Predicting Phenotypes from Brain Connection Structure

TL;DR

This paper introduces a Bayesian nonparametric model for predicting cognitive and neuro-psychiatric traits from brain connectome data, leveraging the entire adjacency matrix for improved interpretability and accuracy.

Contribution

It develops the BaCon model using Poisson-Dirichlet processes and spike-and-slab priors to enhance prediction and interpretability of brain network data.

Findings

Outperforms existing methods in predictive accuracy.

Effectively identifies relevant brain connection patterns.

Handles high-dimensional connectome data efficiently.

Abstract

This article focuses on the problem of predicting a response variable based on a network-valued predictor. Our motivation is the development of interpretable and accurate predictive models for cognitive traits and neuro-psychiatric disorders based on an individual's brain connection network (connectome). Current methods reduce the complex, high dimensional brain network into low-dimensional pre-specified features prior to applying standard predictive algorithms. These methods are sensitive to feature choice and inevitably discard important information. Instead, we propose a nonparametric Bayes class of models that utilize the entire adjacency matrix defining brain region connections to adaptively detect predictive algorithms, while maintaining interpretability. The Bayesian Connectomics (BaCon) model class utilizes Poisson-Dirichlet processes to find a lower-dimensional, bidirectional (covariate, subject) pattern in the adjacency matrix. The small n, large p problem is transformed into a "small n, small q" problem, facilitating an effective stochastic search of the predictors. A spike-and-slab prior for the cluster predictors strikes a balance between regression model parsimony and flexibility, resulting in improved inferences and test case predictions. We describe basic properties of the BaCon model and develop efficient algorithms for posterior computation. The resulting methods are found to outperform existing approaches and applied to a creative reasoning data set.