Useful Papers - Basic Statistical Modeling
Friston et. al (1995), "Analysis of fMRI time-series revisited," NeuroImage 2, 45-53 PDF
Summary: Friston et. al is the theoretical work extending the GLM to account for a known autocorrelation function, to enable the 'coloring' approach to noise autocorrelation in fMRI to be used. The authors argue that swamping unknown autocorrelation by temporally smoothing the data with a known kernel can produce less-biased parameter estimates than no correction. Worsley & Friston is essentialy a correction to the Friston et. al paper, fixing up some math issues.
Summary: A ground-up description of the general linear model and how it's applied to neuroimaging data. Describes how the GLM works in good (but not too dense) mathematical detail, and how it's modified in the case of PET (and fMRI, to a shorter degree).
Summary: Describes the physical and theoretical background for fMRI and a variety of different analysis pathways for dealing with data. One of the earliest papers to describe statistical analysis of fMRI data in thorough detail. Strategies described include voxel-by-voxel analysis, GLM methods, frequency domain methods, cross-correlation methods, and a number of others.