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.

翻译：神经科学的进展为深入理解大脑改变及其与表型特征的对应关系提供了前所未有的机遇。借助各类成像技术采集的数据，研究已整合了从脑结构、功能到代谢等多种类型的信息。最近，一种新兴的成像特征分类方式是通过度量层级体系实现，包括局部节点层面的测量指标和交互网络层面的度量标准。然而，目前整合这些不同层级并深入理解神经生物学机制与通信的研究仍较为有限。本研究通过提出一种同时包含向量型与矩阵型预测因子的贝叶斯回归模型，以填补这一文献空白。为刻画不同预测成分间的相互作用，我们提出了一组以创新性联合阈值先验为核心的生物学合理先验模型。该模型能够捕捉信号模式的耦合与分组效应，以及其在大脑解剖结构中的空间连续性。通过开发后验推断方法，我们能够识别并量化节点层面与网络层面神经标记的信号不确定性，及其对表型结果的预测机制。大量模拟实验表明，所提方法在样本外预测和特征选择方面均显著优于现有替代方法。通过将该模型应用于儿童一般心智能力研究，我们基于识别出的任务对比特征与静息态子网络，建立了一个强大的预测机制。