Bayesian Modelling of fMRI Lime Series
Abstract
We present a Hidden Markov Model (HMM) for inferring the hidden psychological state (or neural activity) during single trial tMRI activa(cid:173) tion experiments with blocked task paradigms. Inference is based on Bayesian methodology, using a combination of analytical and a variety of Markov Chain Monte Carlo (MCMC) sampling techniques. The ad(cid:173) vantage of this method is that detection of short time learning effects be(cid:173) tween repeated trials is possible since inference is based only on single trial experiments.
Cite
Text
Højen-Sørensen et al. "Bayesian Modelling of fMRI Lime Series." Neural Information Processing Systems, 1999.Markdown
[Højen-Sørensen et al. "Bayesian Modelling of fMRI Lime Series." Neural Information Processing Systems, 1999.](https://mlanthology.org/neurips/1999/hjensrensen1999neurips-bayesian/)BibTeX
@inproceedings{hjensrensen1999neurips-bayesian,
title = {{Bayesian Modelling of fMRI Lime Series}},
author = {Højen-Sørensen, Pedro A. d. F. R. and Hansen, Lars Kai and Rasmussen, Carl Edward},
booktitle = {Neural Information Processing Systems},
year = {1999},
pages = {754-760},
url = {https://mlanthology.org/neurips/1999/hjensrensen1999neurips-bayesian/}
}