Campuses:

A General Statistical Analysis for fMRI data

Thursday, October 12, 2000 - 2:00pm - 2:40pm
Keith Worsley (McGill University)
Many methods are available for the statistical analysis of fMRI data that range from a simple linear model for the response and a global autoregressive model for the temporal errors (SPM), to a more sophisticated non-linear model for the response with a local state space model for the temporal errors (Purdon, et al., 1998). We have written Matlab programs (http://www.bic.mni.mcgill.ca/users/keith) that seek a compromise between validity, generality, simplicity and execution speed. The method is based on linear models with local AR(p) errors. The AR(p) model is fitted via the Yule-Walker equations with a simple bias correction, then the parameters are regularized by spatial smoothing. The resulting effects are then combined across runs in the same session, across sessions in the same subject, and across subjects within a population by further linear models that perform a random effects analysis in which the residual degrees of freedom are increased using a form of regularization by spatial smoothing.