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Dear Jesper, On Tue, 2005-04-19 at 14:22 +0200, Jesper Andersson wrote: > But, as I said, for PET it is much easier and you DO assume > heteroscedasticity (at least across voxels) when you use SPM. Many thanks for clearing that up for me! I'm pleased to know, and yet I wonder what this all means wrt sensitivity. Since variance is estimated voxelwise, there are precious few df... Is there an assumption of conditions having equal variances -> pooling across conditions? I think I have to follow up on your parenthetical note. I'm not sure I understand why I'm asked if I want to model non-sphericity (replications across subjects). I understand it as a question, whether or not I want to estimate variance components (of either the proportional scaling or ANCOVA normalization model) in my simple model (10subs, 5cond/sub). The ensuing ReML crashes with each iteration resulting in a NaN. Temporal non-sphericity (over voxels) : ...REML estimation ... Surely there are not enough data to model the variance structure of this PET design? Could you give an example where non-sphericity correction made more sense (or how I should make more sense of the question)? Thanks again for your help, -Chris