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.
Highlights
Resting-state functional Magnetic Resonance Imaging (R-fMRI) has become an important tool in neuroimaging research to examine Resting-State Networks (RSNs) in normal brains, during the aging process and in various neurological disorders (Greicius et al, 2003; Fox et al, 2005; De Luca et al, 2006; Fox and Raichle, 2007; Littow et al, 2010)
As data sharing becomes more important in imaging research [e.g., the Genetic Frontotemporal dementia Initiative (GenFI), Rohrer et al, 2013; 1000 Functional Connectome Project, Biswal et al, 2010; ADHD 200 Consortium dataset (The ADHD-200 Consortium, 2012); and Autism Brain Imaging Data Exchange (ABIDE), Di Martino et al, 2014], methods for reducing scan site differences must be developed
MRI data was included from subjects scanned at the Leiden University Medical Centre (LUMC) and from subjects scanned at the University of Oxford Centre for Clinical Magnetic Resonance Research (OCMR)
Summary
Resting-state functional Magnetic Resonance Imaging (R-fMRI) has become an important tool in neuroimaging research to examine Resting-State Networks (RSNs) in normal brains, during the aging process and in various neurological disorders (Greicius et al, 2003; Fox et al, 2005; De Luca et al, 2006; Fox and Raichle, 2007; Littow et al, 2010). FIX diminishes scan site differences is Independent Component Analysis (ICA)—a data-driven technique that facilitates comparison of functional networks in the brain without requiring a priori selected seed regions (Beckmann and Smith, 2004). Biswal et al (2010) demonstrated that functional connectivity has a universal architecture in an extensive study with 1414 subjects. They found many differences due to center-related variability. As data sharing becomes more important in imaging research [e.g., the Genetic Frontotemporal dementia Initiative (GenFI), Rohrer et al, 2013; 1000 Functional Connectome Project, Biswal et al, 2010; ADHD 200 Consortium dataset (The ADHD-200 Consortium, 2012); and Autism Brain Imaging Data Exchange (ABIDE), Di Martino et al, 2014], methods for reducing scan site differences must be developed
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