Classical and Bayesian Inference in Neuroimaging: Theory

By Karl J. Friston, W. D. Penny, Christophe L. M. Phillips, Stefan J. Kiebel, John Ashburner. NeuroImage, 2002

641 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/fee78005a74d7da336f1979f10ed16b0f8042482

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Citations in the corpus

A neural mass model for MEG/EEG: : coupling and neuronal dynamics (2003, neutral): “See also Robinson et al. (2001); Friston (2002) and Friston et al. (2002).”
Audiovisual Synchrony Improves Motion Discrimination via Enhanced Connectivity between Early Visual and Auditory Areas (2010, methodology): “The data were high-pass filtered to 1/128 Hz and a first-order autoregressive model was used to model the remaining serial correlations (Friston et al., 2002).”
Bayesian Estimation of Dynamical Systems: An Application to fMRI (2002, methodology): “In the first paper (Friston et al., 2002a) we discussed the utility of Bayesian inference in the context of hierarchical observation models commonly employed in fMRI.”
Advances in functional and structural MR image analysis and implementation as FSL. (2004, methodology): “In [17] the hierarchical model is approached using all time-series data as input to a single “all-in-one” analysis.”
The Statistical Analysis of fMRI Data. (2008, background): “These are the assumptions made in the hugely popular GLM approach (Worsley and Friston, 1995; Friston et al., 2002), though the assumption regarding fixed HRF can be relaxed.”
Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution (2003, background): “The notation and form of the equations are consistent with the derivations presented in Friston et al. (2002) to which the interested reader is referred for a full derivation (Friston et al., 2002).”
Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses (2007, positive): “This is a very sketchy account of possible sources of variability across subjects, but one should remember that all these eﬀects equally treated as confounds and globally modeled as second-level variability [Friston et al., 2002] terms in current linear models.”
A Bayesian model of shape and appearance for subcortical brain segmentation (2011, positive): “This empirical approach to setting the parameter values is common to many medical imaging problems (Friston et al., 2002).”
Variational free energy and the Laplace approximation (2007, methodology): “An important application here is the estimation of nonsphericity over voxels in the analysis of fMRI time-series (see Friston et al., 2002, for this use of spm _ ReML.m).”
Modelling event-related responses in the brain (2005, methodology): “In forthcoming studies, we will describe the estimation of such models in a Bayesian framework (Friston et al., 2002) and will apply the present model to the analysis of real ERPs.”
Dynamic causal modelling of evoked responses in EEG/MEG with lead field parameterization (2006, neutral): “This scheme is identical to that employed by DCM for fMRI, the details of which can be found in Friston et al. (2002), (2003).”
WSPM: Wavelet-based statistical parametric mapping (2007, methodology): “Several studies have recommended the ROC approach for validating and comparing statistical methods, e.g., Friston et al. (1996b), Genovese et al. (1997), Skudlarski et al. (1999), Maitra et al. (2002), Liou et al. (2003), Fadili and Bullmore (2004), and Liou et al. (2006).”
Multilevel linear modelling for FMRI group analysis using Bayesian inference (2004, methodology): “Whether we are looking to infer at the top level with the within-session FMRI time series data (Friston et al., 2002) or with summary statistic results from the level below (Holmes and Friston, 1998; Worsley et al., 2002), a fully Bayesian approach provides us with the means to assess the full…”
NIRS-SPM: statistical parametric mapping for near infrared spectroscopy (2008, positive): “where KK=V and V is estimated via restricted maximum likelihood (ReML) (Friston et al., 2002).”