Reliable Neuroimaging Model Interpretation via Closed-Form ROI Influence Posteriors

Neuroimaging-based machine learning models are increasingly used to study brain disorders and aging, yet their interpretations are often reported as deterministic importance maps over brain regions. In region-of-interest (ROI) analyses of structural MRI and fMRI, such point explanations can be unreliable: limited sample sizes, heterogeneous cohorts, and training uncertainty can change which brain regions appear influential, while standard attribution methods rarely quantify whether a regional effect is stable. We propose Closed-Form Influence Posterior (CFIP), a probabilistic framework for reliable neuroimaging model interpretation. CFIP defines regional influence as the model-output change induced by intervening on an ROI, uses a Laplace approximation to model parameter uncertainty, and propagates this uncertainty through a first-order linearization of the ROI influence functional. Under this Laplace-linearized construction, the resulting closed-form posterior over ROI influences provides an analytic distributional view of regional effects, supporting uncertainty-aware interpretation, reliability assessment, and a decomposition of attribution instability into model uncertainty and data heterogeneity. We validate CFIP across multi-cohort neuroimaging benchmarks spanning structural MRI-derived regional features and fMRI connectivity representations, including Alzheimer’s disease and aging settings. CFIP delivers faithful regional interpretations, uncertainty estimates that track observed attribution variability, and efficient closed-form inference without costly sampling. These results support CFIP as a practical reliability layer for anatomically and functionally meaningful neuroimaging model interpretation.