Anatomically informed basis functions

This paper introduces the general framework, concepts, and procedures of an atomically informed basis functions (AIBF), a new method for the analysis o f functional magnetic resonance imaging (fMRI) data. In contradistinction t o existing voxel-based univariate or multivariate methods the approach desc ribed here can incorporate various forms of prior anatomical knowledge to s pecify sophisticated spatiotemporal models for fMRI time-series. In particu lar, we focus on anatomical prior knowledge, based on reconstructed gray ma tter surfaces and assumptions about the location and spatial smoothness of the blood oxygenation level dependent (BOLD) effect. After reconstruction o f the grey matter surface from an individual's high-resolution T1-weighted MRI, we specify a set of anatomically informed basis functions, fit the mod el parameters for a single time point, using a regularized solution, and fi nally make inferences about the estimated parameters over time. Significant effects, induced by the experimental paradigm, can then be visualized in t he native voxel-space or on the reconstructed folded, inflated, or flattene d cortical surface. As an example, we apply the approach to a fMRI study (f inger opposition task) and compare the results to those of a voxel-based an alysis as implemented in the Statistical Parametric Mapping package (SPM99) . Additionally, we show, using simulated data, that the approach offers sev eral desirable features particularly in terms of superresolution and locali zation. (C) 2000 Academic Press.