Classical and Bayesian Inference in Neuroimaging: Applications

By Karl J. Friston, Daniel Glaser, Richard N. Henson, Stefan J. Kiebel, John Ashburner. NeuroImage, 2002

747 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0fbfea70a83e2fce32ae73a00ffe91a83e2abc79

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Citations in the corpus

Activity in the human brain predicting differential heart rate responses to emotional facial expressions (2005, background): “fixed deterministic drifts (removed by high-pass filtering) and stochastic short-term correlations conforming to an AR(1) process (http://www.fil.ion.ucl.ac.uk/spm/spm2.html; Friston et al., 2002).”
Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ) (2011, methodology): “The patterns of parameter changes were assessed using standard mass univariate statistical analysis (Friston et al., 2002).”
Dynamic causal modeling of cortical activity from the acute to the chronic stage after stroke (2011, neutral): “For the group analysis, first-level contrast images of patients who attended all three assessments (n = 10) were entered into a full factorial random-effects analysis (Friston et al., 1999, 2002) with the within-subject factors HAND and SESSION.”
Classical and Bayesian Inference in Neuroimaging: Theory (2002, methodology): “In Friston et al. (2002) we use the same theory to elaborate spatiotemporal models for PET.”
Ten ironic rules for non-statistical reviewers (2012, background): “…because we are dealing with between-subject comparisons, we can ignore the distinction between fixed and mixed-effects models and the composition of random effects — these issues have been considered previously from a Bayesian and classical perspective (Friston et al., 2002, 2005, respectively).”
Separating the Brain Regions Involved in Recollection and Familiarity in Recognition Memory (2005, methodology): “Nonsphericity of the error covariance was accommodated by an autoregressive (1) model, in which the temporal autocorrelation was estimated by pooling over suprathreshold voxels (Friston et al., 2002).”
A Dual Role for Prediction Error in Associative Learning (2008, neutral): “The data were high-pass filtered (cut-off 128 s) to remove low-frequency signal drifts, and a first-order autoregressive model was used to model the remaining serial correlations (Friston et al. 2002).”
Spatial attention can modulate audiovisual integration at multiple cortical and subcortical sites (2009, methodology): “Correction for non-sphericity ( Friston et al., 2002 ) was used to account for possible differences in error variance across conditions and any non-independent error terms for the repeated measures.”
WSPM: Wavelet-based statistical parametric mapping (2007, methodology): “For our framework, we choose to incorporate the method deployed by SPM (Worsley and Friston, 1995; Friston et al., 2000, 2002).”
Audiovisual integration of emotional signals in voice and face: An event-related fMRI study (2007, methodology): “2 (Friston et al., 2002) and an additional white noise component (Purdon and Weisskoff, 1998).”
Bayesian Estimation of Dynamical Systems: An Application to fMRI (2002, methodology): “The nature of Bayesian inference is much more straightforward and as discussed in Friston et al. (2002a) is relatively immune from the multiple comparison problem.”
Variational Bayesian inference for fMRI time series (2003, methodology): “This constitutes the VB solution to the overconfidence problem (Friston et al., 2002a).”
Bayesian fMRI time series analysis with spatial priors (2005, methodology): “, non-adaptive) priors and projection of the data onto a subspace spanned by these priors (see, for example, Section 3 of (Friston et al., 2002)) or used adaptive priors but the computationally expensive MCMC algorithm to sample from the relevant posterior distributions (Gossl et al.”