Sensitivity enhancement of task-evoked fMRI using ensemble empirical mode decomposition

Shang Hua N Lin, Geng Hong Lin, Pei Jung Tsai, Ai Ling Hsu, Men Tzung Lo, Albert C. Yang, Ching Po Lin, Chang-Wei Wu

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Background: Functional magnetic resonance imaging (fMRI) is widely used to investigate dynamic brain functions in neurological and psychological issues; however, high noise level limits its applicability for intensive and sophisticated investigations in the field of neuroscience. New method: To deal with both issue (low sensitivity and dynamic signal), we used ensemble empirical mode decomposition (EEMD), an adaptive data-driven analysis method for nonstationary and nonlinear features, to filter task-irrelevant noise from raw fMRI signals. Using both simulations and representative fMRI data, we optimized the analytic parameters and identified non-meaningful intrinsic mode functions (IMFs) to remove noise. Results: We revealed the following advantages of EEMD in fMRI analysis: (1) EEMD achieved high detectability for task engagement; (2) the functional sensitivity was markedly enhanced by removing task-irrelevant artifacts based on EEMD. Comparison with existing method(s): Compared with other noise-removal methods (e.g., band-pass filtering and independent component analysis), the EEMD-based artifact-removal method exhibited better spatial specificity and superior Gaussianity of the resulting t-score distribution. Conclusions: We found that EEMD method was efficient to enhance the functional sensitivity of evoked fMRI. The same strategy would be applicable to resting-state fMRI signal in the general purpose.

Original languageEnglish
Pages (from-to)56-66
Number of pages11
JournalJournal of Neuroscience Methods
Volume258
DOIs
Publication statusPublished - Jan 30 2016

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Magnetic Resonance Imaging
Noise
Artifacts
Neurosciences
Psychology
Brain

Keywords

  • Ensemble empirical mode decomposition (EEMD)
  • FMRI sensitivity
  • Hilbert-Huang transform
  • Nonlinear
  • Nonstationary
  • Resting-state fMRI
  • Task-fMRI

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Sensitivity enhancement of task-evoked fMRI using ensemble empirical mode decomposition. / Lin, Shang Hua N; Lin, Geng Hong; Tsai, Pei Jung; Hsu, Ai Ling; Lo, Men Tzung; Yang, Albert C.; Lin, Ching Po; Wu, Chang-Wei.

In: Journal of Neuroscience Methods, Vol. 258, 30.01.2016, p. 56-66.

Research output: Contribution to journalArticle

Lin, Shang Hua N ; Lin, Geng Hong ; Tsai, Pei Jung ; Hsu, Ai Ling ; Lo, Men Tzung ; Yang, Albert C. ; Lin, Ching Po ; Wu, Chang-Wei. / Sensitivity enhancement of task-evoked fMRI using ensemble empirical mode decomposition. In: Journal of Neuroscience Methods. 2016 ; Vol. 258. pp. 56-66.
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