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On Tue, 18 Nov 2008 19:26:51 +0100, John Ashburner <[log in to unmask]> wrote: >> Is there any reason to avoid using the "affine only" setting in the >> spatial normalization module? I would have thought that would give an >> affine transform that's more general (flexible) than rigid body. > >It is more flexible, but this may not really be what you want. Note also that >by default it regularises the registration, which has a slight biasing >tendancy to zoom up the images (because the MNI brains are bigger than real >brains). This can be changed via the UI though. (Thanks for your help.) Oh, OK...I assume you're referring to the Bayesian framework you use? >> >> Two more questions on within-subject registration in longitudinal VBM: >> * Is there any reason to prefer rigid body over affine? > >The best model depends on whether or not you expect the voxel sizes to be >correct etc. If the head moves as a rigid-body, then rigid should be the >better model, as the zooms and shears of the affine should be known. If they >are known, then estimating them will only introduce errors. However, if there >are issues with incorrect voxel sizes in the headers etc, then an affine >model may be preferable. If this is the case, because VBM is actually trying >to compare volumes, your data may need to be caligrated in some way. In this experiment, the subjects are rhesus monkeys, and going from juvenile to adult their brains are growing a little bit. So the precise context is coregistering within subject, but at different developmental timepoints. Best, S