Comparing 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 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.
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.|Fondazione don Carlo Gnocchi ONLUS, IRCCS Santa Maria Nascente, Milan, Italy.|Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton, United Kingdom.
Ottavia
http://orcid.org/0000-0002-2867-8222
O
Dipasquale
Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton, United Kingdom.
Arjun
A
Sethi
Fondazione don Carlo Gnocchi ONLUS, IRCCS Santa Maria Nascente, Milan, Italy.
Maria Marcella
MM
Laganà
Fondazione don Carlo Gnocchi ONLUS, IRCCS Santa Maria Nascente, Milan, Italy.
Francesca
F
Baglio
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Giuseppe
G
Baselli
Section on Advanced Functional Neuroimaging, Brain Imaging Center, Icahn School of Medicine at Mount Sinai, New York, United States of America.
Prantik
P
Kundu
Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton, United Kingdom.|Sussex Partnership NHS Foundation Trust, Brighton, United Kingdom.|Sackler Centre for Consciousness Science, University of Sussex, Brighton, United Kingdom.
Neil A
NA
Harrison
Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton, United Kingdom.
Mara
M
Cercignani
D000328:Adult; D016477:Artifacts; D001289:Attention Deficit Disorder with Hyperactivity / Q000000981:diagnostic imaging / Q000503:physiopathology; D001921:Brain / Q000000981:diagnostic imaging* / Q000502:physiology / Q000503:physiopathology; D002560:Cerebrovascular Circulation / Q000502:physiology; D005260:Female; D019416:Head Movements; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010100:Oxygen / Q000097:blood; D012146:Rest
D010100:Oxygen