Constrained linear basis sets for HRF modelling using Variational Bayes

By Mark William Woolrich, Timothy Edward John Behrens, Stephen M. Smith. NeuroImage, 2004

310 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Pediatrics, Perinatology and Child Health, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e3e629790fc0e25f85fbf162ce3a2b581f6b5df7

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Citations in the corpus

Comparing Families of Dynamic Causal Models (2010, methodology): “The Free Energy is but one approximation to the model evidence, albeit one that is widely used in neuroimaging [29,30].”
Advances in functional and structural MR image analysis and implementation as FSL. (2004, methodology): “Therefore, in [41] we proposed a technique for choosing a basis set, and then, importantly, the means to constrain the subspace spanned by the basis set to only include sensible HRF shapes.”
Building a Science of Individual Differences from fMRI. (2016, background): “Popular constrained basis sets include the canonical HRF plus its temporal and dispersion derivatives [52], or a basis function set based on singular value decomposition [53].”
Bayesian analysis of neuroimaging data in FSL (2009, methodology): “However, it is possible for a basis set to produce nonsensical HRFs. Priors can be placed on the basis function regression parameters such that we constrain the inference to only those combinations of parameters that give biophysically plausible HRF shapes (Woolrich et al., 2004b).”
Modeling the Hemodynamic Response Function in fMRI: Efficiency, Bias and Mis-modeling (2008, methodology): “Other approaches include the use of basis sets composed of principal components (Aguirre et al., 1998; Woolrich et al., 2004), cosine functions (Zarahn, 2002), radial basis functions (Riera et al.”
Bayesian fMRI time series analysis with spatial priors (2005, methodology): “These priors have been used in functional imaging by (Gossl et al., 2001) to spatially regularise regression coefficients and by (Woolrich et al., 2004a) to spatially regularise AR coefficients.”
A Bayesian spatiotemporal model for very large data sets (2010, unknown): “Sampling methods have been used to estimate such models for fMRI data (Gossl et al., 2001; Woolrich et al., 2004b), however, compute times can be several hours for one slice.”
Handbook of Functional MRI Data Analysis: Index (2011, methodology): “This is the approach taken by Woolrich et al. (2004a) in the FMIRB Linear Optimal Basis Set (FLOBS) algorithm.”