Summary of the results from a simulation study in which we examined the robustness of our method when randomly sampling from a heterogeneous population of subjects. Specifically, we dealt with a population in which 70% of subjects showed brain responses as generated by model

*m*_{1} shown in , whereas brain activity in the remaining 30% of the population was generated by model

*m*_{2}. We randomly sampled 20 subjects from this population and generated synthetic fMRI data by integrating the state equations of the associated models with fixed parameters and inputs and adding Gaussian observation noise to achieve an SNR of two. Both

*m*_{1} and

*m*_{2} were then fitted to all 20 synthetic data sets. This sampling and data generation procedure was repeated 20 times, resulting in a total of 400 generated data sets and 800 fitted models. For each of the 20 sets of 20 subjects, we computed the different indices provided by random effects BMS (i.e.,

*α*,

*r*,

) and fixed effects BMS (log GBF). This figure shows the mean of these indices together with their 95% confidence intervals (CI).