I receive a lot of questions about how to setup the basic analyses in AFNI.  Previously I detailed using uber_subject.py, the AFNI graphical user interface to afni_proc.py which really does all of the hard work under the hood.  Today I’m going to briefly review the common options in afni_proc.py and why I use them.  You […]

I could say a lot about proper simulation of fMRI experiments.  Basically it’s important to measure the efficiency of your design before an experiment (see here).  If you wanted to perform the calculations yourself, MATLAB/Python/Octave are all options and the process is “fairly simple”. Xmat = [design_matrix’ * design_matrix]; main_effects(ct) = [ contrast’ * (Xmat^-1) […]

Typically when we smooth (aka spatial filter) our fMRI data using a fixed kernel size.  And as we know, the size of a smoothing kernel makes some difference in the final results (see below).  This shows a group analysis map, the results are more shocking on single subject maps. The common misconception is that you […]

AFNI (Analysis of Functional NeuroImages) includes SUMA (Surface Mapping with AFNI) for displaying brains in 3D.  AFNI already includes a 3D Render plugin (shown below), capable for displaying fMRI results in 3D.  But SUMA offers a few additional benefits, not the least of which is the ability to click and rotate the image by hand […]

I’ve written before about how AFNI offers users the ability to perform the same or very similar analyses using a variety of tools.  Performing connectivity analysis is no different.  First of all, most people who are talking about connectivity are really referring to “seed connectivity” (sometimes called functional connectivity or fcMRI), whereby one region of […]