I love Python and its ecosystem of scientific packages because it makes it very easy to experiment with new techniques. My undergraduate research assistant Manon and I have been recently playing with Diffusion Weighted Imaging (DWI) using dipy.

For those unfamiliar with it, DWI is a neuroimaging technique that allows to infer the most likely direction of fiber tracts in in-vivo brains by using computational modeling. I’m interested in using it for my research in human face processing. It also has a certain aesthetic appeal, because in the end you can make nice images like this one (each line is a reconstructed tract, and the colors indicate the direction in the 3D space):

I made a tutorial on using dipy “start to finish” for a recent event we had in our department. You can find it on github. Take it as an exploration—I think I would optimize certain steps before using it in an experimental context, but it helped me understand what I’m dealing with much better than reading endless papers on the method.