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Neuroimage Clin. 2017 Jun 30;16:23-31. doi: 10.1016/j.nicl.2017.06.033. eCollection 2017.

Impact of automated ICA-based denoising of fMRI data in acute stroke patients.

Author information

1
Acute Vascular Imaging Centre, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.
2
Laboratory of Experimental Stroke Research, Department of Surgery and Translational Medicine, University of Milano Bicocca, Milan Center of Neuroscience, Monza, Italy.
3
Department of Social Medicine and Public Health, Faculty of Medicine, Palacky University, Olomouc, Czech Republic.
4
Department of Psychiatry, Warneford Hospital, University of Oxford, Oxford, United Kingdom.
5
Oxford Centre of Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
6
Nuffield Department of Clinical Neurosciences, West Wing level 6, JR hospital, Oxford, United Kingdom.

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.

KEYWORDS:

Acute stroke; BOLD; Denoising; Independent component analysis; Resting state; fMRI

PMID:
28736698
PMCID:
PMC5508492
DOI:
10.1016/j.nicl.2017.06.033
[Indexed for MEDLINE]
Free PMC Article

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