Skip Navigation

This Article
Right arrow FREE Full Text (PDF) Freely available
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Miglioretti, D. L.
Right arrow Articles by Zeger, S. L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Miglioretti, D. L.
Right arrow Articles by Zeger, S. L.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Biostatistics 1:403-421 (2000)
© 2000 Oxford University Press

Template mixture models for direct cortical electrical interference data

Diana L. Miglioretti1, Colin McCulloch2 and Scott L. Zeger3

1 Center for Health Studies, Group Health Cooperative, 1730 Minor Avenue, Suite 1600, Seattle, WA 98101, USA
2 Applied Statistics Program, General Electric Corporate Research and Development, Building K-1, Room 4C29A, NY 12301, Schenectady, USA
3 Department of Biostatistics, Johns Hopkins University, 615 North Wolfe Street, MD 21205, Baltimore, USA

This paper introduces a statistical approach for high-level spatial analysis when there is little prior information about the shape or location of the region of interest in the underlying image and limited spatial resolution of the available data. Our work was motivated by a functional brain mapping technique called direct cortical electrical interference (DCEI) that gives binary observations at multiple sites throughout the brain. We estimate an underlying, binary spatial response function using a mixture of an unknown number of simple geometrical shapes (e.g. circles) with unknown centers and sizes to be estimated. Inference is made using reversible jump Markov chain Monte Carlo. The approach is illustrated with simulated examples and a real example with DCEI data.

Keywords: Functional brain mapping; Image analysis; Logistic regression; Object recognition; Reversible jump Markov chain Monte Carlo


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 J. Neurosci.Home page
D. F. Boatman and D. L. Miglioretti
Cortical Sites Critical for Speech Discrimination in Normal and Impaired Listeners
J. Neurosci., June 8, 2005; 25(23): 5475 - 5480.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.