Characterizing and differentiating task-based and resting state FMRI signals via two-stage dictionary learning

@article{Zhang2015CharacterizingAD,
  title={Characterizing and differentiating task-based and resting state FMRI signals via two-stage dictionary learning},
  author={Shu Zhang and Xiang Li and Jinglei Lv and Xi Jiang and Bao Ge and Lei Guo and Tianming Liu},
  journal={2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)},
  year={2015},
  pages={675-678}
}
A relatively underexplored question in fMRI is whether there are intrinsic differences in terms of signal composition patterns that can effectively characterize and differentiate task-based and resting state fMRI (tfMRI or rsfMRI) signals. In this paper, we propose a novel two-stage sparse representation framework to examine the fundamental difference… CONTINUE READING