Bayesian Network Modeling of Causal Influence within Cognitive Domains and Clinical Dementia Severity Ratings for Western and Indian Cohorts

TL;DR

This paper uses Bayesian network models to analyze causal relationships in dementia severity across Western and Indian cohorts, revealing population-specific differences in domain influences.

Contribution

It introduces a Bayesian network approach with the PC algorithm to compare causal structures of dementia across diverse populations, highlighting key differences and similarities.

Findings

Stronger dependency of CDR on memory functions in both datasets

Significant variations in causal edge strengths between populations

Insights into population-specific dementia progression patterns

Abstract

This study investigates the causal relationships between Clinical Dementia Ratings (CDR) and its six domain scores across two distinct aging datasets: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Longitudinal Aging Study of India (LASI). Using Directed Acyclic Graphs (DAGs) derived from Bayesian network models, we analyze the dependencies among domain scores and their influence on the global CDR. Our approach leverages the PC algorithm to estimate the DAG structures for both datasets, revealing notable differences in causal relationships and edge strengths between the Western and Indian populations. The analysis highlights a stronger dependency of CDR scores on memory functions in both datasets, but with significant variations in edge strengths and node degrees. By contrasting these findings, we aim to elucidate population-specific differences and similarities in dementia progression, providing insights that could inform targeted interventions and improve understanding of dementia across diverse demographic contexts.