This technical note addresses some key reproducibility issues in the dynamic causal modelling of group studies of event related potentials. Specifically, we address the reproducibility of Bayesian model comparison (and inferences about model parameters) from three important perspectives namely: (i) reproducibility with independent data (obtained by averaging over odd and even trials); (ii) reproducibility over formally distinct models (namely, classic ERP and canonical microcircuit or CMC models); and (iii) reproducibility over inversion schemes (inversion of the grand average and estimation of group effects using empirical Bayes). Our hope was to illustrate the degree of reproducibility one can expect from DCM when analysing different data...
This paper provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirica...
The vast majority of published results in the literature is statistically significant, which raises ...
Currently, most studies that employ dynamic causal modeling (DCM) use random-effects (RFX) analysis ...
This technical note addresses some key reproducibility issues in the dynamic causal modelling of gro...
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 technical note considers a simple but important methodological issue in estimating effective co...
Bayesian model selection (BMS) is a powerful method for determining the most likely among a set of c...
We revisit the results of the recent Reproducibility Project: Psychology by the Open Science Collabo...
Boik (1997) presented an empirical Bayes (EB) approach to the analysis of repeated measurements. The...
Two papers were selected this year for their implemen-tation of novel analytical approaches on archi...
Measurement invariance (MI) is conducted to ensure that differences found in the results of group co...
The reproducibility of scientific discoveries is a hallmark of scientific research. Although its cen...
The vast majority of published results in the literature is statistically significant, which raises ...
Dynamic causal modeling (DCM) is a Bayesian framework for inferring effective connectivity among bra...
This paper provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirica...
The vast majority of published results in the literature is statistically significant, which raises ...
Currently, most studies that employ dynamic causal modeling (DCM) use random-effects (RFX) analysis ...
This technical note addresses some key reproducibility issues in the dynamic causal modelling of gro...
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 technical note considers a simple but important methodological issue in estimating effective co...
Bayesian model selection (BMS) is a powerful method for determining the most likely among a set of c...
We revisit the results of the recent Reproducibility Project: Psychology by the Open Science Collabo...
Boik (1997) presented an empirical Bayes (EB) approach to the analysis of repeated measurements. The...
Two papers were selected this year for their implemen-tation of novel analytical approaches on archi...
Measurement invariance (MI) is conducted to ensure that differences found in the results of group co...
The reproducibility of scientific discoveries is a hallmark of scientific research. Although its cen...
The vast majority of published results in the literature is statistically significant, which raises ...
Dynamic causal modeling (DCM) is a Bayesian framework for inferring effective connectivity among bra...
This paper provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirica...
The vast majority of published results in the literature is statistically significant, which raises ...
Currently, most studies that employ dynamic causal modeling (DCM) use random-effects (RFX) analysis ...