Automatic HyperParameter Estimation in fMRI
Maximum a posteriori (MAP) in the scope of the Bayesian framework is a common criterion used in a large number of estimation and decision problems. In image reconstruction problems, typically, the image to be estimated is modeled as a Markov Random Fields (MRF) described by a Gibbs distribution. In this case, the Gibbs energy depends on a multiplicative coefficient, called hyperparameter, that is usually manually tuned  in a trial and error basis.
In this paper we propose an automatic hyperparameter estimation method designed in the scope of functional Magnetic Resonance Imaging (fMRI) to identify activated brain areas based on Blood Oxygen Level Dependent (BOLD) signal.
This problem is formulated as classical binary detection problem in a Bayesian framework where the estimation and inference steps are joined together. The prior terms, incorporating the a priori physiological knowledge about the Hemodynamic Response Function (HRF), drift and spatial correlation across the brain (using edge preserving priors), are automatically tuned with the new proposed method.
Results on real and synthetic data are presented and compared against the conventional General Linear Model (GLM) approach.
KeywordsHyperParameter Estimation Bayesian fMRI HRF
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- 1.Afonso, D., Sanches, J., Lauterbach, M.: Joint bayesian detection of brain activated regions and local hrf estimation in functional mri. In: Proceedings IEEE ICASSP 2008, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Las Vegas, USA, March 30 - April 4 (2008)Google Scholar
- 4.Cruz, P., ao Teixeira, J., Figueiredo, P.: Reproducibility of a rapid visual brain mapping protocol. In: Proc. of the 15th Annual Meeting of the OHBM, San Francisco, US (June 2009)Google Scholar
- 9.Jezzard, P., Matthews, P.M., Smith, S.M.: Functional magnetic resonance imaging: An introduction to methods. Oxford Medical Publications (2006)Google Scholar
- 10.Friston, K.J.: Analyzing brain images: Principles and overview. In: Frackowiak, R.S.J., Friston, K.J., Frith, C., Dolan, R., Mazziotta, J.C. (eds.) Human Brain Function, pp. 25–41. Academic Press, USA (1997)Google Scholar
- 14.Moon, T.K., Stirling, W.C.: Mathematical methods and algorithms for signal processing. Prentice-Hall, Englewood Cliffs (2000)Google Scholar
- 15.Sanches, J., Marques, J.S.: A map estimation algorithm using IIR recursive filters. In: Proceedings International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, Lisbon, Portugal (July 2003)Google Scholar