TITLE: Bayesian Modelling of fMRI Time Series
AUTHORS: Pedro A. d. F. R. Højen-Sørensen, Lars K.
Hansen and Carl Edward Rasmussen
Department of Mathematical Modelling, Building 321
Technical University of Denmark, DK-2800 Lyngby, Denmark
We present a Hidden Markov Model (HMM) for inferring the hidden psychological
state (or neural activity) during single trail fMRI activation 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 advantage of this method is that detection
of short time learning effects between repeated trails is possible since
inference is based only on single trail experiments.
Submitted for NIPS'99, Denver, Colorado, 1999.