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

Authors :
Klaas E. Stephan
Jean Daunizeau
Olivier David
Wellcome Trust Centre for Neuroimaging
University College of London [London] (UCL)
Laboratory for Social and Neural Systems Research (SNS Lab)
Universität Zürich [Zürich] = University of Zurich (UZH)
INSERM U836, équipe 11, Fonctions cérébrales et neuromodulation
Grenoble Institut des Neurosciences (GIN)
Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Service de neuroradiologie [Grenoble]
CHU Grenoble-CHU Grenoble
This work was supported by the University Research Priority Program 'Foundations of Human Social Behaviour' at the University of Zurich (KES), the NEUROCHOICE project of the Swiss Systems Biology initiative SystemsX.ch (JD, KES) and the INSERM (OD)
David, Olivier
University of Zurich
Daunizeau, J
Source :
NeuroImage, NeuroImage, Elsevier, 2011, 58 (2), pp.312-22. ⟨10.1016/j.neuroimage.2009.11.062⟩
Publication Year :
2011
Publisher :
HAL CCSD, 2011.

Abstract

International audience; 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.

Details

Language :
English
ISSN :
10538119 and 10959572
Database :
OpenAIRE
Journal :
NeuroImage, NeuroImage, Elsevier, 2011, 58 (2), pp.312-22. ⟨10.1016/j.neuroimage.2009.11.062⟩
Accession number :
edsair.doi.dedup.....7355524a6b9aefdef6baf40fc6ca2b92
Full Text :
https://doi.org/10.1016/j.neuroimage.2009.11.062⟩