Abstract:
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 p...Show MoreMetadata
Abstract:
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. We evaluate the performance of the algorithm on synthetic data using ROC curves and on real fMRI data using the NPAIRSresampling framework.
Published in: 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821)
Date of Conference: 18-18 April 2004
Date Added to IEEE Xplore: 07 March 2005
Print ISBN:0-7803-8388-5