  We propose a novel denoising framework for task functional Magnetic Resonance
Imaging (tfMRI) data to delineate the high-resolution spatial pattern of the
brain functional connectivity via dictionary learning and sparse coding (DLSC).
In order to address the limitations of the unsupervised DLSC-based fMRI
studies, we utilize the prior knowledge of task paradigm in the learning step
to train a data-driven dictionary and to model the sparse representation. We
apply the proposed DLSC-based method to Human Connectome Project (HCP) motor
tfMRI dataset. Studies on the functional connectivity of cerebrocerebellar
circuits in somatomotor networks show that the DLSC-based denoising framework
can significantly improve the prominent connectivity patterns, in comparison to
the temporal non-local means (tNLM)-based denoising method as well as the case
without denoising, which is consistent and neuroscientifically meaningful
within motor area. The promising results show that the proposed method can
provide an important foundation for the high-resolution functional connectivity
analysis, and provide a better approach for fMRI preprocessing.
