Modeling Causal Interactions Across Brain Functional Subnetworks for Population-specific Disease Analysis

TL;DR

This study constructs directed hyper connectomes from neuroimaging data to analyze population-specific brain network patterns related to Alzheimer’s disease and worry levels, highlighting the importance of considering racial differences in neuroimaging research.

Contribution

It introduces a novel population-aware hyper-connectome modeling approach that reveals both shared and unique brain network features across racial groups in disease and psychological risk analysis.

Findings

Neglecting racial variation reduces predictive accuracy.

Identified population-specific hyper-connectome patterns.

Discovered distinct feedback loop circuits across groups.

Abstract

Current neuroimaging studies on neurodegenerative diseases and psychological risk factors have been developed predominantly in non Hispanic White cohorts, with other populations markedly underrepresented. In this work, we construct directed hyper connectomes among large scale functional brain systems based on causal influences between brain regions, and examine their links to Alzheimer Disease progression and worry levels across racial groups. By using Health and Aging Brain Study Health Disparities (HABS HD) dataset, our experimental results suggest that neglecting racial variation in brain network architecture may reduce predictive performance in both cognitive and affective phenotypes. Important shared and population-specific hyper-connectome patterns related to both AD progression and worry levels were identified. We further observed distinct closed loop directed circuits across groups, suggesting that different populations may rely on distinct feedback based network regulation strategies when supporting cognition or managing emotional states. Together, these results indicate a common backbone of network vulnerability with population-dependent variations in regulatory coordination, underscoring the importance of population-aware neuroimaging models.