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Brain Imaging and Behavior

, Volume 10, Issue 4, pp 1206–1222 | Cite as

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 LiuEmail author
Original Research

Abstract

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.

Keywords

Resting state fMRI Sampling DTI DICCCOL Resting state networks 

Notes

Acknowledgments

We thank all investigators contributing data to the 1000 Functional Connectomes project, without whom this analysis could not have been performed. T Liu was supported by NIH R01 DA-033393, NIH R01 AG-042599, NSF CAREER Award IIS-1149260, NSF CBET-1302089 and NSF BCS-1439051. B Ge was supported by NSFC 61403243, 2015JM6312, the Fundamental Research Funds for the Central Universities from China (No. GK201402008) and Interdisciplinary Incubation Project of Learning Science of Shaanxi Normal University. The authors would like to thank the anonymous reviewers for their constructive comments.

Compliance with Ethical Standards

Conflict of Interest

None of the authors has conflict of interest to declare.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Bao Ge
    • 1
    • 3
  • Milad Makkie
    • 3
  • Jin Wang
    • 4
  • Shijie Zhao
    • 2
    • 3
  • Xi Jiang
    • 3
  • Xiang Li
    • 3
  • Jinglei Lv
    • 2
    • 3
  • Shu Zhang
    • 3
  • Wei Zhang
    • 3
  • Junwei Han
    • 2
  • Lei Guo
    • 2
  • Tianming Liu
    • 3
    Email author
  1. 1.School of Physics & Information TechnologyShaanxi Normal UniversityXi’anChina
  2. 2.School of AutomationNorthwestern Polytechnical UniversityXi’anChina
  3. 3.Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research CenterThe University of GeorgiaAthensUSA
  4. 4.Institute of BioinformaticsThe University of GeorgiaAthensUSA

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