Bayesian Network--Response Regression

TL;DR

This paper introduces a Bayesian semiparametric model combining low-rank factorizations and Gaussian process priors to analyze how human brain networks vary with continuous traits like intelligence, offering a flexible and efficient inference framework.

Contribution

It develops a novel Bayesian model that captures changes in brain network structures across traits, incorporating subject-specific effects and providing computational tools for inference.

Findings

Insights into the association between intelligence and brain connectivity

Good predictive performance demonstrated

Framework facilitates uncertainty quantification

Abstract

There is increasing interest in learning how human brain networks vary as a function of a continuous trait, but flexible and efficient procedures to accomplish this goal are limited. We develop a Bayesian semiparametric model, which combines low-rank factorizations and flexible Gaussian process priors to learn changes in the conditional expectation of a network-valued random variable across the values of a continuous predictor, while including subject-specific random effects. The formulation leads to a general framework for inference on changes in brain network structures across human traits, facilitating borrowing of information and coherently characterizing uncertainty. We provide an efficient Gibbs sampler for posterior computation along with simple procedures for inference, prediction and goodness-of-fit assessments. The model is applied to learn how human brain networks vary across individuals with different intelligence scores. Results provide interesting insights on the association between intelligence and brain connectivity, while demonstrating good predictive performance.