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Dynamic causal modelling: A critical review of the biophysical and statistical foundations

Dynamic causal modelling: A critical review of the biophysical and statistical foundations,10.1016/j.neuroimage.2009.11.062,Neuroimage,J. Daunizeau,O.

Dynamic causal modelling: A critical review of the biophysical and statistical foundations   (Citations: 11)
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The goal of dynamic causal modelling (DCM) of neuroimaging data is to study experimentally induced changes in functional integration among brain regions. This requires (i) biophysically plausible and physiologically interpretable models of neuronal network dynamics that can predict distributed brain responses to experimental stimuli and (ii) efficient statistical methods for parameter estimation and model comparison. These two key components of DCM have been the focus of more than thirty methodological articles since the seminal work of Friston and colleagues published in 2003.In this paper, we provide a critical review of the current state-of-the-art of DCM. We inspect the properties of DCM in relation to the most common neuroimaging modalities (fMRI and EEG/MEG) and the specificity of inference on neural systems that can be made from these data. We then discuss both the plausibility of the underlying biophysical models and the robustness of the statistical inversion techniques. Finally, we discuss potential extensions of the current DCM framework, such as stochastic DCMs, plastic DCMs and field DCMs.
Journal: Neuroimage , vol. 58, no. 2, pp. 312-322, 2011
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    • ...In this work we are concerned with sets of variables and their interactions. We assume that the state of the variables is uncertain and that we have access to the (possibly time-dependent) joint probability over the variables. This is to avoid issues related to estimation from data. Our results are generic but it might be instructive to think of the variables as corresponding to the states of a set of neurons or other ‘units’ of the brain. We will further assume that the variables interact directly with each other, that is, that the variables are, or might be, causally connected. Note that experimental data sometimes reflect non-causal variables such as the blood oxygenation level depend (BOLD) signal and local field potentials (LFPs), in which case some additional level of modeling might be needed in order to make inferences about causality (c.f. ...

    Daniel Chicharroet al. When Two Become One: The Limits of Causality Analysis of Brain Dynamic...

    • ... One technique currently being explored is DCM, which directly assesses functional integration across interconnected brain regions and offers a valuable method for testing connectivity hypotheses (see ...

    Rebecca Elliottet al. Affective Cognition and its Disruption in Mood Disorders

    • ... Typically, DCM relies upon Bayesian model comparison to identify the most likely network structure subtending observed fMRI time series within regions of interest. We refer the interested reader to ...

    Jean Daunizeauet al. Optimizing Experimental Design for Comparing Models of Brain Function

    • ...The algorithmic implementation of the variational Bayesian inversion of the response model is formally identical to that of a Dynamic Causal Model (DCM, see e.g. ...

    Jean Daunizeauet al. Observing the Observer (II): Deciding When to Decide

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