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

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posted on 2023-06-09, 05:32 authored by Ottavia Dipasquale, Arjun Sethi, Maria Marcella Laganà, Francesca Baglio, Giuseppe Baselli, Prantik Kundu, Neil Harrison, Mara Cercignani
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 methodsÐregression 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)Ðwith 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.

History

Publication status

  • Published

File Version

  • Published version

Journal

PLoS ONE

ISSN

1932-6203

Publisher

Public Library of Science

Issue

3

Volume

12

Article number

e0173289

Department affiliated with

  • BSMS Neuroscience Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2017-03-22

First Open Access (FOA) Date

2017-03-22

First Compliant Deposit (FCD) Date

2017-03-22

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