IDEAS home Printed from https://ideas.repec.org/a/bla/scjsta/v29y2002i3p333-353.html
   My bibliography  Save this article

A Stochastic Geometry Model for Functional Magnetic Resonance Images

Author

Listed:
  • NIELS VÆVER HARTVIG

Abstract

In functional magnetic resonance imaging, spatial activation patterns are commonly estimated using a non‐parametric smoothing approach. Significant peaks or clusters in the smoothed image are subsequently identified by testing the null hypothesis of lack of activation in every volume element of the scans. A weakness of this approach is the lack of a model for the activation pattern; this makes it difficult to determine the variance of estimates, to test specific neuroscientific hypotheses or to incorporate prior information about the brain area under study in the analysis. These issues may be addressed by formulating explicit spatial models for the activation and using simulation methods for inference. We present one such approach, based on a marked point process prior. Informally, one may think of the points as centres of activation, and the marks as parameters describing the shape and area of the surrounding cluster. We present an MCMC algorithm for making inference in the model and compare the approach with a traditional non‐parametric method, using both simulated and visual stimulation data. Finally we discuss extensions of the model and the inferential framework to account for non‐stationary responses and spatio‐temporal correlation.

Suggested Citation

  • Niels Væver Hartvig, 2002. "A Stochastic Geometry Model for Functional Magnetic Resonance Images," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(3), pages 333-353, September.
  • Handle: RePEc:bla:scjsta:v:29:y:2002:i:3:p:333-353
    DOI: 10.1111/1467-9469.00294
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-9469.00294
    Download Restriction: no

    File URL: https://libkey.io/10.1111/1467-9469.00294?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:jss:jstsof:44:i14 is not listed on IDEAS
    2. Lei Xu & Timothy D. Johnson & Thomas E. Nichols & Derek E. Nee, 2009. "Modeling Inter-Subject Variability in fMRI Activation Location: A Bayesian Hierarchical Spatial Model," Biometrics, The International Biometric Society, vol. 65(4), pages 1041-1051, December.
    3. Gordana Derado & F. DuBois Bowman & Clinton D. Kilts, 2010. "Modeling the Spatial and Temporal Dependence in fMRI Data," Biometrics, The International Biometric Society, vol. 66(3), pages 949-957, September.

    More about this item

    Statistics

    Access and download statistics

    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:bla:scjsta:v:29:y:2002:i:3:p:333-353. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0303-6898 .

    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.