title: The impact of denoising on independent component analysis of functional magnetic resonance imaging data.
abstract: 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.
authors.affiliation: Division of Neurology, Department of Clinical Neurosciences, University of Geneva, Geneva University Hospitals, CH-1211 Geneva 14, Switzerland. jean-michel.pignat@hcuge.ch
authors.forename: Jean Michel
authors.initials: JM
authors.lastname: Pignat
authors.forename: Oleksiy
authors.initials: O
authors.lastname: Koval
authors.forename: Dimitri
authors.initials: D
authors.lastname: Van De Ville
authors.forename: Sviatoslav
authors.initials: S
authors.lastname: Voloshynovskiy
authors.forename: Christoph
authors.initials: C
authors.lastname: Michel
authors.forename: Thierry
authors.initials: T
authors.lastname: Pun
mesh_terms: D003627:Data Interpretation, Statistical; D007091:Image Processing, Computer-Assisted / Q000379:methods / Q000706:statistics & numerical data*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods / Q000706:statistics & numerical data*; D015233:Models, Statistical; D016011:Normal Distribution; D010100:Oxygen / Q000097:blood; D025341:Principal Component Analysis; D059629:Signal-To-Noise Ratio; D058067:Wavelet Analysis
chemical_list: D010100:Oxygen