Dynamic causal modelling

By Karl J. Friston, Lee M. Harrison, W. D. Penny. 2003

4,588 citations recorded by the source corpus.

In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.

Subfields: Cognitive Neuroscience

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Citations in the corpus

Learning stable and predictive structures in kinetic systems (2018, background): “This assumes that [1] holds (approximately) when including the unseen experiments, too.”
Estimation of functional connectivity in fMRI data using stability selection-based sparse partial correlation with elastic net penalty (2011, positive): “The signal-to-noise ratio (SNR) for the output BOLD time series was set at 1 (the default value in the DCM simulation script).”
Effective connectivity: Influence, causality and biophysical modeling (2011, neutral): “Thisis whyitwascalled dynamic causal modeling to distinguish it from structural causal modeling (Friston et al., 2003).”
Mapping directed influence over the brain using Granger causality and fMRI (2005, methodology): “The same holds for dynamic causal models (Friston et al., 2003) that use deterministic state- space models to represent neuronal dynamics and interactions augmented with forward models of the regional hemodynamic response.”
Wiener-Granger Causality: A well established methodology (2011, neutral): “In the limit, as Friston (this issue) points out, bothWGC and DCM are based on TSI and can be subsumed within a single theoretical framework.”
Automated adaptive inference of phenomenological dynamical models (2015, neutral): “10).”
Variational Bayesian inversion of the equivalent current dipole model in EEG/MEG (2008, methodology): “The marginal posteriors can be used to derive Bayesian confidence intervals for all parameters (Friston et al., 2003).”
Hierarchical Mixture of Classification Experts Uncovers Interactions between Brain Regions (2009, background): “Models for effective connectivity, such as Granger causality mapping [14] and dynamic causal modeling [13], model directed connections between brain regions.”
Bayesian model selection for group studies - Revisited (2014, positive): “In brief, we used a simplified version of the exemplar analysis in Friston et al. (2003) that tries to identify the impact of experimental factors (photic stimulation and motion) in a three-region network (V1, V5 and SPC(3)).”
Comparing Families of Dynamic Causal Models (2010, methodology): “Dynamic Causal Models Dynamic Causal Modelling is a framework for fitting differential equation models of neuronal activity to brain imaging data using Bayesian inference.”
Ten Simple Rules for Dynamic Causal Modelling (2009, neutral): “We are very grateful to our fellow DCM developers in the FIL methods group, particularly Lee Harrison for his comments on the manuscript and the shaping discussions on DCM we have had with him over many years.”