Signal sampling for efficient sparse representation of resting state FMRI data

Bao Ge, Milad Makkie, Jin Wang, Shijie Zhao, Xi Jiang, Xiang Li, Jinglei Lv, Shu Zhang, Wei Zhang, Junwei Han, Lei Guo, Tianming Liu

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


As the size of brain imaging data such as fMRI grows explosively, it provides us with unprecedented and abundant information about the brain. How to reduce the size of fMRI data but not lose much information becomes a more and more pressing issue. Recent literature studies tried to deal with it by dictionary learning and sparse representation methods, however, their computation complexities are still high, which hampers the wider application of sparse representation method to large scale fMRI datasets. To effectively address this problem, this work proposes to represent resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse representation. First we sampled the whole brain’s signals via different sampling methods, then the sampled signals were aggregate into an input data matrix to learn a dictionary, finally this dictionary was used to sparsely represent the whole brain’s signals and identify the resting state networks. Comparative experiments demonstrate that the proposed signal sampling framework can speed-up by ten times in reconstructing concurrent brain networks without losing much information. The experiments on the 1000 Functional Connectomes Project further demonstrate its effectiveness and superiority.

Original languageEnglish (US)
Pages (from-to)1206-1222
Number of pages17
JournalBrain Imaging and Behavior
Issue number4
StatePublished - Dec 1 2016
Externally publishedYes


  • DTI
  • Resting state fMRI
  • Resting state networks
  • Sampling

ASJC Scopus subject areas

  • Clinical Neurology
  • Neurology
  • Psychiatry and Mental health
  • Cellular and Molecular Neuroscience
  • Cognitive Neuroscience
  • Radiology Nuclear Medicine and imaging
  • Behavioral Neuroscience


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