Multi-subject dictionary learning to segment an atlas of brain spontaneous activity

Varoquaux, G., Gramfort, A., Pedregosa, F., Michel, V., Thirion, B. (2012). Multi-subject dictionary learning to segment an atlas of brain spontaneous activity. Information Processing in Medical Imaging 6801.

Ok so a lot of the registration algorithms for fMRI might be relevant for my VSD data. This paper was recommended to me by Stephan Gerhard (from NS&B).

They take an approach that combines ICA with dictionary learning.

Start with a linear signal decomposition. Y is data time series (n timeponts x p voxels). V describes functional processes or structured measurement artifacts with k spatial latent variables (p x k). U describes the time series of the latent variables (n x k). Y = U * V

So they are setting up the problem to create a hierarchical generative model of the data, and they have an optimization procedure to find U and V.

The algorithms are written into the text as pseudo-code, varoquaux's website: http://gael-varoquaux.info/, and maybe some related code: https://github.com/GaelVaroquaux/canica

Theres also a package NiPy that has a lot of neuro-imaging algorithms. There may be some relevant algorithms there for my data. And also: http://scikit-learn.org/stable/

The rest of the paper is validation, but there are some other good references. I need to dive into the code to start getting some real insights into the applicability of this to my data.