 |
 |
Dear list, I have data from a PET experiment of 10 subjects, 5 conditions per subject (some data missing). The images going into the analysis are themselves parametric maps of blood flow (instead of the more common scaled count rate images). I have reason to believe that the variances of the parameterized blood flow maps are spatially inhomogeneous. That is, areas of high residual variance (after parameterization) coincide with those of high blood flow. I suppose a more extreme case of the situation I describe would be that of, e.g., a dopamine receptor map. My problem is that I am very uncertain of the exact method SPM2 uses to estimate standard deviations for inference in the type of PET setup described above. I want to make sure I am not pooling over brain tissue (Worsley et al., 1992). I think I've grasped the approach taken in FMRI to remove serial correlations (partly global hyperparameter estimation), but PET remains elusive. In "Human Brain Function" (2nd Ed), Chapter 37, p. 736, the authors write: "In PET data there is substantial evidence against an assumption of constant variance (homoscedasticity)..." ... "If homoscedasticity could be assumed, variance estimates could legitimately be pooled accross all voxels." Since I (want to) assume heteroscedasticity, what approach should I take when using SPM2? Sincerely, -Chris -- Christopher Bailey <[log in to unmask]> PET Centre and Center for Functionally Integrative Neuroscience Aarhus University Hospital, Denmark http://www.cfin.au.dk/