Effect of spatial normalization on analysis of functional data

Conventional analysis of functional data often involves a normalization step in which the data are spatially aligned so that a measurement can be made across or between studies. Whether to enhance the signal-to-noise ratio or to detect significant deviations in activation from normal, the method used to register the underlying anatomies clearly impacts the viability of the analysis. Nevertheless, it is common practice to infer only homogeneous transformations, in which all parts of the image volume undergo the same mapping. To detect subtle effects or to extend the analysis to anatomies that exhibit considerable morphological variation, higher dimensional mappings to allow more accurate alignment will be crucial. We describe a Bayesian volumetric warping approach to the normalization problem, which matches local image features between MRI brain volumes, and compare its performance with a standard method (SPM'96) as well as contrast its effect on the analysis of a set of functional MRI studies against that obtained with a g-parameter affine registration.