BASIL-DCM: Biophysical Amortized Scalable Inference for Latent Dynamic Causal Modeling

Estimating directed, weighted, and signed interactions among brain regions ($\textit{effective connectivity}$) from fMRI requires disentangling neural dynamics from delayed and nonlinear hemodynamic transformations. Dynamic Causal Modeling (DCM) provides a principled solution by inverting a biophysical generative model, but the standard Variational Laplace inversion method is computationally prohibitive for large parcellations and cohort-scale datasets. We introduce $\textbf{BASIL-DCM}$, a physics-informed amortized inference model that estimates subject-specific effective connectivity and biophysical DCM parameters, including ROI-wise hemodynamic transit time, spectral properties of endogenous neural fluctuations, and observation noise, $\textit{in a single forward pass}$. BASIL-DCM combines a linear-time state-space temporal encoder with an ROI-wise Transformer to capture long-range temporal dependencies and inter-regional interactions. The model is trained on data informed by Human Connectome Project resting-state fMRI, with effective connectivity initialized from regression DCM and complementary biophysical parameters sampled from physiologically plausible ranges. Learning is further constrained by a differentiable spectral-consistency objective derived from the DCM forward model. This approach enables fast, uncertainty-aware whole-brain network inference while preserving mechanistic interpretability and ensuring consistency with biophysical dynamics.