Bayesian method quantifies uncertainty in fMRI connectivity scans
Bayesian Uncertainty Quantification for fMRI Functional Connectivity via Simulation-Based Inference
Machine Learning
Summary
Measuring brain connectivity with fMRI scans is tricky because of noise and differences between people. The authors developed a new statistical method that uses Bayesian models to estimate how certain we can be about these brain connections. They tested this method on data from 28 healthy people scanned at high and standard magnetic strengths. Their approach helps figure out the minimum scan time and data needed for reliable results and shows that higher-strength scanners can reduce scan times significantly. This provides useful, scanner-specific advice for better brain scan designs and more trustworthy medical results.
What this means in practice
- •For clinical imaging teams: Optimize fMRI scanning protocols to reduce scan time while ensuring reliable connectivity estimates specific to scanner strength.
- •For medical device engineers: Design next-generation fMRI scanners and software with calibrated protocols based on quantified noise and variability profiles.
Authors
Simon Carter, Zeming Kuang, Lilianne R. Mujica-Parodi, Helmut H. Strey
Abstract
Optimizing fMRI scan duration and spatial resolution is critical for experimental design, yet traditional correlation-based approaches cannot quantify uncertainty or disentangle scanner measurement noise from true neural variability across subjects. Without principled uncertainty bounds, researchers cannot know whether a protocol is long enough to reliably estimate connectivity, or whether between-subject differences reflect biological variation or noise. We present a Bayesian framework modeling BOLD dynamics as coupled Ornstein-Uhlenbeck processes, using Sequential Neural Posterior Estimation to obtain connectivity posteriors while accounting for frequency-independent measurement noise across the BOLD spectrum. Applied to N = 28 healthy controls (55 scans) at 7T using a functional network atlas (65 DMN regions), the framework quantifies uncertainty across its sources: scanner noise, subject variability, and acquisition length. Spatial analysis identifies a mean of 46 voxels per ROI, roughly half of typical region sizes, as sufficient to achieve 90% of asymptotic precision. At the single-subject level, 7T reaches its within-session precision plateau in approximately 7 minutes versus 10 minutes for 3T, a 40% reduction in required scan time, providing the first direct, model-based quantification of the scan-time advantage conferred by higher field strength. At the population level, 3T requires roughly 37 times more per-subject scan time than 7T for the pooled curves to converge, confirming a consistent advantage of higher field strength at every timescale. Together these findings provide concrete, scanner-specific guidance for protocol optimization, with direct implications for reducing acquisition costs and improving the reliability of connectivity-based clinical biomarkers. We provide code enabling researchers to derive these bounds from their own data.