Bayesian Uncertainty Quantification for fMRI Functional Connectivity via Simulation-Based Inference
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 a...
| Authors | Simon Carter, Zeming Kuang, Lilianne R. Mujica-Parodi et al. |
|---|---|
| Primary category | stat.ML |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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