Bayesian second-level analysis of functional magnetic resonance images.
We propose a new method for the second-level analysis of functional MRI data based on Bayesian statistics. Our method does not require a computationally costly Bayesian model on the first level of analysis. Rather, modeling for single subjects is realized by means of the commonly applied General Linear Model. On the basis of the resulting parameter estimates for single subjects we calculate posterior probability maps and maps of the effect size for effects of interest in groups of subjects. A comparison of this method with the conventional analysis based on t statistics shows that the new approach is more robust against outliers. Moreover, our method overcomes some of the severe problems of null hypothesis significance tests such as the need to correct for multiple comparisons and facilitates inferences which are hard to formulate in terms of classical inferences.
Max-Planck-Institute of Cognitive Neuroscience, Stephanstrasse 1a, D-04103, Leipzig, Germany. neumann@cns.mpg.de
Jane
J
Neumann
Gabriele
G
Lohmann
D000465:Algorithms; D001499:Bayes Theorem; D002540:Cerebral Cortex / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008959:Models, Neurological; D011338:Probability Theory