Modelling functional integration: a comparison of structural equation and dynamic causal models.

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

352 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a630578f24269c0b4a2c61a61aabe310293f64ff

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Dynamic causal modelling: A critical review of the biophysical and statistical foundations (2011, background): “Dynamic Causal Modelling: criticisms.”
A validated network of effective amygdala connectivity (2007, background): “Dynamic Causal Modeling (DCM) is able to take this information into account and is also better able to represent neural interactions directly by explicitly modeling the dependence of BOLD signal on neural response (Penny et al., 2004).”
Partial correlation for functional brain interactivity investigation in functional MRI (2006, methodology): “Methods have been developed to cope with this issue (Bullmore et al., 2000; Mechelli et al., 2002; Penny et al., 2004a).”
Effective connectivity: Influence, causality and biophysical modeling (2011, positive): “Thus one could expect that (stochastic) DCMmight perform better, as supported by a comparison of SEM and DCM (Penny et al., 2004).”
Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples (2012, methodology): “Since GIMME estimates connectivity maps from an SEM framework, data sets were simulated using generative approaches similar to those seen elsewhere in fMRI literature examining SEM reliability (e.g., 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).”
Health, USA Reviewed by: (2010, neutral): “If so, then dynamic causal modeling (DCM) (Friston et al., 2003; Penny et al., 2004a; Sonty et al., 2007; Stephan et al., 2008; Rowe et al., 2010), SEM (McIntosh and Gonzalez-Lima, 1994; Buchel and Friston, 1997; Rowe et al., 2002b; Sharma et al., 2009) or Granger causality modeling (GCM)…”
Handbook of Functional MRI Data Analysis: Index (2011, background): “The details of this estimation are quite complicated, and the interested reader should consult (Friston et al., 2003; Penny et al., 2004; Stephan et al., 2007) for more details.”
Resolving emotional conflict: a role for the rostral anterior cingulate cortex in modulating activity in the amygdala. (2006, background): “Dynamic causal modeling can indicate the directionality of interregional interactions, within the context of a model composed of a priori-defined anatomical regions with predetermined connections (Friston et al., 2003; Penny et al., 2004).”
Ten Simple Rules for Dynamic Causal Modelling (2009, neutral): “Gibbs sampling must be used instead (Penny et al., submitted for publication). 11 Computational efficiency of model selection procedures is becoming an increas3104 K.E. Stephan et al. / NeuroImage 49 (2010) 3099–3109”
Modulating cortical connectivity in stroke patients by rTMS assessed with fMRI and dynamic causal modeling (2010, neutral): “…with earlier data (Eickhoff et al., 2008; Grefkes et al., 2008a,b) the model evidence was approximated using both the Bayesian Information Criterion (BIC) and the Akaike Information Criterion (AIC), and a decision was only made if BIC and AIC concurred (Penny et al., 2004; Stephan et al., 2007a,b).”
Dynamic intra- and interhemispheric interactions during unilateral and bilateral hand movements assessed with fMRI and DCM (2008, methodology): “2) reflecting biologically plausible hypotheses about the context-specific modulations of interregional coupling, and used Bayesian model selection (Penny et al., 2004b) as implemented in SPM5 to identify that connectivity model showing the highest (posterior) evidence in the applied Bayesian…”
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).”