Comparing dynamic causal models

By W. D. Penny, Klaas E. Stephan, Andrea Mechelli, Karl J. Friston. NeuroImage, 2004

893 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/2191c8f1f283174db0760d18cfd7daee6a3c251e

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A Dual Role for Prediction Error in Associative Learning (2008, neutral): “These models were fitted separately to each subject’s data and compared using Bayesian model selection (Penny et al. 2004).”
Comparing hemodynamic models with DCM (2007, neutral): “We then compare the DCMs with different BMs by means of Bayesian model selection (BMS; Penny et al., 2004a).”
Dynamic causal modeling for EEG and MEG (2009, methodology): “Bayesian model comparison is used to decide which model, amongst a set of competing models, best explains the data [Penny et al., 2004].”
Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. (2007, result): “Our results demonstrated the basic organizational principles for the anatomical network in the human brain compatible with previous functional networks studies, which provides important implications of how functional brain states originate from their structural underpinnings.”
The anatomy of choice: active inference and agency (2013, background): “This divergence is known as model complexity (Spiegelhalter et al., 2002; Penny et al., 2004), ensuring that inference is both accurate and parsimonious (cf. Occam’s razor).”
Variational free energy and the Laplace approximation (2007, methodology): “The log-evidence plays a central role in model selection, comparison and averaging (for examples in neuroimaging, see Penny et al., 2004; Trujillo- Barreto et al., 2004).”
Bayesian mixed-effects inference on classification performance in hierarchical data sets (2012, background): “As mentioned above, a principled way of evaluating such different propositions rests upon Bayesian model comparison (MacKay, 1992; Madigan and York, 1997; Penny et al., 2004), which we illustrate by deciding between alternative parameterizations for inference on the balanced accuracy.”
A free energy principle for a particular physics (2019, background): “, the best explanation provides an accurate account with the smallest change in posterior beliefs relative to prior beliefs (Penny et al., 2004).”
Behavioral / Systems / Cognitive Striatal Prediction Error Modulates Cortical Coupling (2010, neutral): “BMS provides a principled foundation for comparing competing models of different complexity (Penny et al., 2004).”
Bayesian analysis of neuroimaging data in FSL (2009, methodology): “The problem can then be solved using Variational Bayes in the normal way (Penny et al., 2004; Friston et al., 2007; Woolrich and Behrens, 2006; Chappell et al., 2008).”
Modelling event-related responses in the brain (2005, methodology): “inference procedures for dynamic causal modelling of functional MRI (Friston et al., 2003; Penny et al., 2004 ).”
A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula (2016, methodology): “The framework of Dynamic Causal Modeling [Friston et al., 2003] can also indicate that connectivity between two brain regions is modulated by an external stimulus or condition such as attention [Penny et al., 2004].”
Nonlinear Dynamic Causal Models for fMRI (2008, positive): “…have been used in previous analyses of effective connectivity (Büchel and Friston 1997; Friston and Büchel 2000; Friston et al., 2003; Harrison et al., 2003; Marreiros et al., 2008; Penny et al., 2004a, b); a full description of the experimental paradigm can be found in Büchel and Friston (1997).”