Fast Neural Mass Model Inversion via Amortized Inference

Understanding how brain regions influence one another through effective connectivity (EC) is important for explaining brain function. Dynamic Causal Modeling (DCM) estimates these interactions using biologically grounded generative models, but requires iterative model inversion for each observation. We investigate whether this inversion can be amortized by training neural networks on simulated brain dynamics and then reusing the learned inverse model for new observations. We evaluate two amortized estimators: an EC temporal convolutional network (EC-TCN) for constrained connectivity recovery, and SparseGraphInverse, a shared edge-scoring model designed to scale to larger networks. We test these models using (1) whole-brain Jansen–Rit simulations, (2) an independent benchmark generated using the Statistical Parametric Mapping version 12 (SPM) ERP DCM forward model, and (3) auditory EEG. We further examine how recovery changes as the number of potential connections increases. In particular, we distinguish between identifying the target regions whose incoming coupling changes between conditions and identifying which source–target connections carry these changes. On the 200-case SPM benchmark, EC-TCN reaches an area under the precision-recall curve (AUPRC) of $0.593\pm 0.005$ (five training seeds), 37% above DCM ($0.433$; 95% CI $0.402$–$0.465$), with lower magnitude error ($0.316$ versus $0.484$) and better-calibrated 95% intervals ($0.93$ versus $0.36$ coverage). Inference takes $2.71$ms per case (batch of 200) versus $79.13$s for DCM, roughly $29,000\times$ faster. Amortized inversion can therefore provide fast, EC recovery when the set of possible connections is constrained.