A Hierarchical Validity-Audit Framework for Neural Mass Models in Simulation-Based Inference: From Observational Coverage to Mechanistic Interpretation

Abstract

Neural mass models describe population activity with low-dimensional dynamics, but simulation-based posterior recovery does not ensure that a model fits real observations or that inferred parameters support physiological interpretation. We introduce NMM-SBI Audit, a hierarchical framework that evaluates whether a model configuration covers observed data, assesses recoverability across multilevel parameter coordinates and summary representations, and examines joint parameter compensation and cross-track consistency. In experiments with known ground truth, the framework controlled empirical error rates and detected prespecified failures. Applied to real data, a single-source Epileptor model failed to cover core seizure statistics of SOZ-local iEEG, rendering simulation-recoverable targets unsuitable for patient-specific mechanistic interpretation. In contrast, a CMC-inspired auditory network model showed no systematic representation-level mismatch and supported conditional recovery of selected superficial-layer and inhibitory gains, while revealing parameter compensation, summary information loss, and instability of the active structure. These results show that observation fit, target recoverability, and joint interpretability provide distinct levels of evidence. NMM-SBI Audit offers a scalable approach to limiting unsupported mechanistic claims in simulation-based inference of neural dynamics.