A Hypothesis-First Framework for Mechanistic Modeling in Neuroimaging

Turning rich neuroimaging data into mechanistic insight remains challenging. Statistical models capture associations but remain largely agnostic to underlying mechanisms. Biophysical models embody candidate mechanisms but remain difficult to deploy without specialized expertise. Here, we present a hypothesis-first framework recasting model specifications as testable mechanistic hypotheses and streamlines the procedure for rejecting inappropriate hypotheses before moving to typical analyses. The key innovation is an expectation of model behavior under feature generalization constraints: we compute the model's expected $Y$ output across the parameter space based on the likelihood for a broader/distinct feature $Z$. Mirror statistical models are derived from these expected outputs and compared to the empirical ones with standard statistics. In synthetic experiments, our framework rejected mis-specified hypotheses and penalized unnecessary degrees of freedom while retaining valid hypotheses. These results demonstrate a practical hypothesis-driven approach for using mechanistic models in neuroimaging without requiring advanced training, complementing traditional analyses.