Assessing brain activity through spatial Bayesian variable selection.
Statistical parametric mapping (SPM), relying on the general linear model and classical hypothesis testing, is a benchmark tool for assessing human brain activity using data from fMRI experiments. Friston et al. discuss some limitations of this frequentist approach and point out promising Bayesian perspectives. In particular, a Bayesian formulation allows explicit modeling and estimation of activation probabilities. In this study, we directly address this issue and develop a new regression based approach using spatial Bayesian variable selection. Our method has several advantages. First, spatial correlation is directly modeled for activation probabilities and indirectly for activation amplitudes. As a consequence, there is no need for spatial adjustment in a postprocessing step. Second, anatomical prior information, such as the distribution of grey matter or expert knowledge, can be included as part of the model. Third, the method has superior edge-preservation properties as well as being fast to compute. When applied to data from a simple visual experiment, the results demonstrate improved sensitivity for detecting activated cortical areas and for better preserving details of activated structures.
Smith
Michael
M
University of Sydney, Sydney, Australia. mikes@econ.usyd.edu.au
Pütz
Benno
B
Auer
Dorothee
D
Fahrmeir
Ludwig
L
D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D002138:Calibration; D003198:Computer Simulation; D005829:Geniculate Bodies / Q000502:physiology; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008297:Male; D008959:Models, Neurological; D009010:Monte Carlo Method; D012016:Reference Values; D012044:Regression Analysis