arXiv subjects

Dominic Boutet

Publications and source records attributed to Dominic Boutet.

2 recordsLinked to original sources

arXiv2025

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.

q-bio.NC↗

arXiv2023

A Metaheuristic for Amortized Search in High-Dimensional Parameter Spaces

Parameter inference for dynamical models of (bio)physical systems remains a challenging problem. Intractable gradients, high-dimensional spaces, and non-linear model functions are typically problematic without large computational budgets. A recent body of work in that area has focused on Bayesian inference methods, which consider parameters under their statistical distributions and therefore, do not derive point estimates of optimal parameter values. Here we propose a new metaheuristic that drives dimensionality reductions from feature-informed transformations (DR-FFIT) to address these bottlenecks. DR-FFIT implements an efficient sampling strategy that facilitates a gradient-free parameter search in high-dimensional spaces. We use artificial neural networks to obtain differentiable proxies for the model's features of interest. The resulting gradients enable the estimation of a local active subspace of the model within a defined sampling region. This approach enables efficient dimensionality reductions of highly non-linear search spaces at a low computational cost. Our test data show that DR-FFIT boosts the performances of random-search and simulated-annealing against well-established metaheuristics, and improves the goodness-of-fit of the model, all within contained run-time costs.

q-bio.QM↗