Bayesian fMRI data analysis with sparse spatial basis function priors

By Guillaume Flandin, W. D. Penny. 2007

113 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistics and Probability

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Citations in the corpus

Variational Inference: A Review for Statisticians (2016, methodology): “There have been many applications of variational inference to neuroscience, especially for autoregressive processes (Roberts and Penny 2002; Penny, Kiebel, and Friston 2003; Penny, Trujillo-Barreto, and Friston 2005; Flandin and Penny 2007; Harrison and Green 2010).”
Mean field variational Bayes for continuous sparse signal shrinkage: Pitfalls and remedies (2014, unknown): “Much of the early MFVB literature treated models with standard distributions such as the Dirichlet, Gamma and Normal families (e.g. Attias [6]; Teschendorff et al. [29]; Flandin and Penny [12]; McGrory and Titterington [23]; Consonni and Marin [10]).”
A Bayesian spatiotemporal model for very large data sets (2010, unknown): “The general pattern is one of SVPs producing smoother estimates, which outperform the joint prior given increased smoothness of GLM coefficients and observation error. fMRI data set Here we show an analysis of the same data set reported elsewhere (Flandin and Penny, 2007; Penny et al., 2005b).”
WSPM: Wavelet-based statistical parametric mapping (2007, background): “, 2005), Bayesian modeling (Turkheimer et al., 2006; Flandin and Penny, 2007), optimizing statistical power while controlling false discovery rate (Sendur et al.”
Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference (2009, background): “…still be value in searching/integrating over a range of smoothings, as suggested in scale-space and wavelet literature going back more than a decade [Worsley et al., 1996a, Lindeberg et al., 1999, Coulon et al., 2000, Fadili and Bullmore, 2004, Flandin and Penny, 2007, Van De Ville et al., 2007].”
ASYMPTOTIC NORMALITY AND VALID INFERENCE FOR GAUSSIAN VARIATIONAL APPROXIMATION (2011, unknown): “Examples of such applications are cluster analysis of gene-expression data [17], fitting spatial models to neuroimage data [6], image segmentation [4] and genome-wide association analysis [8].”
Variational Bayesian inversion of the equivalent current dipole model in EEG/MEG (2008, methodology): “…Bayes provides a fast and efficient approximation to the necessary integrals and has been applied successfully to source imaging in M/EEG (Daunizeau et al., 2007; Sato et al., 2004) and other problems in functional imaging (Flandin and Penny, 2007; Penny et al., 2003; Woolrich and Behrens, 2006).”
A variational Bayes approach to variable selection (2017, background): “Nevertheless, VB has shown to be an effective approach to several practical problems including document retrieval [35], functional magnetic resonance imaging [23, 50], and cluster analysis for gene-expression data [63].”