[Neuroimaging] Interest in modeling library for NiPy

Marmaduke Woodman mmwoodman at gmail.com
Fri Nov 4 13:47:01 EDT 2016


hi

> How can we help?

It would be really really helpful to get feedback on the scope: what sort
of functionality would you want from such a library? If you've ever done
modeling work, what tripped you up or made life difficult?

I see a few main dimensions of scope & functionality:

First, in the projects I've seen using TVB and DCM, I see network models
used both parametrically and non parametrically. By parametrically, I mean,
for example,  that we do a parameter sweep over coupling strength, and
compare empirical and simulated FC, and then look at, for example, a group
wise difference of the best coupling strength. By non parametrically, I
have in mind things like DCM's estimates of effective connectivity, where
many parameters are being estimated and they are interpreted as an
ensemble, not individually. This is naturally a slippery slope and some
modeling questions lie between the two extremes.

The second dimension of scope is in terms of the methods and numerics
implemented. Simple time-stepping schemes for differential equations are
easy to implement, but making them high-performance is less so (think
CUDA/OpenCL). Bayesian inversion is really neat, but requires computing
gradients or using packages like PyMC3 or Stan.

Finally, I would assume we're mainly interested in human or primate
neuroimaging, so modalities like fMRI & MEG, and maybe invasive clinical
modalities too. As more of a methods library, this would be a detail I
guess, and I would expect to delegate I/O, formatting, etc to the
respective libraries.


Again, consider this a RFC and let me know what you think.

Marmaduke
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