Semi-parametric Bayes Regression with Network Valued Covariates

TL;DR

This paper introduces a semi-parametric Bayesian method for modeling brain networks in PTSD, capturing complex relationships while reducing dimensionality and improving prediction accuracy.

Contribution

It develops a novel two-stage Bayesian framework combining latent node representations and Gaussian process regression for high-dimensional network data.

Findings

Outperforms linear models in prediction accuracy

Provides interpretable node-specific insights

Scales efficiently to large brain networks

Abstract

There is an increasing recognition of the role of brain networks as neuroimaging biomarkers in mental health and psychiatric studies. Our focus is posttraumatic stress disorder (PTSD), where the brain network interacts with environmental exposures in complex ways to drive the disease progression. Existing linear models seeking to characterize the relation between the clinical phenotype and the entire edge set in the brain network may be overly simplistic and often involve inflated number of parameters leading to computational burden and inaccurate estimation. In one of the first such efforts, we develop a novel two stage Bayesian framework to find a node-specific lower dimensional representation for the network using a latent scale approach in the first stage, and then use a flexible Gaussian process regression framework for prediction involving the latent scales and other supplementary covariates in the second stage. The proposed approach relaxes linearity assumptions, addresses the curse of dimensionality and is scalable to high dimensional networks while maintaining interpretability at the node level of the network. Extensive simulations and results from our motivating PTSD application show a distinct advantage of the proposed approach over competing linear and non-linear approaches in terms of prediction and coverage.