English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Journal Article

Probabilistic modeling of single-trial fMRI data

MPS-Authors
/persons/resource/persons20036

Svensen,  M.
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

/persons/resource/persons19800

Kruggel,  F.
Department Cognitive Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

/persons/resource/persons20070

von Cramon,  D. Yves
MPI of Cognitive Neuroscience (Leipzig, -2003), The Prior Institutes, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Svensen, M., Kruggel, F., & von Cramon, D. Y. (2000). Probabilistic modeling of single-trial fMRI data. IEEE Transactions on Medical Imaging, 19(1), 25-35. doi:10.1109/42.832957.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0010-D219-4
Abstract
This paper describes a probabilistic framework for modeling single-trial functional magnetic resonance (fMR) images based on a parametric model for the hemodynamic response and Markov random field (MRF) image models. The model is fitted to image data by maximizing a lower bound on the log likelihood. The result is an approximate maximum a posteriori estimate of the joint distribution over the model parameters and pixel labels. Examples show how this technique can used to segment two-dimensional (2-D) fMR images, or parts thereof, into regions with different characteristics of their hemodynamic response.