ntroduction:Thisarticleispart of theFocus Theme of Methods of Information in Medi- cine on “ Biosignal Interpretation: Advanced Methods for Neural Signals and Images” . Background: Dynamic Causal Modelling (DCM) is a generic formalism to study effec- tive brain connectivity based on neuroimag- ing data, particularly functional Magnetic Resonance Imaging (fMRI). Recently, there have been attempts at modifying this model to allow for stochastic disturbances in the statesof themodel. O b j e c t i v e s : Th i s p a p e r p r o p o s e s t h e Multiple-Model Kalman Filtering (MMKF) technique as a stochastic identification model discriminating among different hypo- thetical connectivity structures in the DCM framework; moreover, the performance com- pared to a similar deterministic identification model is assessed. Met hods: The integration of the stochastic DCM equations is first presented, and a MMKF algorithm is then developed to per- form model selection based on these equations. Monte Carlo simulations are per- formed in order to investigate the ability of MMKF to distinguish between different con- nectivity structures and to estimate hidden states under both deterministic and stochas- tic DCM. Results: The simulations show that the pro- posed MMKF algorithm was able to success- fully select the correct connectivity model structure from a set of pre-specified plau- sible alternatives. Moreover, the stochastic approach by MMKFwas more effective com- pared to its deterministic counterpart, both in the selection of the correct connectivity structure and in the estimation of the hidden states. Conclusions: These results demonstrate the applicability of a MMKF approach to the study of effective brain connectivity using DCM, particularly when a stochastic formu- lation is desirable.