Integrated wavelet processing and spatial statistical testing of fMRI data

By D. Van de Ville, Thierry Blu, Michael Unser. 2004

78 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Signal Processing

Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, neutral): “Machine learning based methods Davatzikos (2004) Cuingnet et al. (2011) De Martino et al. (2008) Fan et al. (2007) Gaonkar and Davatzikos (2012) Xu et al. (2009) Van De Ville et al. (2007) Van De Ville et al. (2004) Mingoia et al. (2012) can offer diagnostics based on neuroimaging data.”
Wavelets on Graphs via Spectral Graph Theory (2009, neutral): “Classical wavelet methods have been studied for use in fMRI processing, both for denoising in the image domain [52] and for constructing statistical hypothesis testing [53,54].”
RAPTT: An Exact Two-Sample Test in High Dimensions Using Random Projections (2014, unknown): “Testing of the equality of means among high-dimensional populations occurs, for example, in biological applications (Goeman and Buhlmann, 2007; Ville et al., 2004).”
Bayesian fMRI data analysis with sparse spatial basis function priors (2007, unknown): “The use of wavelets for fMRI has already been proposed in the literature (Ruttimann et al., 1998; Van De Ville et al., 2004; Aston et al., 2005, 2006), see (Bullmore et al., 2004) for an overview.”
WSPM: Wavelet-based statistical parametric mapping (2007, unknown): “The main result of our integrated framework (Van De Ville et al., 2004) states that the statistical test amounts to checking whether or not ũ[n] ≥ τ s Λ[n], where τ s is the threshold in the spatial domain and Λ[n] is the reconstruction of the values s w ½k= ffiffiffi J p by a modified inverse DWT…”
A More Powerful Two-Sample Test in High Dimensions using Random Projection (2011, unknown): “Application domains such as molecular biology and fMRI [e.g., 1, 2, 3, 4] have stimulated considerable interest in two-sample hypothesis testing problems in the high-dimensional setting, where two samples of data { X 1 , . . . , X n 1 } and { Y 1 , . . . , Y n 2 } are subsets of R p , and n 1 , n 2…”