Integrative Predictor-Dependent Learning of Network Data and Spatially Correlated Nodal Attributes for Multimodal Brain Imaging in Aging

TL;DR

This paper presents a flexible Bayesian framework for joint modeling of brain network data and spatially correlated attributes, enabling comprehensive inference on aging-related brain changes from multimodal imaging data.

Contribution

The novel framework simultaneously models network structure, spatial correlations, and predictor relationships, improving inference accuracy in multimodal brain imaging analysis.

Findings

Outperforms existing methods by accounting for network and spatial correlations.

Provides robust uncertainty quantification in small sample scenarios.

Facilitates integrated analysis of structural and functional brain data.

Abstract

This article introduces a predictor-dependent joint modeling framework for network data obtained from multiple subjects over a shared set of nodes with spatial co-ordinates and spatially correlated nodal attributes. The framework is highly flexible, allowing concurrent inference on nodes significantly associated with a predictor, spatial associations of nodal attributes and the regression relationship between a predictor and edge connecting a pair of nodes or a specific nodal attribute. Empirical results indicate a superior performance of the proposed approach due to accounting for network structure and spatial correlation in the data simultaneously. The methodology analyzes multimodal brain imaging data collected first-hand in the coauthor's Lifespan Cognitive and Motor Neuroimaging Laboratory, with a focus on integrating structural and functional information. It examines brain connectivity, represented as a connectome network across regions of interest (ROIs) derived from functional magnetic resonance imaging (fMRI), while also incorporating ROI-specific attributes obtained from structural MRI data, for each subject. Subject-specific aging-related features and spatial locations of ROIs are incorporated in the analysis. This framework facilitates robust inference on the associations between predictors and brain connectivity patterns, the spatial relationships among ROI-specific attributes, and the regression relationships involving edges or ROI-specific attributes with aging-related predictors. By integrating these diverse data sources, the approach provides a deeper understanding of the complex interplay between brain structure, function, aging-related changes, and external predictors. As a model-based Bayesian approach, it provides uncertainty quantification for all inferences, offering robust and reliable results, particularly in scenarios with limited sample size.