dipasquale_plosone2017.pdf (3.43 MB)
Comparing resting state fMRI de-noising approaches using multi- and single-echo acquisitions
journal contribution
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 CercignaniArtifact 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.
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Publication status
- Published
File Version
- Published version
Journal
PLoS ONEISSN
1932-6203Publisher
Public Library of ScienceExternal DOI
Issue
3Volume
12Article number
e0173289Department affiliated with
- BSMS Neuroscience Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2017-03-22First Open Access (FOA) Date
2017-03-22First Compliant Deposit (FCD) Date
2017-03-22Usage metrics
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