Project Grant R01EB037715

The National Institute of Biomedical Imaging and Bioengineering awarded Rensselaer Polytechnic Institute $366,006 on September 1, 2026, under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286) to develop a simulation-based inference framework that transforms functional near-infrared spectroscopy (fNIRS) data analysis through artificial intelligence.

The project delivers a biophysically detailed simulator integrating realistic models of hemodynamic response, systemic physiology, and data corruption using mesh-based Monte Carlo methods. The framework calibrates this simulator to individual trials and subjects using amortized Bayesian inference to enable fast, accurate estimation of latent neural parameters with uncertainty quantification across experimental and hardware setups. The work generates large-scale synthetic datasets that preserve subject- and task-specific dynamics to support data augmentation for deep learning and interpretable downstream analysis. Semi-supervised learning tools merge synthetic and real data, diagnose simulation gaps, and improve reliability under distribution shifts. Hierarchical Bayesian models extract trial- and subject-level parameters for noise-resilient subject-specific brain understanding. Computational benchmarks and model applications address surgical training scenarios using raw and denoised time series and biophysical parameters for interpretable, uncertainty-aware skill classification and performance prediction.

The award addresses persistent methodological challenges in fNIRS—including sensitivity to physiological noise, motion artifacts, individual variability, and lack of standardized, scalable analysis pipelines—that currently limit broader adoption in research and clinical settings.

Performance occurs in Troy, New York, with a completion date of June 30, 2030.