ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI

Abstract

Resting-state fMRI (R-fMRI) has shown considerable promise in providing potential biomarkers for diagnosis, prognosis and drug response across a range of diseases. Incorporating R-fMRI into multi-center studies is becoming increasingly popular, imposing technical challenges on data acquisition and analysis, as fMRI data is particularly sensitive to structured noise resulting from hardware, software, and environmental differences. Here, we investigated whether a novel clean up tool for structured noise was capable of reducing center-related R-fMRI differences between healthy subjects. We analyzed three Tesla R-fMRI data from 72 subjects, half of whom were scanned with eyes closed in a Philips Achieva system in The Netherlands, and half of whom were scanned with eyes open in a Siemens Trio system in the UK. After pre-statistical processing and individual Independent Component Analysis (ICA), FMRIB's ICA-based X-noiseifier (FIX) was used to remove noise components from the data. GICA and dual regression were run and non-parametric statistics were used to compare spatial maps between groups before and after applying FIX. Large significant differences were found in all resting-state networks between study sites before using FIX, most of which were reduced to non-significant after applying FIX. The between-center difference in the medial/primary visual network, presumably reflecting a between-center difference in protocol, remained statistically significant. FIX helps facilitate multi-center R-fMRI research by diminishing structured noise from R-fMRI data. In doing so, it improves combination of existing data from different centers in new settings and comparison of rare diseases and risk genes for which adequate sample size remains a challenge.

Statistical harmonization corrects site effects in functional connectivity measurements from multi‐site fMRI data

“…These additional sources of unwanted variability may decrease statistical power and lead to spurious results. Many multi-site studies have reported considerable site or scanner effects in fMRI data (Friedman et al, 2006, 2008; Suckling et al, 2008, 2010; Van Horn and Toga, 2009; Gountouna et al, 2010; Brown et al, 2011; McGonigle, 2012; Turner et al, 2013; Forsyth et al, 2014; Feis et al, 2015; Rath et al, 2016; Jovicich et al, 2016; Dansereau et al, 2017; Noble et al, 2017; Abraham et al, 2017). However, most of these studies only describe the problem or report the magnitude of site effects in fMRI measurements.…”

“…To our knowledge, until now, there has been only one attempt to diminish scanner differences in multi-site resting-state fMRI post-acquisition. The authors used an independent component analysis (ICA) based approach that reduced differences across sites in some resting-state network connectivity measures but did not fully eliminate the structured noise arising from different scanners (Feis et al, 2015). …”

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

“…While straightforward to implement in single-subject analyses, group ICA analyses are more complex and require choosing between several different workflows and algorithm definitions (Beckmann and Smith, 2004; Calhoun et al, 2009; Schöpf et al, 2010; Du et al, 2016). ICA methods also have been used extensively in rs-fMRI studies (Beckmann et al, 2005; Soares et al, 2016), task-based fMRI (Calhoun et al, 2008), and for artifact removal (Perlbarg et al, 2007; Feis et al, 2015; Pruim et al, 2015). …”

EEG-Based Person Authentication Using a Fuzzy Entropy-Related Approach with Two Electrodes

“…The purpose of data preprocessing is to obtain the data set of segmented EEG signals, including the removal of EOG (Electro-Oculogram) [33][34][35][36], epoch, filtering, etc. In this paper, data preprocessing is realized using Scan 4.3 software (Neuroscan).…”