Identification of non-linear models of neural activity in bold fmri |
Daniel J. Jacobsen, Kristoffer H. Madsen, Lars Kai Hansen
|
| Abstract | Non-linear hemodynamic models express the BOLD signal as
a nonlinear, parametric functional of the temporal sequence of
local neural activity. Several models have been proposed for
this neural activity. We identify one such parametric model
by estimating the distribution of its parameters. These distributions
are themselves stochastic, therefore we estimate their
variance by epoch based leave-one-out cross validation, using
a Metropolis-Hastings algorithm for sampling of the posterior
parameter distribution. |
| Keywords | BOLD fMRI, MCMC, learning, model comparison, Bayes |
| Type | Conference paper [With referee] |
| Conference | 3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano |
| Year | 2006 Month April pp. 952-955 |
| ISBN / ISSN | 0-7803-9576-x |
| BibTeX data | [bibtex] |
| IMM Group(s) | Intelligent Signal Processing |