In a December post I suggested a decision tree that can be used for deciding whether or not to adopt a new (or new to you) method or device for your next fMRI experiment. In essence it was a form of risk analysis. But it isn't only new methods that need to be evaluated carefully before you embark on an experiment. What about the plethora of parameters that characterize even the simplest combination of single-shot EPI with whatever passes for standard hardware on your scanner? RF coil selection, echo spacing, TE, slice thickness, slice gap, TR, RF flip angle... all can have profound effects on your data. In the absence of a compelling paper that strongly implicates a particular protocol for your experiment, how do you make an informed choice before you proceed?
Functional signal and physiologic noise
In an ideal world you would be able to run a pilot experiment that robustly activates all the brain regions you're interested in. This approach can work well if all of your regions of interest lie in primary cortex: responses to stimuli are typically robust, baselines are fairly easily established, and simple stimuli can often be used to assess regional responses. But many contemporary experiments don't lend themselves to extensive piloting; actually doing the entire experiment may be the only way to assess whether regions A, B and C are activated at all, let alone more or less with a particular parameter setting! Instead, we may have to focus our attention on the noise properties of the tissue.