I admit that it's a common practice. Maybe we should say something
skeptical about this? A caveat at the end of the example?


On Mon, Nov 25, 2013 at 5:52 PM, MrBago <notifications@github.com> wrote:

> In doc/examples/streamline_tools.py:
>
> > @@ -0,0 +1,265 @@
> > +"""
> > +
> > +==================================================
> > +Using Dipy to Target, Group, and Count Streamlines
> > +==================================================
> > +
> > +This example is meant to be an introduction to some of the streamline tools
> > +available in dipy. Some of the functions covered in this example are
> > +``target``, ``connectivity_matrix`` and ``density map``. ``target`` allows one
> > +to filter streamlines that either pass though or do not pass through some
> > +region of the brain, ``connectivity__matrix`` groups and counts streamlines
> > +based on where in the brain be begin and end, and finally, density map counts
> > +the number of streamlines that pass though every voxel of some image.
>
> @arokem <https://github.com/arokem>, I agree that this kind of inference
> is very tricky to make but the literature is full of them. Often the
> connectivity matrix is threshold, hopefully at a range of values not just
> one value, and then network measures of the un-weighted and undirected
> graph are taken. But I've seen cases where people use a weighted graph,
> weighted by number of streamlines and so on. It's defiantly a bit of a wild
> west right now.
>
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>