  In this paper, the task-related fMRI problem is treated in its matrix
factorization formulation, focused on the Dictionary Learning (DL) approach.
The new method allows the incorporation of a priori knowledge associated both
with the experimental design as well as with available brain Atlases. Moreover,
the proposed method can efficiently cope with uncertainties related to the HRF
modeling. In addition, the proposed method bypasses one of the major drawbacks
that are associated with DL methods; that is, the selection of the
sparsity-related regularization parameters. In our formulation, an alternative
sparsity promoting constraint is employed, that bears a direct relation to the
number of voxels in the spatial maps. Hence, the related parameters can be
tuned using information that is available from brain atlases. The proposed
method is evaluated against several other popular techniques, including GLM.
The obtained performance gains are reported via a novel realistic synthetic
fMRI dataset as well as real data that are related to a challenging
experimental design.
