Journal
JOURNAL OF NEUROSCIENCE METHODS
Volume 213, Issue 1, Pages 105-122Publisher
ELSEVIER
DOI: 10.1016/j.jneumeth.2012.10.011
Keywords
Independent component analysis; Denoising; Wavelet-based filtering; Non-stationary Gaussian modelling; Gaussian smoothing
Categories
Funding
- Swiss National Sciences Foundation [31-59516, PP00P2-123438]
- de Reuter Foundation
- Ernst & Lucie Schmidheiny Foundation
- Academic Society of Geneva
- Center for Biomedical Imaging (CIBM) of the Geneva University
- Center for Biomedical Imaging (CIBM) of the Lausanne University
- EPFL
- Geneva University Hospitals
- Lausanne University Hospitals
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Independent component analysis (ICA) is a suitable method for decomposing functional magnetic resonance imaging (fMRI) activity into spatially independent patterns. Practice has revealed that low-pass filtering prior to ICA may improve ICA results by reducing noise and possibly by increasing source smoothness, which may enhance source independence; however, it eliminates useful information in high frequency features and it amplifies low signal fluctuations leading to independence loss. On the other hand, high-pass filtering may increase the independence by preserving spatial information, but its denoising properties are weak. Thus, such filtering strategies did not lead to simultaneous enhancements in independence and noise reduction; therefore, band-pass filtering or more sophisticated filtering methods are expected to be more appropriate. We used advanced wavelet filtering procedures, such as wavelet-based methods relying upon hard and soft coefficient thresholding and non-stationary Gaussian modelling based on geometrical prior information, to denoise artificial and real fMRI data. We compared the performance of these methods with the performance of traditional Gaussian smoothing techniques. First, we demonstrated both analytically and empirically the consistent performance increase of spatial filtering prior to ICA using spatial correlation and statistical sensitivity as quality measures. Second, all filtering methods were computationally efficient. Finally, denoising using low-pass filters was needed to improve ICA, suggesting that noise reduction may have a more significant effect on the component independence than the preservation of information contained within high frequencies. (C) 2012 Elsevier B.V. All rights reserved.
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