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Brain Imaging and Behavior

, Volume 10, Issue 1, pp 21–32 | Cite as

Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations

  • Shu Zhang
  • Xiang Li
  • Jinglei Lv
  • Xi Jiang
  • Lei Guo
  • Tianming Liu
Original Research

Abstract

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 or resting state fMRI (tfMRI or rsfMRI) signals. In this paper, we propose a novel two-stage sparse representation framework to examine the fundamental difference between tfMRI and rsfMRI signals. Specifically, in the first stage, the whole-brain tfMRI or rsfMRI signals of each subject were composed into a big data matrix, which was then factorized into a subject-specific dictionary matrix and a weight coefficient matrix for sparse representation. In the second stage, all of the dictionary matrices from both tfMRI/rsfMRI data across multiple subjects were composed into another big data-matrix, which was further sparsely represented by a cross-subjects common dictionary and a weight matrix. This framework has been applied on the recently publicly released Human Connectome Project (HCP) fMRI data and experimental results revealed that there are distinctive and descriptive atoms in the cross-subjects common dictionary that can effectively characterize and differentiate tfMRI and rsfMRI signals, achieving 100 % classification accuracy. Moreover, our methods and results can be meaningfully interpreted, e.g., the well-known default mode network (DMN) activities can be recovered from the very noisy and heterogeneous aggregated big-data of tfMRI and rsfMRI signals across all subjects in HCP Q1 release.

Keywords

Task-based fMRI Resting-state fMRI Sparse coding Online dictionary learning 

Notes

Acknowledgments

T Liu was supported by NSF CAREER Award (IIS-1149260), NIH R01 DA-033393, NIH R01 AG-042599, NSF CBET-1302089 and NSF BCS-1439051. L Guo was supported by the NSFC #61273362.

Conflict of Interest

Shu Zhang, Xiang Li, Jinglei Lv, Xi Jiang, Lei Guo, and Tianming Liu declare that they have no conflicts of interest.

Informed Consent

Data used in this study were previously collected and archived in a data bank.

Supplementary material

11682_2015_9359_MOESM1_ESM.docx (3.9 mb)
ESM 1 (DOCX 4041 kb)

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Shu Zhang
    • 1
  • Xiang Li
    • 1
  • Jinglei Lv
    • 1
    • 2
  • Xi Jiang
    • 1
  • Lei Guo
    • 2
  • Tianming Liu
    • 1
  1. 1.Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research CenterThe University of GeorgiaAthensUSA
  2. 2.School of AutomationNorthwestern Polytechnic UniversityXi’anChina

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