[MINC-users] running voxel-wise mixed-effects modeling on huge dataset with Python Rpy2 and pyezminc

[Andrew Janke]

On 19 February 2014 16:43, Vladimir S. Fonov <vladimir.fonov at gmail.com> wrote:
> For example:
> https://github.com/BIC-MNI/pyezminc/blob/develop/examples/glim_image.py -
> performs a simple voxel-level t-test on difference between two cohorts ,
> using DBM data from 64 subjects (with ROI covering the whole brain)
> execution time is 14m56s , equivalent analysis performed using glim_image on
> the same computer takes 3m12s.

Hi Vladimir,

This looks like a pretty spectacularly useful bit of porting to me. I
find we are now using python more and more although I do have a stats
PhD student who is an R aficionado.  For those that are interested in
python, you might also find this of interest:

   https://github.com/carlohamalainen/volgenmodel-nipype

A friend of mine is working on integration of some of the existing
MINC tools with nipype, the example is volgenmodel (MDA creation).

This so far has been far more stable than previous versions that used
perl scripts to generate SGI scripts with dependencies and the likes.


a