Ten Simple Rules for Dynamic Causal Modelling

By Klaas E. Stephan, W. D. Penny, Rosalyn J. Moran, H. D. den Ouden, Jean Daunizeau, Karl J. Friston. NeuroImage, 2009

839 citations recorded by the source corpus.

Dynamic causal modelling (DCM) is a generic Bayesian framework for inferring hidden neuronal states from measurements of brain activity. It provides posterior estimates of neurobiologically interpretable quantities such as the effective strength of synaptic connections among neuronal populations and their context-dependent modulation. DCM is increasingly used in the analysis of a wide range of neuroimaging and electrophysiological data. Given the relative complexity of DCM, compared to conventional analysis techniques, a good knowledge of its theoretical foundations is needed to avoid pitfalls in its application and interpretation of results. By providing good practice recommendations for DCM, in the form of ten simple rules, we hope that this article serves as a helpful tutorial for the growing community of DCM users.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/7025272ce0ea2b3d47012d19d7a3f3568cde2835

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In Cognitive Neuroscience

Citations in the corpus

Neural Correlates of Multisensory Perceptual Learning (2012, methodology): “Dynamic causal modeling (Friston et al., 2003; Lee et al., 2006; Kasess et al., 2010; Stephan et al., 2010) of neural responses to A, V, and AV presentation blocks during the blocked-design functional runs was implemented through the use of the Dynamic Causal Modeling functionality that is part of t”
Comparing Dynamic Causal Models using AIC, BIC and Free Energy (2012, methodology): “More recently it has been proposed (Stephan et al., 2010), on theoretical grounds, to instead score DCMs using the Free Energy (Friston et al., 2007a).”
Understanding DCM: Ten simple rules for the clinician (2013, neutral): “(Litvak et al., 2011; Rowe, 2010; Seghier et al., 2010; Stephan et al., 2010).”
Different Neural Mechanisms within Occipitotemporal Cortex Underlie Repetition Suppression across Same and Different-Size Faces (2012, background): “The ROIs included in a model network should be both related to the experimental design, established on the basis of a general linear model (Stephan et al. 2010) and sufficient to test the hypothesis in question.”
Application of Graph Theory for Identifying Connectivity Patterns in Human Brain Networks: A Systematic Review (2019, background): “DCM is based on a general bilinear state equation that quantiﬁes how variations in neural activity in one node are aﬀected by the activation in another node under predeﬁned stimuli ( Friston et al., 2003; Stephan et al., 2010).”
Dynamic causal modeling of cortical activity from the acute to the chronic stage after stroke (2011, positive): “Furthermore, the relay of neural information by regions which were not explicitly modelled in DCM is implicitly captured in the coupling parameters between two regions (Friston et al., 2003; Stephan et al., 2010).”
A guide to group effective connectivity analysis, part 1: First level analysis with DCM for fMRI (2019, methodology): “We hope this worked example and tutorial-style overview of the theory will complement and expand on previous reviews and tutorials on DCM (Stephan, 2004; Seghier et al., 2010; Stephan et al., 2010; Kahan and Foltynie, 2013).”
Handbook of Functional MRI Data Analysis: Index (2011, neutral): “For this reason, normalization of the mutual information coefficient has been suggested (Studholme et al., 1999):”