Learned Hemodynamic Coupling Inference in Resting-State Functional MRI

TL;DR

This paper introduces a novel deep learning-based method to infer spatially varying hemodynamic responses from resting-state fMRI data, improving the accuracy of connectivity estimates and providing potential biomarkers.

Contribution

It presents a scalable, surface-based inference approach that marginalizes neural activity, employs deep neural networks with normalizing flows, and quantifies uncertainty in hemodynamic estimates.

Findings

Improved hemodynamic estimation over existing methods

Enhanced accuracy in brain connectivity analysis

Validated on synthetic and real datasets

Abstract

Functional magnetic resonance imaging (fMRI) provides an indirect measurement of neuronal activity via hemodynamic responses that vary across brain regions and individuals. Ignoring this hemodynamic variability can bias downstream connectivity estimates. Furthermore, the hemodynamic parameters themselves may serve as important imaging biomarkers. Estimating spatially varying hemodynamics from resting-state fMRI (rsfMRI) is therefore an important but challenging blind inverse problem, since both the latent neural activity and the hemodynamic coupling are unknown. In this work, we propose a methodology for inferring hemodynamic coupling on the cortical surface from rsfMRI. Our approach avoids the highly unstable joint recovery of neural activity and hemodynamics by marginalizing out the latent neural signal and basing inference on the resulting marginal likelihood. To enable scalable, high-resolution estimation, we employ a deep neural network combined with conditional normalizing flows to accurately approximate this intractable marginal likelihood, while enforcing spatial coherence through priors defined on the cortical surface that admit sparse representations. Uncertainty in the hemodynamic estimates is quantified via a double-bootstrap procedure. The proposed approach is extensively validated using synthetic data and real fMRI datasets, demonstrating clear improvements over current methods for hemodynamic estimation and downstream connectivity analysis.