Dipasquale, Ottavia, Sethi, Arjun, Laganà, Maria Marcella, 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. ISSN 1932-6203

PDF - Published Version Available under License Creative Commons Attribution. Download (3MB)

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 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.

Item Type:	Article
Schools and Departments:	Brighton and Sussex Medical School > Neuroscience
Subjects:	R Medicine > R Medicine (General) > R895 Medical physics. Medical radiology. Nuclear medicine
Depositing User:	Patricia Butler
Date Deposited:	22 Mar 2017 14:12
Last Modified:	19 Jul 2017 04:28
URI:	http://srodev.sussex.ac.uk/id/eprint/67157

View download statistics for this item

📧 Request an update