Bayesian Network Modeling of Causal Influence within Cognitive Domains and Clinical Dementia Severity Ratings for Western and Indian Cohorts
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914921296429056 |
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| 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 |