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Critical comments on dynamic causal modelling

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Lohmann,  Gabriele
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Mueller,  Karsten
Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Turner,  Robert
Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

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Citation

Lohmann, G., Erfurth, K., Mueller, K., & Turner, R. (2012). Critical comments on dynamic causal modelling. NeuroImage, 59(3), 2322-2329. doi:10.1016/j.neuroimage.2011.09.025.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0012-2919-F
Abstract
Dynamic causal modelling (DCM) (Friston et al., 2003) is a technique designed to investigate the influence between brain areas using time series data obtained by EEG/MEG or functional magnetic resonance imaging (fMRI). The basic idea is to fit various models to time series data, and select one of those models using Bayesian model comparison. Here, we present a critical evaluation of DCM in which we show that DCM can be challenged on several grounds. We will discuss three main points relating to combinatorial explosion, the validity of the model selection procedure, and problems with respect to model validation.