gilmore, John H.; Escolar, Maria L.; Vardhan, Avantika; Gupta, Aditya; Styner, Martin; Gerig, Guido.

    ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro. 2013. p. 684-687 6556567.

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

    Sharma, A, Fletcher, PT, Gilmore, JH, Escolar, ML, Vardhan, A, Gupta, A, Styner, M & Gerig, G 2013, Spatiotemporal modeling of distribution-valued data applied to DTI tract evolution in infant neurodevelopment. in ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro., 6556567, pp. 684-687, 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013, San Francisco, CA, United States, 4/7/13. https://doi.org/10.1109/ISBI.2013.6556567
    Sharma A, Fletcher PT, Gilmore JH, Escolar ML, Vardhan A, Gupta A et al. Spatiotemporal modeling of distribution-valued data applied to DTI tract evolution in infant neurodevelopment. In ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro. 2013. p. 684-687. 6556567 https://doi.org/10.1109/ISBI.2013.6556567
    Sharma, Anuja ; Fletcher, P. Thomas ; Gilmore, John H. ; Escolar, Maria L. ; Vardhan, Avantika ; Gupta, Aditya ; Styner, Martin ; Gerig, Guido. / Spatiotemporal modeling of distribution-valued data applied to DTI tract evolution in infant neurodevelopment. ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro. 2013. pp. 684-687
    @inproceedings{7b503060138f40b1b58dfac4f69d91e2,
    title = "Spatiotemporal modeling of distribution-valued data applied to DTI tract evolution in infant neurodevelopment",
    abstract = "This paper proposes a novel method that extends spatiotemporal growth modeling to distribution-valued data. The method relaxes assumptions on the underlying noise models by considering the data to be represented by the complete probability distributions rather than a representative, single-valued summary statistics like the mean. When summarizing by the latter method, information on the underlying variability of data is lost early in the process and is not available at later stages of statistical analysis. The concept of 'distance' between distributions and an 'average' of distributions is employed. The framework quantifies growth trajectories for individuals and populations in terms of the complete data variability estimated along time and space. Concept is demonstrated in the context of our driving application which is modeling of age-related changes along white matter tracts in early neurodevelopment. Results are shown for a single subject with Krabbe's disease in comparison with a normative trend estimated from 15 healthy controls.",
    keywords = "diffusion tensor imaging, distribution-valued data, early neurodevelopment, Mallow's distance, spatiotemporal growth trajectory",
    author = "Anuja Sharma and Fletcher, {P. Thomas} and Gilmore, {John H.} and Escolar, {Maria L.} and Avantika Vardhan and Aditya Gupta and Martin Styner and Guido Gerig",
    year = "2013",
    doi = "10.1109/ISBI.2013.6556567",
    language = "English (US)",
    isbn = "9781467364546",
    pages = "684--687",
    booktitle = "ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro",

    }

    TY - GEN

    T1 - Spatiotemporal modeling of distribution-valued data applied to DTI tract evolution in infant neurodevelopment

    AU - Sharma, Anuja

    AU - Fletcher, P. Thomas

    AU - Gilmore, John H.

    AU - Escolar, Maria L.

    AU - Vardhan, Avantika

    AU - Gupta, Aditya

    AU - Styner, Martin

    AU - Gerig, Guido

    PY - 2013

    Y1 - 2013

    N2 - This paper proposes a novel method that extends spatiotemporal growth modeling to distribution-valued data. The method relaxes assumptions on the underlying noise models by considering the data to be represented by the complete probability distributions rather than a representative, single-valued summary statistics like the mean. When summarizing by the latter method, information on the underlying variability of data is lost early in the process and is not available at later stages of statistical analysis. The concept of 'distance' between distributions and an 'average' of distributions is employed. The framework quantifies growth trajectories for individuals and populations in terms of the complete data variability estimated along time and space. Concept is demonstrated in the context of our driving application which is modeling of age-related changes along white matter tracts in early neurodevelopment. Results are shown for a single subject with Krabbe's disease in comparison with a normative trend estimated from 15 healthy controls.

    AB - This paper proposes a novel method that extends spatiotemporal growth modeling to distribution-valued data. The method relaxes assumptions on the underlying noise models by considering the data to be represented by the complete probability distributions rather than a representative, single-valued summary statistics like the mean. When summarizing by the latter method, information on the underlying variability of data is lost early in the process and is not available at later stages of statistical analysis. The concept of 'distance' between distributions and an 'average' of distributions is employed. The framework quantifies growth trajectories for individuals and populations in terms of the complete data variability estimated along time and space. Concept is demonstrated in the context of our driving application which is modeling of age-related changes along white matter tracts in early neurodevelopment. Results are shown for a single subject with Krabbe's disease in comparison with a normative trend estimated from 15 healthy controls.

    KW - diffusion tensor imaging

    KW - distribution-valued data

    KW - early neurodevelopment

    KW - Mallow's distance

    KW - spatiotemporal growth trajectory

    UR - http://www.scopus.com/inward/record.url?scp=84881618101&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84881618101&partnerID=8YFLogxK

    U2 - 10.1109/ISBI.2013.6556567

    DO - 10.1109/ISBI.2013.6556567

    M3 - Conference contribution

    AN - SCOPUS:84881618101

    SN - 9781467364546

    SP - 684

    EP - 687

    BT - ISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro

    ER -