Bayesian network-response regression.
BACKGROUND
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.


RESULTS
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.


METHODS
Source code implemented in R and data are available at https://github.com/wangronglu/BNRR.


BACKGROUND
rl.wang@duke.edu.


BACKGROUND
Supplementary data are available at Bioinformatics online.
Department of Statistical Science, Duke University, Durham, NC, USA.
Lu
L
Wang
Department of Statistical Sciences, University of Padova, Padova, Italy.
Daniele
D
Durante
Department of Psychology, University of New Mexico, Albuquerque, NM, USA.
Rex E
RE
Jung
Department of Statistical Science, Duke University, Durham, NC, USA.
David B
DB
Dunson
D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D009415:Nerve Net / Q000033:anatomy & histology* / Q000502:physiology; D012984:Software