The generalized form of contextdependent PPI approach increases flexibility of statistical modeling by allowing for more than two task conditions, and improves singlesubject modelfit thereby increasing the sensitivity to true positive findings and a reduction in false positives. Developed by Donald McLaren, PhD. Available at: http://www.brainmap.wisc.edu/pages/11AGeneralizedFormofContextDependentP

The Peak_nii toolbox was developed to allow the user to have flexibility of clustering their data. Based on your threshold, it will cluster your data and find the peaks within each cluster. Additionally, it has been combined with a data extraction tool that allows one to extract the data from all the scans of the analysis from all the clusters, along with several other extraction options, with a single command. Developed by Donald McLaren, PhD. Available at: www.martinos.org/~mclaren/ftp/Utilities_DGM

People have often complained that AFNI does this, while FSL does that, and SPM does this other thing. Wouldn't it be nice if there was one image viewer that did it all. I came across this a few months ago and it combines that best features from these three packages. Notable features include: peak labelling, cluster extraction and plotting (if using GLM_flex, see below), multiple overlays, split overlays that can be synced, on the fly thresholding, multiple slice views, multiple colormap options, and many more. Developed by Aaron Schultz, PhD. Available at: http://nmr.mgh.harvard.edu/harvardagingbrain/People/AaronSchultz/OrthoView.html

Typical image analysis at the group level has two limitations: (1) analysis only includes voxels for which all subject have data; and (2) only hands purely withinsubject designs (and possibly only those with one factor) and purely betweensubject designs. As experiments become more complicated, mixed designs are being used more frequently, but current approaches overestimate the betweensubject effects. GLM Flex correctly estimates the betweensubject effects in mixeddesigns with a single model. GLM Flex can also analyze all voxels that have data, rather than the subset that contains data from all subjects. Developed by Donald McLaren, PhD and Aaron Schultz, PhD. Available at: http://nmr.mgh.harvard.edu/harvardagingbrain/People/AaronSchultz/GLM_Flex.html

The NeuroSynth framework is a collaborative effort between Tal Yarkoni, Tor Wager, Tom Nichols, Russ Poldrack, and David Van Essen. The website, and most of the underlying analysis tools, were developed by Tal Yarkoni. Lots of other people have contributed valuable feedback and testing, including Alex Shackman, Drew Fox, Luke Chang, Tim Vickery, Jessica AndrewsHanna, and members of the Neuroimaging Data Access Group. Available at: http://neurosynth.org/