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
emails: phs,lkhansen,carl@imm.dtu.dk
www: http://eivind.imm.dtu.dk


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.