Limitations and Scope ##################### VBI is designed for amortized simulation-based inference (SBI) on whole-brain network models. It is most useful when you need full posterior distributions over a modest number of biophysically meaningful control parameters and can afford an upfront simulation and training budget in exchange for fast, reusable inference on new observations. When to use VBI =============== VBI is a good fit when: - You work with whole-brain network models (e.g. Jansen-Rit, Wilson-Cowan, Wong-Wang, Montbrió/MPR, Stuart-Landau, Epileptor/VEP) and want to invert them against empirical neuroimaging data (fMRI, EEG, MEG, SEEG). - You want a full posterior distribution over model parameters, including uncertainty, rather than a single point estimate. - You plan to perform inference repeatedly on many subjects or observations, so the one-time cost of amortized training pays off. - You can run a sufficiently large simulation budget on CPU or GPU. Limitations =========== Users should keep the following limitations in mind: **Parameter identifiability.** Some parameters are only jointly (not individually) identifiable. For example, in the Wong-Wang model the global coupling ``G`` and synaptic coupling ``J`` are structurally non-identifiable and yield a curved, degenerate posterior. This is a property of the model and the data rather than of VBI itself, but it limits how uniquely individual parameters can be recovered. **Choice of data features.** Inference quality depends strongly on selecting low-dimensional data features that are informative about the target parameters. Functional connectivity alone (FC/FCD) is often insufficient for estimating regional parameters; spatio-temporal features are usually required. **Noise sensitivity.** High observational or dynamical noise can corrupt feature estimation and degrade the resulting posterior. **Simulation budget.** There is no principled rule for the number of simulations required to train a reliable estimator. Adequacy must be checked empirically, for example using posterior z-scores and shrinkage or simulation-based calibration (SBC). **Upfront computational cost.** Single-round amortized training requires a substantial one-time compute investment (roughly tens of minutes to several hours, depending on the model and simulation budget) before inference becomes inexpensive. **Preprocessing.** Some statistical information (e.g. signal mean and variance) can be lost during feature preprocessing, reducing the informativeness of the extracted features. Further reading =============== For a detailed validation study and an in-depth discussion of these points, see `Ziaeemehr et al., eLife 2025 `_.