title: Sensitivity enhancement of task-evoked fMRI using ensemble empirical mode decomposition.
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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan.
authors.forename: Shang-Hua N
authors.initials: SH
authors.lastname: Lin
authors.affiliation: Graduate Institute of Biomedical Engineering, National Central University, Taoyuan, Taiwan.
authors.forename: Geng-Hong
authors.initials: GH
authors.lastname: Lin
authors.affiliation: Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan.
authors.forename: Pei-Jung
authors.initials: PJ
authors.lastname: Tsai
authors.affiliation: Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.
authors.forename: Ai-Ling
authors.initials: AL
authors.lastname: Hsu
authors.affiliation: Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan.
authors.forename: Men-Tzung
authors.initials: MT
authors.lastname: Lo
authors.affiliation: Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.
authors.forename: Albert C
authors.initials: AC
authors.lastname: Yang
authors.affiliation: Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan. Electronic address: cplin@ym.edu.tw.
authors.forename: Ching-Po
authors.initials: CP
authors.lastname: Lin
authors.affiliation: Department of Biomedical Sciences and Engineering, National Central University, Taoyuan, Taiwan; Brain and Consciousness Research Center, Taipei Medical University-Shuang Ho Hospital, New Taipei, Taiwan. Electronic address: restingfmri@gmail.com.
authors.forename: Changwei W
authors.initials: CW
authors.lastname: Wu
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio; D055815:Young Adult
keywords: Ensemble empirical mode decomposition (EEMD); Hilbert–Huang transform; Nonlinear; Nonstationary; Resting-state fMRI; Task-fMRI; fMRI sensitivity