biostudies-literatureUnknown12(3)Dipasquale OArtifact 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 procedurePloS onee0173289https://www.ebi.ac.uk/biostudies/studies/S-EPMC5360253biostudies-literatureComparing resting state fMRI de-noising approaches using multi- and single-echo acquisitions.PMC5360253Baglio FKundu PSethi ALagana MMDipasquale OCercignani MBaselli GHarrison NAfalseComparing resting state fMRI de-noising approaches using multi- and single-echo acquisitions.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 procedure2017-01-01T00:00:00Z20172025-04-04T12:40:09.586Z2019-03-26T22:49:45ZS-EPMC53602532832382110.1371/journal.pone.0173289