  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.
