Abstract
Different strategies have been developed using Independent Component Analysis (ICA) to automatically de-noise fMRI data, either focusing on removing only certain components (e.g. motion-ICA-AROMA, Pruim et al., 2015a) or using more complex classifiers to remove multiple types of noise components (e.g. FIX, Salimi-Khorshidi et al., 2014 Griffanti et al., 2014). However, denoising data obtained in an acute setting might prove challenging: the presence of multiple noise sources may not allow focused strategies to clean the data enough and the heterogeneity in the data may be so great to critically undermine complex approaches. The purpose of this study was to explore what automated ICA based approach would better cope with these limitations when cleaning fMRI data obtained from acute stroke patients.The performance of a focused classifier (ICA-AROMA) and a complex classifier (FIX) approaches were compared using data obtained from twenty consecutive acute lacunar stroke patients using metrics determining RSN identification, RSN reproducibility, changes in the BOLD variance, differences in the estimation of functional connectivity and loss of temporal degrees of freedom.The use of generic-trained FIX resulted in misclassification of components and significant loss of signal (< 80%), and was not explored further. Both ICA-AROMA and patient-trained FIX based denoising approaches resulted in significantly improved RSN reproducibility (p < 0.001), localized reduction in BOLD variance consistent with noise removal, and significant changes in functional connectivity (p < 0.001). Patient-trained FIX resulted in higher RSN identifiability (p < 0.001) and wider changes both in the BOLD variance and in functional connectivity compared to ICA-AROMA.The success of ICA-AROMA suggests that by focusing on selected components the full automation can deliver meaningful data for analysis even in population with multiple sources of noise. However, the time invested to train FIX with appropriate patient data proved valuable, particularly in improving the signal-to-noise ratio.
Highlights
After acute brain injury, brain plasticity and reorganization underlie functional recovery, and improvement is mediated by adaptations in brain network connectivity (Andrews, 1991)
Independent component analysis (ICA) based denoising approaches were successfully applied to rsfMRI data obtained from acute stroke patients resulting in improved Resting State Networks (RSN) reproducibility, localized reduction in BOLD variance consistent with noise removal and significant changes in functional connectivity
Compared to ICA-AROMA, patient-trained Functional Magnetic Resonance Imaging of the Brain (FMRIB)'s ICA-based X-noiseifier (FIX) resulted in higher RSN identifiability and wider changes both in the BOLD variance and in functional connectivity
Summary
Brain plasticity and reorganization underlie functional recovery, and improvement is mediated by adaptations in brain network connectivity (Andrews, 1991). Overcoming the contribution of noise in the acquisition of rs-fMRI data is important in limiting false observations and reliably estimating functional connectivity in Resting State Networks (RSN) (Raichle and Snyder, 2007; Friston et al, 1996; Dagli et al, 1999; Glover et al, 2000; Windischberger et al, 2002). Independent component analysis (ICA) can be used to reliably separate signal from noise, enabling artefacts to be regressed out of the data thereby allowing a significant improvement on results obtained with traditional pre-processing (Stone et al, 2002; Thomas et al, 2002; Kochiyama et al, 2005; McKeown et al, 2005; Zou et al, 2009; Zuo and Xing, 2014). While manual component classification has been widely used as the gold standard (De Martino et al, 2007; Bhaganagarapu et al, 2013; Rummel et al, 2013; Salimi-Khorshidi et al, 2014), it is time-consuming, operator dependent and requires expert knowledge to separate signal and noise characteristics
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