Useful Papers - P-thresholds
Summary: Landmark paper applying Benjamini and Hochberg's original concepts (below) specifically to neuroimaging. Briefly reviews the concept of FDR, its mathematical background, and methods to control it, then demonstrates its use on sample and real fMRI datasets. Extremely readable and short - should be required reading for anyone using FDR control.
Summary: A good reference on Gaussian RFT methods in neuroimaging; reviews some of the ideas of FWE control in general and has a little bit of math, but not too much, on how Gaussian methods work. Set-, cluster- and voxel-level inferences are introduced, with power analyses for all and some discussion of when each is appropriate.
Summary: A specific attempt to bring permuation tests to the masses, this is a clear and comprehensive introduction to permutation testing, with a concise but thorough review of the concepts, and, crucially, three fully worked-out examples showing how permutation testing is applied to PET and fMRI data.
Summary: The best overview of FWE correction (with an excellent section on FDR) I've seen. Somewhat technical, but comprehensive. The authors review the mathematical background for several FWE correction methods (RFT, permutation, Bonferroni, etc.), and then, crucially, perform a variety of tests comparing the different methods in simulated and real data with various characteristics.
Summary: The original paper describing the current method of FDR control. Authors review the concepts of FWE correction and discuss why they're not always appropriate, then describe a simple mathematical procedure to control FDR, using some examples to show how it may be used. They use simulations to show that the gain in power over FWE methods may be substantial.
Summary: Not the first paper applying Gaussian RFT methods to neuroimaging data, but one of the most important ones. Worsley et. al bring together several lines of research on Gaussian RFT methods and tie up a number of loose threads to create a single statistical system for correcting FWE in neuroimaging (at the time, generally PET) data. This paper's approach is the foundation of SPM96 and SPM99's FWE correction.
Summary: An experimental paper showing the viability of permutation testing for a variety of different statistics in a group-analysis setting. Bullmore et. al look at practical issues surrounding permutation testing of various different statistics in real structural data.