title: Critical comments on dynamic causal modelling.
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.
authors: Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. lohmann@cbs.mpg.de
authors: Gabriele
authors: G
authors: Lohmann
authors: Kerstin
authors: K
authors: Erfurth
authors: Karsten
authors: K
authors: Müller
authors: Robert
authors: R
authors: Turner
mesh_terms: D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology; D015984:Causality; D004569:Electroencephalography / Q000379:methods; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods; D015225:Magnetoencephalography / Q000379:methods; D015233:Models, Statistical; D015203:Reproducibility of Results; D015983:Selection Bias