Title: Information Assisted Dictionary Learning for fMRI data analysis,
Abstract: In this paper, the task-related fMRI problem is treated in its matrix
factorization formulation. The focus of the reported work is on the dictionary
learning (DL) matrix factorization approach. A major novelty of the paper lies
in the incorporation of well-established assumptions associated with the GLM
technique, which is currently in use by the neuroscientists. These assumptions
are embedded as constraints in the DL formulation. In this way, our approach
provides a framework of combining well-established and understood techniques
with a more ``modern'' and powerful tool. Furthermore, this paper offers a way
to relax a major drawback associated with DL techniques; that is, the proper
tuning of the DL regularization parameter. This parameter plays a critical role
in DL-based fMRI analysis since it essentially determines the shape and
structures of the estimated functional brain networks. However, in actual fMRI
data analysis, the lack of ground truth renders the a priori choice of the
regularization parameter a truly challenging task. Indeed, the values of the DL
regularization parameter, associated with the $\ell_1$ sparsity promoting norm,
do not convey any tangible physical meaning. So it is practically difficult to
guess its proper value. In this paper, the DL problem is reformulated around a
sparsity-promoting constraint that can directly be related to the minimum
amount of voxels that the spatial maps of the functional brain networks occupy.
Such information is documented and it is readily available to neuroscientists
and experts in the field.
The proposed method is tested against a number of other popular techniques
and the obtained performance gains are reported using a number of synthetic
fMRI data. Results with real data have also been obtained in the context of a
number of experiments and will be soon reported in a different publication.