Abstract

The detection of significantly activated brain regions in multi-subject functional magnetic resonance imaging (fMRI) studies almost invariably entails the coregistration of individual subjects' data in a standard space. Here, we investigate how sensitivity to detect loci of generic activation in such studies may be conditioned by the precision of anatomical registration. We describe a novel algorithm, implemented in the wavelet domain, for inhomogeneous deformation of individual images to match a template. The algorithm matches anatomical features in a coarse-to-fine fashion, first minimising a cost function in terms of relatively coarse spatial features and then proceeding iteratively to match the images in terms of progressively more detailed anatomical features. Applying the method to data acquired from two groups of 12 healthy volunteers (with mean age 27 and 70 years, respectively), during performance of a paired associate learning task, we show that geometrical overlap between template and individual images is monotonically improved, compared to an affine transform, by additional inhomogeneous deformations informed by more detailed features. Likewise, sensitivity to detect activated voxels can be substantially improved, by a factor of 4 or more, if wavelet-mediated deformations informed by medium-sized anatomical features are applied in addition to a preliminary affine transform. However, sensitivity to detect activated voxels was reduced by "over-registering" data or matching anatomical features at the finest scales of the wavelet transform. The benefits of variable precision registration are particularly salient for data acquired in older subjects, which showed evidence of greater inter-subject anatomic variability and generally required more extensive local deformation to achieve a satisfactory match to the template image. We conclude that major benefits in sensitivity to detect functional activation in multi-subject fMRI studies can be attained with an inhomogeneous deformation applied over appropriate spatial scales.