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

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Title:	Sensitivity enhancement of task-evoked fMRI using ensemble empirical mode decomposition
Authors:	羅孟宗;Lin, Shang-Hua N.;Lin, Geng-Hong;Tsai, Pei-Jung;Hsu, Ai-Ling;Lo, Men-Tzung;Yang, Albert C.;Lin, Ching-Po;Wu, Changwei W.
Contributors:	生醫理工學院生醫科學與工程學系
Keywords:	Adult;Algorithms;Brain - physiology;Brain Mapping - methods;Computer Simulation;Ensemble empirical mode decomposition (EEMD);Female;fMRI sensitivity;Hilbert–Huang transform;Humans;Magnetic Resonance Imaging - methods;Male;Nonlinear;Nonstationary;Resting-state fMRI;Signal Processing, Computer-Assisted;Signal-To-Noise Ratio;Task-fMRI;Young Adult
Date:	2016-01-30
Issue Date:	2026-04-23 11:20:47 (UTC+8)
Publisher:	Netherlands: Elsevier B.V
Abstract:	摘要： •EEMD in the fMRI preprocessing provided high efficacy in noise extraction.•Functional sensitivity was enhanced up to 60% following EEMD noise-removal.•The EEMD-purified fMRI signal showed better tendency in parametric statistics, providing higher Gaussianity in the effect size than that of data processed by ICA/band-pass filter. 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. 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. 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. 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. 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. 其他題名： J Neurosci Methods 出版者： Netherlands: Elsevier B.V 出版日期： 2016-01-30 出處： Journal of neuroscience methods, 2016-01, Vol.258, p.56-66 版權： 2015 Elsevier B.V. 版權： Copyright © 2015 Elsevier B.V. All rights reserved. 識別號： ISSN: 0165-0270 識別號： EISSN: 1872-678X 識別號： DOI: 10.1016/j.jneumeth.2015.10.009 識別號： PMID: 26523767
Appears in Collections:	[Department of Biomedical Sciences and Engineering ] journal & Dissertation

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