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

Fuente: arXiv
Enregistré dans:
Détails bibliographiques
Auteurs principaux: Kumar, Wupadrasta Santosh, Bhutare, Sayali Rajendra, Sinha, Neelam, Issac, Thomas Gregor
Format: Preprint
Publié: 2024
Sujets:
Accès en ligne:
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
_version_ 1866914921296429056
author Kumar, Wupadrasta Santosh
Bhutare, Sayali Rajendra
Sinha, Neelam
Issac, Thomas Gregor
author_facet Kumar, Wupadrasta Santosh
Bhutare, Sayali Rajendra
Sinha, Neelam
Issac, Thomas Gregor
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2408_12669
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Bayesian Network Modeling of Causal Influence within Cognitive Domains and Clinical Dementia Severity Ratings for Western and Indian Cohorts
Kumar, Wupadrasta Santosh
Bhutare, Sayali Rajendra
Sinha, Neelam
Issac, Thomas Gregor
Machine Learning
Artificial Intelligence
Neurons and Cognition
Applications
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.
title Bayesian Network Modeling of Causal Influence within Cognitive Domains and Clinical Dementia Severity Ratings for Western and Indian Cohorts
topic Machine Learning
Artificial Intelligence
Neurons and Cognition
Applications
url https://arxiv.org/abs/2408.12669