title: Bayesian analysis of fMRI data with ICA based spatial prior.
abstract: Spatial modeling is essential for fMRI analysis due to relatively high noise in the data. Earlier approaches have been primarily concerned with the spatial coherence of the BOLD response in local neighborhoods. In addition to a smoothness constraint, we propose to incorporate prior knowledge of brain activation patterns learned from training samples. This spatially informed prior can significantly enhance the estimation process by inducing sensitivity to task related regions of the brain. As fMRI data exhibits intersubject variability in functional anatomy, we design the prior using Independent Component Analysis (ICA). Due to the non-Gaussian assumption, ICA does not regress to the mean activation pattern and thus avoids suppressing intersubject differences. Results from a real fMRI experiment indicate that our approach provides statistically significant improvement in estimating activation compared to the standard general linear model (GLM) based methods.
authors.affiliation: Department of Biomedical Engineering, Yale University, P.O. Box 208042, New Haven, CT 06520, USA. deepti.bathula@yale.edu
authors.forename: Deepti R
authors.initials: DR
authors.lastname: Bathula
authors.forename: Hemant D
authors.initials: HD
authors.lastname: Tagare
authors.forename: Lawrence H
authors.initials: LH
authors.lastname: Staib
authors.forename: Xenophon
authors.initials: X
authors.lastname: Papademetris
authors.forename: Robert T
authors.initials: RT
authors.lastname: Schultz
authors.forename: James S
authors.initials: JS
authors.lastname: Duncan
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003627:Data Interpretation, Statistical; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity