title: Improving the use of principal component analysis to reduce physiological noise and motion artifacts to increase the sensitivity of task-based fMRI.
abstract: BACKGROUND
  Functional magnetic resonance imaging (fMRI) time series are subject to corruption by many noise sources, especially physiological noise and motion. Researchers have developed many methods to reduce physiological noise, including RETROICOR, which retroactively removes cardiac and respiratory waveforms collected during the scan, and CompCor, which applies principal components analysis (PCA) to remove physiological noise components without any physiological monitoring during the scan.
  
  
  METHODS
  We developed four variants of the CompCor method. The optimized CompCor method applies PCA to time series in a noise mask, but orthogonalizes each component to the BOLD response waveform and uses an algorithm to determine a favorable number of components to use as "nuisance regressors." Whole brain component correction (WCompCor) is similar, except that it applies PCA to time-series throughout the whole brain. Low-pass component correction (LCompCor) identifies low-pass filtered components throughout the brain, while high-pass component correction (HCompCor) identifies high-pass filtered components.
  
  
  CONCLUSIONS
  We compared the new methods with the original CompCor method by examining the resulting functional contrast-to-noise ratio (CNR), sensitivity, and specificity.
  
  
  RESULTS
  (1) The optimized CompCor method increased the CNR and sensitivity compared to the original CompCor method and (2) the application of WCompCor yielded the best improvement in the CNR and sensitivity.
  
  
  CONCLUSIONS
  The sensitivity of the optimized CompCor, WCompCor, and LCompCor methods exceeded that of the original CompCor method. However, regressing noise signals showed a paradoxical consequence of reducing specificity for all noise reduction methods attempted.
authors.affiliation: Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Office of Medical Products and Tobacco, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD 20993, USA. Electronic address: david.soltysik@fda.hhs.gov.
authors.forename: David A
authors.initials: DA
authors.lastname: Soltysik
authors.affiliation: Integrated Research Facility, National Institute of Allergy and Infectious Disease, Fort Detrick, MD 21702, USA.
authors.forename: David
authors.initials: D
authors.lastname: Thomasson
authors.affiliation: Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Office of Medical Products and Tobacco, U.S. Food and Drug Administration, 10903 New Hampshire Ave, Silver Spring, MD 20993, USA.
authors.forename: Sunder
authors.initials: S
authors.lastname: Rajan
authors.affiliation: Radiology and Imaging Sciences, Warren Grant Magnuson Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA.
authors.forename: Nadia
authors.initials: N
authors.lastname: Biassou
mesh_terms: D000328:Adult; D016477:Artifacts; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000592:standards; D008279:Magnetic Resonance Imaging / Q000592:standards*; D008297:Male; D008875:Middle Aged; D009068:Movement / Q000502:physiology*; D025341:Principal Component Analysis / Q000592:standards*; D011597:Psychomotor Performance / Q000502:physiology*; D066191:Sensorimotor Cortex / Q000502:physiology*
keywords: BOLD fMRI; Physiological noise, Principal components analysis