Bayesian thresholded modeling for integrating brain node and network predictors

TL;DR

This paper introduces a Bayesian regression model that integrates node-level and network-level brain imaging data to better understand neurobiological mechanisms and predict phenotypic outcomes, with applications to mental ability assessment.

Contribution

It proposes a novel joint thresholded prior within a Bayesian framework to effectively combine hierarchical brain imaging features and improve prediction and feature selection.

Findings

Outperforms existing methods in prediction accuracy

Identifies meaningful neuromarkers linked to mental abilities

Demonstrates the model's effectiveness through simulations and real data

Abstract

Progress in neuroscience has provided unprecedented opportunities to advance our understanding of brain alterations and their correspondence to phenotypic profiles. With data collected from various imaging techniques, studies have integrated different types of information ranging from brain structure, function, or metabolism. More recently, an emerging way to categorize imaging traits is through a metric hierarchy, including localized node-level measurements and interactive network-level metrics. However, limited research has been conducted to integrate these different hierarchies and achieve a better understanding of the neurobiological mechanisms and communications. In this work, we address this literature gap by proposing a Bayesian regression model under both vector-variate and matrix-variate predictors. To characterize the interplay between different predicting components, we propose a set of biologically plausible prior models centered on an innovative joint thresholded prior. This captures the coupling and grouping effect of signal patterns, as well as their spatial contiguity across brain anatomy. By developing a posterior inference, we can identify and quantify the uncertainty of signaling node- and network-level neuromarkers, as well as their predictive mechanism for phenotypic outcomes. Through extensive simulations, we demonstrate that our proposed method outperforms the alternative approaches substantially in both out-of-sample prediction and feature selection. By implementing the model to study children's general mental abilities, we establish a powerful predictive mechanism based on the identified task contrast traits and resting-state sub-networks.