Dynamic causal modelling of evoked responses: the role of intrinsic connections.
Dynamic causal modelling is an approach to characterising evoked responses as measured by magneto/electroencephalography (M/EEG). A dynamic causal model (DCM) is a spatiotemporal, generative network model for event-related fields/responses (ERP/ERF) data. Using Bayesian model inversion, one can compute the posterior distributions of the DCM's physiological parameters and its marginal likelihood for model comparison. Model comparison can be used to test mechanistic hypotheses about how electrophysiological data were generated. In this work, we look at the relative importance of changes in intrinsic (within source) and extrinsic (between sources) connections in generating mismatch responses. In short, we introduce the modulation of intrinsic connectivity to the DCM framework. This is useful for testing hypotheses about adaptation of neuronal responses to local influences, in relation to influences that are mediated by long-range extrinsic connections (forward, backward, and lateral) from other sources. We illustrate this extension using synthetic data and empirical data from an oddball ERP experiment.
Kiebel
Stefan J
SJ
Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3AR, UK. skiebel@fil.ion.ucl.ac.uk
Garrido
Marta I
MI
Friston
Karl J
KJ
D000328:Adult; D001303:Auditory Cortex / Q000502:physiology*; D015984:Causality; D003198:Computer Simulation; D005072:Evoked Potentials, Auditory / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D010898:Pitch Perception / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*