A Bayesian Framework for Quantifying Association Between Functional and Structural Data in Neuroimaging

TL;DR

This paper introduces a Bayesian framework for statistically testing and quantifying the association between structural and functional neuroimaging data, addressing limitations of previous correlation-based methods.

Contribution

It develops a hierarchical Bayesian model that integrates brain network data with structural measures, enabling formal hypothesis testing with uncertainty quantification.

Findings

Accurately detects associations across various noise levels.

Performs well with different numbers of brain regions.

Handles diverse structural imaging measures.

Abstract

Structural and functional neuroimaging modalities provide complementary windows into brain organization: structural imaging characterizes neural tissue anatomy and microstructure, while functional imaging captures dynamic patterns of neural activity and connectivity. Together, they offer a more complete picture than either alone. Recent multimodal neuroimaging work has focused on joint modeling of structural and functional data, often assuming a strong association between them to improve prediction and interpretability. However, relatively little attention has been given to developing statistically principled frameworks for formally testing hypotheses about these associations. Existing approaches typically rely on simple correlation-based measures or heuristic integration strategies, which may fail to capture the complex dependencies inherent in neuroimaging data, particularly when functional data are represented as brain networks and structural data as region-specific anatomical measures. We address this gap by developing an explicit Bayesian hypothesis testing framework for quantifying associations between structural and functional neuroimaging data. Our approach constructs functional brain networks from fMRI data, then integrates them with structural measurements through a hierarchical Bayesian model. The Bayesian formulation naturally accommodates two types of datasets with different structures, incorporates prior knowledge, and yields full posterior uncertainty quantification. Through extensive empirical studies, we demonstrate that the proposed method achieves excellent performance in detecting associations under a wide range of settings, including varying signal-to-noise ratios, different numbers of brain regions, and diverse sets of structural imaging measures.