IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v65y2009i4p1041-1051.html
   My bibliography  Save this article

Modeling Inter-Subject Variability in fMRI Activation Location: A Bayesian Hierarchical Spatial Model

Author

Listed:
  • Lei Xu
  • Timothy D. Johnson
  • Thomas E. Nichols
  • Derek E. Nee

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:biomet:v:65:y:2009:i:4:p:1041-1051
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2008.01190.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Timothy D. Johnson, 2007. "Analysis of Pulsatile Hormone Concentration Profiles with Nonconstant Basal Concentration: A Bayesian Approach," Biometrics, The International Biometric Society, vol. 63(4), pages 1207-1217, December.
    2. 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.
    3. Jörg Polzehl & Vladimir G. Spokoiny, 2001. "Functional and dynamic magnetic resonance imaging using vector adaptive weights smoothing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(4), pages 485-501.
    4. Carmen Fernández & Peter J. Green, 2002. "Modelling spatially correlated data via mixtures: a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 805-826, October.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Zhe Yu & Raquel Prado & Erin Burke Quinlan & Steven C. Cramer & Hernando Ombao, 2016. "Understanding the Impact of Stroke on Brain Motor Function: A Hierarchical Bayesian Approach," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 549-563, April.
    2. Daniel Spencer & Rajarshi Guhaniyogi & Raquel Prado, 2020. "Joint Bayesian Estimation of Voxel Activation and Inter-regional Connectivity in fMRI Experiments," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 845-869, December.
    3. Suyu Liu & Ying Yuan & Richard Castillo & Thomas Guerrero & Valen E. Johnson, 2014. "Evaluation of image registration spatial accuracy using a Bayesian hierarchical model," Biometrics, The International Biometric Society, vol. 70(2), pages 366-377, June.
    4. repec:jss:jstsof:44:i14 is not listed 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.
    1. M. Rufo & J. Martín & C. Pérez, 2006. "Bayesian analysis of finite mixture models of distributions from exponential families," Computational Statistics, Springer, vol. 21(3), pages 621-637, December.
    2. Håvard Rue & Ingelin Steinsland & Sveinung Erland, 2004. "Approximating hidden Gaussian Markov random fields," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 877-892, November.
    3. Jan Povala & Seppo Virtanen & Mark Girolami, 2020. "Burglary in London: insights from statistical heterogeneous spatial point processes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1067-1090, November.
    4. 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.
    5. repec:dau:papers:123456789/9572 is not listed on IDEAS
    6. Vidal Rodeiro, Carmen L. & Lawson, Andrew B., 2005. "An evaluation of the edge effects in disease map modelling," Computational Statistics & Data Analysis, Elsevier, vol. 49(1), pages 45-62, April.
    7. repec:jss:jstsof:44:i14 is not listed on IDEAS
    8. Vinicius Mayrink & Dani Gamerman, 2009. "On computational aspects of Bayesian spatial models: influence of the neighboring structure in the efficiency of MCMC algorithms," Computational Statistics, Springer, vol. 24(4), pages 641-669, December.
    9. Congdon, Peter, 2007. "Mixtures of spatial and unstructured effects for spatially discontinuous health outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3197-3212, March.
    10. Deb, Soudeep & Karmakar, Sayar, 2023. "A novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).
    11. Polzehl, Jörg & Tabelow, Karsten, 2007. "Adaptive Smoothing of Digital Images: The R Package adimpro," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 19(i01).
    12. Bolin, David & Wallin, Jonas & Lindgren, Finn, 2019. "Latent Gaussian random field mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 130(C), pages 80-93.
    13. repec:jss:jstsof:19:i01 is not listed on IDEAS
    14. Huayu Liu & Nichole E. Carlson & Gary K. Grunwald & Alex J. Polotsky, 2018. "Modeling associations between latent event processes governing time series of pulsing hormones," Biometrics, The International Biometric Society, vol. 74(2), pages 714-724, June.

    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:biomet:v:65:y:2009:i:4:p:1041-1051. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.