Bayesian fMRI time series analysis with spatial priors

By W. D. Penny, Nelson J. Trujillo-Barreto, Karl J. Friston. 2005

277 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Karl J. Friston
2. W. D. Penny
3. Jean Daunizeau
4. Nelson J. Trujillo-Barreto
5. John Ashburner
6. Jérémie Mattout
7. Klaas E. Stephan
8. Guillaume Flandin

In Signal Processing

1. Karl J. Friston
2. Jean Daunizeau
3. W. D. Penny
4. Guillaume Flandin
5. Jérémie Mattout
6. David M. Blei
7. Alp Kucukelbir
8. Jon D. McAuliffe

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).”
• A guide to group effective connectivity analysis, part 2: Second level analysis with PEB (2019, background): “Hierarchical Bayesian modelling is an established approach in the context of functional imaging data (Woolrich et al., 2004; Penny et al., 2005).”
• Variational free energy and the Laplace approximation (2007, methodology): “Bayesian inversion using VB is ubiquitous in neuroimaging (e.g., Penny et al., 2005).”
• Bayesian fMRI data analysis with sparse spatial basis function priors (2007, methodology): “…problem is the Variational Bayes (VB) framework (see appendix B) (Lappalainen and Miskin, 2000; Beal, 2003), already used in previous publications (Penny et al., 2003, 2005b), which provides an approximate factorised posterior distribution q(Θ|Y ), with minimal Kullback-Leibler (KL) divergence…”
• A Bayesian spatiotemporal model for very large data sets (2010, unknown): “Gaussian Markov random field priors and the mean field approximation We now consider the second aside on the MFA described by Penny et al. (2005b).”
• DEM: A variational treatment of dynamic systems (2008, background): “The inversion of these models enables inference about parameters that control coupling among brain areas (see Friston et al., 2003; Penny et al., 2005).”