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Bayesian fMRI time series analysis with spatial priors.
- Source :
-
NeuroImage [Neuroimage] 2005 Jan 15; Vol. 24 (2), pp. 350-62. - Publication Year :
- 2005
-
Abstract
- We describe a Bayesian estimation and inference procedure for fMRI time series based on the use of General Linear Models (GLMs). Importantly, we use a spatial prior on regression coefficients which embodies our prior knowledge that evoked responses are spatially contiguous and locally homogeneous. Further, using a computationally efficient Variational Bayes framework, we are able to let the data determine the optimal amount of smoothing. We assume an arbitrary order Auto-Regressive (AR) model for the errors. Our model generalizes earlier work on voxel-wise estimation of GLM-AR models and inference in GLMs using Posterior Probability Maps (PPMs). Results are shown on simulated data and on data from an event-related fMRI experiment.
- Subjects :
- Bayes Theorem
Brain anatomy & histology
Brain Mapping methods
Face
Humans
Models, Neurological
Models, Theoretical
Multivariate Analysis
Normal Distribution
Regression Analysis
Reproducibility of Results
Sensitivity and Specificity
Visual Perception
Brain physiology
Magnetic Resonance Imaging methods
Subjects
Details
- Language :
- English
- ISSN :
- 1053-8119
- Volume :
- 24
- Issue :
- 2
- Database :
- MEDLINE
- Journal :
- NeuroImage
- Publication Type :
- Academic Journal
- Accession number :
- 15627578
- Full Text :
- https://doi.org/10.1016/j.neuroimage.2004.08.034