Useful Papers - Contrasts
Summary: An excellent overview of the basic statistical model and the difference between F- and t-tests. Some good examples (p.43-47) of how to design contrasts to test particular questions about the differences between two or more impulse functions. Also contains a good overview of the deconvolution model and its derivation from the basic statistical model.
Summary: Some nice walk-throughs of how to design conjunction contrasts, F-contrasts, and other somewhat advanced techniques in SPM. Some particularly good points are made about F-contrast construction - how to make them for several basis functions at once, for example, their relation to t-contrasts, and other points.
Summary: Further, quite concise, overview of the GLM and what contrasts mean. A nice breakdown of how the various ways of testing for activity (correlation coefficient, ANOVA, etc.) all break down to the GLM, and explanation of why the T-contrast (and hence F-contrast) supersede those ways generally. Some description of the 'basis function' model of HRF that transitions between the canonical and the FIR model.
Summary: Introduction of the basis-function approach to event-related analysis, where the design matrix is convolved with a non-Fourier basis function set, designed to span the space of reasonable responses in a compact way. The set introduced here separately models the magnitude and latency of a canonical HRF, and experimental evidence shows different areas differ on those two different metrics.
Summary: The original paper describing conjunction designs as an alternative to subtraction designs. The difference from subtractions is outlined, as are the important ways conjunction analyses fit in well with factorial designs. A primitive way of doing a conjunction analysis (since discarded) is laid forth, and some PET data analyzed with these methods is examined.
Summary: A follow-up to the Price & Friston paper above, this paper more explicitly describes the difference between conjunction and subtraction analyses in terms of how the former takes account of the interaction term (should there be one). Factorial designs used with conjunction analyses allow a much more explicit treatment of those interactions. Some PET data is examined.
Summary: Attempts to get by one big hurdle of the standard conjunction analysis, which is that it's only available in fixed-effect fashion, preventing that style of analysis from being used in a true random-effects group model. The authors suggest a formula that allows one to used a fixed-effect conjunction group model and calculate a reasonable confidence interval of the population that might share such an effect.