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

Dipasquale, Ottavia, Sethi, Arjun, Lagana, Maria M, Baglio, Francesca, Baselli, Giuseppe, Kundu, Prantik, Harrison, Neil A and Cercignani, Mara 2017. Comparing resting state fMRI de-noising approaches using multi-and single-echo acquisitions. PloS One 12 (3) , e0173289. 10.1371/journal.pone.0173289

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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 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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Medicine
Publisher: Public Library of Science
ISSN: 1932-6203
Date of First Compliant Deposit: 13 May 2019
Date of Acceptance: 17 February 2017
Last Modified: 15 Jul 2019 09:59
URI: http://orca.cf.ac.uk/id/eprint/121427

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