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

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

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 multiecho 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.

Original languageEnglish
Article numbere0173289
JournalPLoS One
Volume12
Issue number3
DOIs
Publication statusPublished - Mar 1 2017

Fingerprint

Independent component analysis
Magnetic Resonance Imaging
scanners
cerebrospinal fluid
cleaning
reproducibility
Head
Cerebrospinal fluid
methodology
Artifacts
Cleaning
Aptitude
Attention Deficit Disorder with Hyperactivity
Cerebrospinal Fluid
Healthy Volunteers

ASJC Scopus subject areas

  • Medicine(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

Comparing resting state fMRI de-noising approaches using multi-and single-echo acquisitions. / Dipasquale, Ottavia; Sethi, Arjun; Laganà, Maria Marcella; Baglio, Francesca; Baselli, Giuseppe; Kundu, Prantik; Harrison, Neil A.; Cercignani, Mara.

In: PLoS One, Vol. 12, No. 3, e0173289, 01.03.2017.

Research output: Contribution to journalArticle

Dipasquale, Ottavia ; Sethi, Arjun ; Laganà, Maria Marcella ; Baglio, Francesca ; Baselli, Giuseppe ; Kundu, Prantik ; Harrison, Neil A. ; Cercignani, Mara. / Comparing resting state fMRI de-noising approaches using multi-and single-echo acquisitions. In: PLoS One. 2017 ; Vol. 12, No. 3.
@article{cb283c810fd147c3bd2dca6f05fb35b3,
title = "Comparing resting state fMRI de-noising approaches using multi-and single-echo acquisitions",
abstract = "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 multiecho 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.",
author = "Ottavia Dipasquale and Arjun Sethi and Lagan{\`a}, {Maria Marcella} and Francesca Baglio and Giuseppe Baselli and Prantik Kundu and Harrison, {Neil A.} and Mara Cercignani",
year = "2017",
month = "3",
day = "1",
doi = "10.1371/journal.pone.0173289",
language = "English",
volume = "12",
journal = "PLoS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "3",

}

TY - JOUR

T1 - Comparing resting state fMRI de-noising approaches using multi-and single-echo acquisitions

AU - Dipasquale, Ottavia

AU - Sethi, Arjun

AU - Laganà, Maria Marcella

AU - Baglio, Francesca

AU - Baselli, Giuseppe

AU - Kundu, Prantik

AU - Harrison, Neil A.

AU - Cercignani, Mara

PY - 2017/3/1

Y1 - 2017/3/1

N2 - 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 multiecho 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.

AB - 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 multiecho 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.

UR - http://www.scopus.com/inward/record.url?scp=85016054920&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85016054920&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0173289

DO - 10.1371/journal.pone.0173289

M3 - Article

AN - SCOPUS:85016054920

VL - 12

JO - PLoS One

JF - PLoS One

SN - 1932-6203

IS - 3

M1 - e0173289

ER -