A reversible jump Markov chain Monte Carlo algorithm for analysis of functional neuroimages

We propose a new signal-detection approach for detecting brain activations from PET or fMRI images in a two-state ("on-off") neuroimaging study. We model the activation pattern as a superposition of an unknown number of circular spatial basis functions of unknown position, size, and amplitude. We determine the number of these functions and their parameters by maximum a posteriori (MAP) estimation. To maximize the posterior distribution we use a reversible jump Markov-chain Monte-Carlo (RJMCMC) algorithm. The main advantage of RJMCMC is that it can estimate parameter vectors of unknown length. Thus, in the model used the number of activation sites does not need to be known. Using a phantom derived from a neuroimaging study, we demonstrate that the proposed method can estimate more accurately the activation pattern from traditional approaches.

[1]  P. Green.  Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[2]  K J Worsley,et al.  An overview and some new developments in the statistical analysis of PET and fMRI data , 1997, Human brain mapping.

[3]  Guillaume Stawinski,et al.  Reversible jump Markov chain Monte Carlo for Bayesian deconvolution of point sources , 1998, Optics & Photonics.

[4]  Karl J. Friston.  Imaging neuroscience: principles or maps? , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Christophe Andrieu,et al.  Joint Bayesian model selection and estimation of noisy sinusoids via reversible jump MCMC , 1999, IEEE Trans. Signal Process..

[6]  S. Strother,et al.  An evaluation of methods for detecting brain activations from PET or fMRI images , 1999, 1999 IEEE Nuclear Science Symposium. Conference Record. 1999 Nuclear Science Symposium and Medical Imaging Conference (Cat. No.99CH37019).

[7]  C. Robert,et al.  Bayesian inference in hidden Markov models through the reversible jump Markov chain Monte Carlo method , 2000 .

[8]  N V Hartvig,et al.  Spatial mixture modeling of fMRI data , 2000, Human brain mapping.

[9]  Yongyi Yang,et al.  A signal-detection approach for analysis of functional neuroimages , 2001, 2001 IEEE Nuclear Science Symposium Conference Record (Cat. No.01CH37310).

[10]  Stephen J. Roberts,et al.  Minimum-Entropy Data Partitioning Using Reversible Jump Markov Chain Monte Carlo , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Stephen C. Strother,et al.  An evaluation of methods for detecting brain activations from functional neuroimages , 2002, Artif. Intell. Medicine.