This technical note considers a simple but important methodological issue in estimating effective connectivity; namely, how do we integrate measurements frommultiple subjects to infer functional brain architectures that are conserved over subjects. We offer a solution to this problem that rests on a generalization of random effects analyses to Bayesian inference about nonlinear models of electrophysiological time-series data. Specifically, we present an empirical Bayesian scheme for group or hierarchical models, in the setting of dynamic causal modeling (DCM). Recent developments in approximate Bayesian inference for hierarchical models enable the efficient estimation of group effects in DCM studies of multiple trials, sessions, or subjects...
Contains fulltext : 173085.pdf (publisher's version ) (Open Access)In many fields ...
This article describes the use of Bayes factors for comparing dynamic causal models (DCMs). DCMs are...
This article describes the use of Bayes factors for comparing Dynamic Causal Models (DCMs). DCMs are...
This technical note describes some Bayesian procedures for the analysis of group studies that use no...
AbstractThis technical note describes some Bayesian procedures for the analysis of group studies tha...
This paper provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirica...
This technical note addresses some key reproducibility issues in the dynamic causal modelling of gr...
Background Generative models of neuroimaging data, such as dynamic causal models (DCMs), are commonl...
This technical note addresses some key reproducibility issues in the dynamic causal modelling of gro...
Bayesian model selection (BMS) is a powerful method for determining the most likely among a set of c...
Dynamic Causal Modelling (DCM) is the predominant method for inferring effective connectivity from n...
International audienceDynamic causal modeling (DCM) is a methodological approach to study effective ...
Dynamic Causal Modelling (DCM) is an approach first introduced for the analysis of functional magnet...
This paper reviews hierarchical observation models, used in functional neuroimaging, in a Bayesian l...
Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previ...
Contains fulltext : 173085.pdf (publisher's version ) (Open Access)In many fields ...
This article describes the use of Bayes factors for comparing dynamic causal models (DCMs). DCMs are...
This article describes the use of Bayes factors for comparing Dynamic Causal Models (DCMs). DCMs are...
This technical note describes some Bayesian procedures for the analysis of group studies that use no...
AbstractThis technical note describes some Bayesian procedures for the analysis of group studies tha...
This paper provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirica...
This technical note addresses some key reproducibility issues in the dynamic causal modelling of gr...
Background Generative models of neuroimaging data, such as dynamic causal models (DCMs), are commonl...
This technical note addresses some key reproducibility issues in the dynamic causal modelling of gro...
Bayesian model selection (BMS) is a powerful method for determining the most likely among a set of c...
Dynamic Causal Modelling (DCM) is the predominant method for inferring effective connectivity from n...
International audienceDynamic causal modeling (DCM) is a methodological approach to study effective ...
Dynamic Causal Modelling (DCM) is an approach first introduced for the analysis of functional magnet...
This paper reviews hierarchical observation models, used in functional neuroimaging, in a Bayesian l...
Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previ...
Contains fulltext : 173085.pdf (publisher's version ) (Open Access)In many fields ...
This article describes the use of Bayes factors for comparing dynamic causal models (DCMs). DCMs are...
This article describes the use of Bayes factors for comparing Dynamic Causal Models (DCMs). DCMs are...