CiteSeerX — Search Results — dynamic causal model

Results 1 - 10 of 169,733

Comparing Dynamic Causal Models

by W. D. Penny, K. E. Stephan, A. Mechelli, K. J. Friston - NEUROIMAGE , 2004

"... This article describes the use of Bayes factors for comparing Dynamic Causal Models (DCMs). DCMs are used to make inferences about effective connectivity from functional Magnetic Resonance Imaging (fMRI) data. These inferences, however, are contingent upon assumptions about model structure, that is, ..."

Abstract - Cited by 114 (37 self) - Add to MetaCart

This article describes the use of Bayes factors for comparing Dynamic Causal Models (DCMs). DCMs are used to make inferences about effective connectivity from functional Magnetic Resonance Imaging (fMRI) data. These inferences, however, are contingent upon assumptions about model structure, that is

Abstract Dynamic causal modelling

by K. J. Friston, L. Harrison, W. Penny , 2003

"... In this paper we present an approach to the identification of nonlinear input–state–output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influen ..."

Abstract - Add to MetaCart

In this paper we present an approach to the identification of nonlinear input–state–output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate

of Dynamic Causal Models

by Michael Lechner, Michael Lechner , 2004

"... positions. ..."

Abstract - Add to MetaCart

positions.

Comparing families of dynamic causal models

by Will D. Penny, Klaas E. Stephan, Jean Daunizeau, Maria J. Rosa, Karl J. Friston, Thomas M, Alex P. Leff - PLoS Comput. Biol , 2010

"... Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and then makes inferences based on the parameters of ..."

Abstract - Cited by 27 (7 self) - Add to MetaCart

connection? We apply Bayesian model averaging within families to provide inferences about parameters that are independent of further assumptions about model structure. We illustrate the methods using Dynamic Causal Models of brain imaging data.

Dynamic causal modelling of induced responses

by C. C. Chen, S. J. Kiebel, K. J. Friston - NeuroImage , 2008

"... This paper describes a dynamic causal model (DCM) for induced or spectral responses as measured with the electroencephalogram (EEG) or the magnetoencephalogram (MEG). We model the time-varying power, over a range of frequencies, as the response of a distributed system of coupled electromagnetic sour ..."

Abstract - Cited by 18 (4 self) - Add to MetaCart

This paper describes a dynamic causal model (DCM) for induced or spectral responses as measured with the electroencephalogram (EEG) or the magnetoencephalogram (MEG). We model the time-varying power, over a range of frequencies, as the response of a distributed system of coupled electromagnetic

Dynamic causal modeling for EEG and MEG

by Stefan J Kiebel , Marta I Garrido , Rosalyn Moran , Chun-Chuan Chen , Karl J Friston , 2009

"... Abstract: We present a review of dynamic causal modeling (DCM) for magneto-and electroencephalography (M/EEG) data. DCM is based on a spatiotemporal model, where the temporal component is formulated in terms of neurobiologically plausible dynamics. Following an intuitive description of the model, w ..."

Abstract - Cited by 8 (3 self) - Add to MetaCart

Abstract: We present a review of dynamic causal modeling (DCM) for magneto-and electroencephalography (M/EEG) data. DCM is based on a spatiotemporal model, where the temporal component is formulated in terms of neurobiologically plausible dynamics. Following an intuitive description of the model