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

研究成果: 雜誌貢獻文章

3 引文 (Scopus)

摘要

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.
原文英語
頁(從 - 到)56-66
頁數11
期刊Journal of Neuroscience Methods
258
DOIs
出版狀態已發佈 - 一月 30 2016

指紋

Magnetic Resonance Imaging
Noise
Artifacts
Neurosciences
Psychology
Brain

ASJC Scopus subject areas

  • Neuroscience(all)

引用此文

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.

於: Journal of Neuroscience Methods, 卷 258, 30.01.2016, p. 56-66.

研究成果: 雜誌貢獻文章

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. 於: Journal of Neuroscience Methods. 2016 ; 卷 258. 頁 56-66.
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AU - Yang, Albert C.

AU - Lin, Ching Po

AU - Wu, Chang-Wei

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AB - 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.

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