Biometrics. 2022 Feb 27. doi: 10.1111/biom.13648. Online ahead of print.
ABSTRACT
Multi-modality or multi-construct data arise increasingly in functional neuroimaging studies to characterize brain activity under different cognitive states. Relying on those high-resolution imaging collections, it is of great interest to identify predictive imaging markers and inter-modality interactions with respect to behavior outcomes. Currently, most of the existing variable selection models do not consider predictive effects from interactions, and the desired higher-order terms can only be included in the predictive mechanism following a two-step procedure, suffering from potential mis-specification. In this paper, we propose a unified Bayesian prior model to simultaneously identify main effect features and inter-modality interactions within the same inference platform in the presence of high dimensional data. To accommodate the brain topological information and correlation between modalities, our prior is designed by compiling the intermediate selection status of sequential partitions in light of the data structure and brain anatomical architecture, so that we can improve posterior inference and enhance biological plausibility. Through extensive simulations, we show the superiority of our approach in main and interaction effects selection, and prediction under multi-modality data. Applying the method to the Adolescent Brain Cognitive Development (ABCD) study, we characterize the brain functional underpinnings with respect to general cognitive ability under different memory load conditions. This article is protected by copyright. All rights reserved.
PMID:35220581 | DOI:10.1111/biom.13648