Comparing resting state fMRI de-noising approaches using multi- and single-echo acquisitions

Ottavia Dipasquale, Arjun Sethi, Maria Marcella Laganà, Francesca Baglio, Giuseppe Baselli, Prantik Kundu, Neil A Harrison, Mara Cercignani

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Artifact removal in resting state fMRI (rfMRI) data remains a serious challenge, with even subtle head motion undermining reliability and reproducibility. Here we compared some of the most popular single-echo de-noising methods-regression of Motion parameters, White matter and Cerebrospinal fluid signals (MWC method), FMRIB's ICA-based X-noiseifier (FIX) and ICA-based Automatic Removal Of Motion Artifacts (ICA-AROMA)-with a multi-echo approach (ME-ICA) that exploits the linear dependency of BOLD on the echo time. Data were acquired using a clinical scanner and included 30 young, healthy participants (minimal head motion) and 30 Attention Deficit Hyperactivity Disorder patients (greater head motion). De-noising effectiveness was assessed in terms of data quality after each cleanup procedure, ability to uncouple BOLD signal and motion and preservation of default mode network (DMN) functional connectivity. Most cleaning methods showed a positive impact on data quality. However, based on the investigated metrics, ME-ICA was the most robust. It minimized the impact of motion on FC even for high motion participants and preserved DMN functional connectivity structure. The high-quality results obtained using ME-ICA suggest that using a multi-echo EPI sequence, reliable rfMRI data can be obtained in a clinical setting.

© 2017 O. Dipasquale, A. Sethi, M. Lagana, F. Baglio, G. Baselli, P. Kundu, N. Harrison, M. Cercignani. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See  

Original languageEnglish
Pages (from-to)e0173289
JournalPLoS One
Issue number3
Publication statusPublished - 21 Mar 2017


  • Adult
  • Artifacts
  • Attention Deficit Disorder with Hyperactivity/diagnostic imaging
  • Brain/diagnostic imaging
  • Cerebrovascular Circulation/physiology
  • Female
  • Head Movements
  • Humans
  • Image Processing, Computer-Assisted/methods
  • Linear Models
  • Magnetic Resonance Imaging/methods
  • Male
  • Oxygen/blood
  • Rest

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