A Bayesian Model for Network Analysis under Edge Misclassification

Thomas M. Menino Convention & Exhibition Center 

Functional brain networks derived from resting-state fMRI are widely used to investigate connectivity alterations in biomedical research. However, such networks are often corrupted by measurement error: noisy correlations can introduce spurious edges (false positives), while low signal-to-noise ratio or infarcts may obscure true connections (false negatives). Standard network models typically ignore this uncertainty, leading to biased estimation of network structure and reduced reproducibility. We propose a latent space covariate model (LSCM) with explicit link misclassification to address these challenges. The model extends classical latent space approaches by incorporating node-level covariates and dyadic similarities while capturing hidden structure through latent positions. Misclassification is parameterized through both false positive and false negative error rates, yielding an observed network likelihood that is an affine transformation of the underlying true edge probabilities. Inference is carried out within a Bayesian framework, with prior distributions carefully chosen to regularize parameters that are otherwise weakly identifiable. Simulation studies demonstrate that explicitly modeling link misclassification improves recovery of latent positions, covariate effects, and global network properties compared to naive approaches. We further apply the proposed model to resting-state fMRI data in Alzheimer's disease, where it identifies robust and biologically meaningful subnetworks associated with cognitive decline.

network analysis

rs-fMRI data

erroneous links

latent space model

Alzheimer's disease