Learned Hemodynamic Coupling Inference in Resting-State Functional MRI

2026-01-02

AI Summary

This study addresses the bias in functional connectivity estimation from resting-state fMRI that arises from ignoring hemodynamic variability across brain regions and individuals. To mitigate this, the authors propose a novel method that directly infers hemodynamic coupling parameters on the cortical surface by marginalizing latent neural activity and optimizing the marginal likelihood, thereby avoiding the instability of joint estimation. The approach innovatively integrates deep neural networks with conditional normalizing flows to efficiently approximate the complex marginal likelihood, while incorporating a cortical surface–aware sparsity prior to enhance spatial consistency. Experiments on both synthetic and real data demonstrate that the proposed method significantly outperforms existing approaches in estimating hemodynamic parameters and improves downstream functional connectivity analyses.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Learning with Manifolds
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

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.

Problem

• hemodynamic variability
• resting-state fMRI
• blind inverse problem
• neural activity
• hemodynamic coupling

Innovation

• hemodynamic coupling
• resting-state fMRI
• marginal likelihood
• conditional normalizing flows
• cortical surface priors