IDEAS home Printed from https://ideas.repec.org/a/spr/sistpr/v25y2022i3d10.1007_s11203-022-09268-6.html
   My bibliography  Save this article

A chi-square type test for time-invariant fiber pathways of the brain

Author

Listed:
  • Juna Goo

    (Boise State University)

  • Lyudmila Sakhanenko

    (Michigan State University)

  • David C. Zhu

    (Michigan State University)

Abstract

A longitudinal diffusion tensor imaging (DTI) study on a single brain can be remarkably useful to probe white matter fiber connectivity that may or may not be stable over time. We consider a novel testing problem where the null hypothesis states that the trajectories of a coherently oriented fiber population remain the same over a fixed period of time. Compared to other applications that use changes in DTI scalar metrics over time, our test is focused on the partial derivative of the continuous ensemble of fiber trajectories with respect to time. The test statistic is shown to have the limiting chi-square distribution under the null hypothesis. The power of the test is demonstrated using Monte Carlo simulations based on both the theoretical and empirical critical values. The proposed method is applied to a longitudinal DTI study of a normal brain.

Suggested Citation

  • Juna Goo & Lyudmila Sakhanenko & David C. Zhu, 2022. "A chi-square type test for time-invariant fiber pathways of the brain," Statistical Inference for Stochastic Processes, Springer, vol. 25(3), pages 449-469, October.
  • Handle: RePEc:spr:sistpr:v:25:y:2022:i:3:d:10.1007_s11203-022-09268-6
    DOI: 10.1007/s11203-022-09268-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11203-022-09268-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11203-022-09268-6?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lyudmila Sakhanenko & Michael DeLaura & David C. Zhu, 2021. "Nonparametric model for a tensor field based on high angular resolution diffusion imaging (HARDI)," Statistical Inference for Stochastic Processes, Springer, vol. 24(2), pages 445-476, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      Corrections

      All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sistpr:v:25:y:2022:i:3:d:10.1007_s11203-022-09268-6. See general information about how to correct material in RePEc.

      If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

      If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

      For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

      Please note that corrections may take a couple of weeks to filter through the various RePEc services.

      IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.