In the Gabrieli Lab, the current development team and a number of current and former students and postdocs have written a lot of custom code over the years to do new analyses or simplify doing old analyses.
One big package of those scripts, which relates mostly to region-of-interest (ROI) analyses and artifact detection, is collected in the RoiToolbox, but there are others as well - some of which were written for particular people, some of which are intended for labwide use.
If you have any code out there you've written and found useful or think other people might find useful, let us know, even if it's designed for your own directory structure. We'd be happy to make it generic and usable by everyone, so the lab can share all the tools people are out there writing.
This is a quick overview of some of the scripts that I know are available; this page will probably be further subdivided as the list grows. For now, this'll do, though.
GlmMask and glm_specmask
These scripts do essentially the same thing; GlmMask is a stripped-down version, where glm_specmask allows a couple other options. They're both intended to avoid a problem with SPM where voxels that have low intensity are dropped from further analysis. These scripts allow the user to explicitly specify a mask image before model estimation that can explicitly include all the voxels in the brain, to make sure every voxel in the brain is included in the model. The difference between the two is where that mask image comes from. GlmMask assumes the user already has an appropriate mask image and asks for a preexisting image file; glm_specmask allows the user to specify a preexisting mask, but if he/she doesn't have one, it allows the user to create one based on a particular subject's anatomy.
I'm actually not super familiar with contrast_creator2, but I think these both do essentially the same thing, which is copy individual subjects' contrast images into a central directory and rename them based on the subject they came from, to make doing a lot of group analyses on those images a lot faster - you can pick all the images in a single directory instead of having to navigate to every subjects' results directory for every analysis. Sue, do I have this one right?
A great program written by Kalina Christoff that, given an SPMcfg.mat file, accumulates information about each condition in the experiment (like where it's located in the design matrix, what its onset times are, etc.) and gets it into a matlab structure array. Very useful for writing your own scripts.