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

By Rogier A. Feis, Stephen M. Smith, Nicola Filippini, Gwenaëlle Douaud, E. Dopper, Verena Heise, Aaron J. Trachtenberg, J. V. van Swieten, M. van Buchem, Serge A. R. B. Rombouts and 1 more. 2015

62 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Signal Processing

P. Cook

Citations in the corpus

Invariant Representations without Adversarial Training (2018, neutral): “Modern methods have included more nuanced feature generation and more complex models, but follow along the same vein [5, 6] “Regressing out” or “controlling for” unwanted factors can be effective, but place strong constraints on later analyses (namely, the observation of the same unwanted covariates”
Hand classification of fMRI ICA noise components (2017, unknown): “…it has been shown that the number of artefactual components decreases when effective noise removal is first performed at single subject level (Feis et al., 2015), the noise left in the data, either from mixed/unknown components or in the residuals of single-subject ICA decomposition, can…”
Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data (2018, methodology): “Based on the literature (Dansereau et al., 2017; Friedman et al., 2008; Feis et al., 2015; Rath et al., 2016), we speculated that measurements such as DTI fractional anisotropy (Fortin et al., 2017), MRI cortical thickness (Fortin et al., 2018), and fMRI functional connectivity (the present study)…”
Evading the Adversary in Invariant Representation (2018, unknown): “Modern methods have included more nuanced feature generation and more complex models, but follow along the same vein [5, 6] “Regressing out” or “controlling for” unwanted factors can be effective, but place strong constraints on later analyses (namely, the observation of the same unwanted covariates”