Estimating Brain Effective Connectivity in fMRI data by Nonstationary Dynamic Bayesian Networks Estimating brain effective connectivity (EC) from neuroimaging data has recently received wide interest and become a new topic in the neuroinformatics field. Currently, dynamic Bayesian networks (DBN) have been successfully applied to estimating EC from functional magnetic resonance imaging (fMRI) timeseries data as they can capture the temporal characteristics of connectivity among brain regions. However, DBN methods assume that activations of brain regions are stationary and follow a Markovian condition, which are strong assumptions that may not be valid in many cases. In this paper, we introduce a novel method to estimate brain effective connectivity networks from fMRI data using nonstationary dynamic Bayesian networks, named as ECnsDBN. ECnsDBN can not only capture the nonstationary temporal information from fMRI timeseries data but also estimate how interactions among brain regions change dynamically over the fMRI experiments. Systematic experiments on simulated data show that ECnsDBN has better direction identification ability compared with other stateoftheart algorithms, and can accurately capture the temporal characteristics of connectivity. Experiments on realworld data sets are also provided to support our analysis.

Bayesian fusion and multimodal DCM for EEG and fMRI Abstract   This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the use of Bayesian fusion to provide informative (empirical) neuronal priors xe2x80x93 derived from dynamic causal modelling (DCM) of EEG data xe2x80x93 for subsequent DCM of fMRI data. To illustrate this procedure, we generated synthetic EEG and fMRI timeseries for a mismatch negativity (or auditory oddball) paradigm, using biologically plausible model parameters (i.e., posterior expectations from a DCM of empirical, open access, EEG data). Using model inversion, we found that Bayesian fusion provided a substantial improvement in marginal likelihood or model evidence, indicating a more efficient estimation of model parameters, in relation to inverting fMRI data alone. We quantified the benefits of multimodal fusion with the information gain pertaining to neuronal and haemodynamic parameters xe2x80x93 as measured by the KullbackLeibler divergence between their prior and posterior densities. Remarkably, this analysis suggested that EEG data can improve estimates of haemodynamic parameters; thereby furnishing proofofprinciple that Bayesian fusion of EEG and fMRI is necessary to resolve conditional dependencies between neuronal and haemodynamic estimators. These results suggest that Bayesian fusion may offer a useful approach that exploits the complementary temporal (EEG) and spatial (fMRI) precision of different data modalities. We envisage the procedure could be applied to any multimodal dataset that can be explained by a DCM with a common neuronal parameterisation.