Information Assisted Dictionary Learning for fMRI data analysis
This work addresses fMRI data analysis for neuroscience researchers, offering an incremental improvement by integrating prior information to enhance dictionary learning.
The paper tackles the problem of task-related fMRI analysis by proposing a dictionary learning method that incorporates prior knowledge from experimental design and brain atlases, bypasses sparsity parameter selection, and handles HRF modeling uncertainties, achieving performance gains over methods like GLM on synthetic and real datasets.
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.