In this thesis, a method is presented that incorporates anatomical information into the statistical analysis of functional neuroimaging data. Available anatomical information is used to explicitly specify spatial components within a functional volume that are assumed to carry evidence of functional activation. After estimating the activity by fitting the same spatial model to each functional volume and projecting the estimates back into voxel-space, one can proceed with a conventional time-series analysis such as statistical parametric mapping (SPM). The anatomical information used in this work comprised the reconstructed grey matter surface, derived from high-resolution T1-weighted magnetic resonance images (MRI). The spatial components specified in the model were of low spatial frequency and confined to the grey matter surface. By explaining the observed activity in terms of these components, one efficiently captures spatially smooth response components induced by underlying neuronal activations localised close to or within the grey matter sheet. Effectively, the method implements a spatially variable anatomically informed deconvolution and consequently the method was named anatomically informed basis functions (AIBF). AIBF can be used for the analysis of any functional imaging modality. In this thesis it was applied to simulated and real functional MRI (fMRI) and positron emission tomography (PET) data. Amongst its various applications are high-resolution modelling of single-subject data (e.g.~fMRI), spatial deconvolution (PET) and the analysis of multiple subject data using canonical anatomical bases.
