Abstract
BackgroundAlthough some neuropsychological (NP) tests are considered more central for the diagnosis of Alzheimer disease (AD), there is a lack of understanding about the interaction between different cognitive tests.ObjectiveThis study aimed to demonstrate a global view of hierarchical probabilistic dependencies between NP tests and the likelihood of cognitive impairment to assist physicians in recognizing AD precursors.MethodsOur study included 2091 participants from the Framingham Heart Study. These participants had undergone a variety of NP tests, including Wechsler Memory Scale, Wechsler Adult Intelligence Scale, and Boston Naming Test. Heterogeneous cognitive Bayesian networks were developed to understand the relationship between NP tests and the cognitive status. The performance of probabilistic inference was evaluated by the 10-fold cross validation.ResultsA total of 4512 NP tests were used to build the Bayesian network for the dementia diagnosis. The network demonstrated conditional dependency between different cognitive functions that precede the development of dementia. The prediction model reached an accuracy of 82.24%, with sensitivity of 63.98% and specificity of 92.74%. This probabilistic diagnostic system can also be applied to participants that exhibit more heterogeneous profiles or with missing responses for some NP tests.ConclusionsWe developed a probabilistic dependency network for AD diagnosis from 11 NP tests. Our study revealed important psychological functional segregations and precursor evidence of AD development and heterogeneity.
Highlights
BackgroundAlzheimer disease (AD) is a chronic neurodegenerative disease characterized by cognitive decline [1]
Our study included 4512 sets of NP tests from 2091 participants, primarily of European ancestry (1166 females, mean age 79 [SD 6] years)
32 NP tests have been administered at the Framingham Heart Study (FHS) (Multimedia Appendix 1), this study was focused on 11 NP tests that were administered to more than 85% of participants between 1999 and 2016
Summary
BackgroundAlzheimer disease (AD) is a chronic neurodegenerative disease characterized by cognitive decline [1]. Some neuropsychological (NP) tests are considered more central for the diagnosis of Alzheimer disease (AD), there is a lack of understanding about the interaction between different cognitive tests. Objective: This study aimed to demonstrate a global view of hierarchical probabilistic dependencies between NP tests and the likelihood of cognitive impairment to assist physicians in recognizing AD precursors. Heterogeneous cognitive Bayesian networks were developed to understand the relationship between NP tests and the cognitive status. The network demonstrated conditional dependency between different cognitive functions that precede the development of dementia. The prediction model reached an accuracy of 82.24%, with sensitivity of 63.98% and specificity of 92.74% This probabilistic diagnostic system can be applied to participants that exhibit more heterogeneous profiles or with missing responses for some NP tests. Conclusions: We developed a probabilistic dependency network for AD diagnosis from 11 NP tests. Our study revealed important psychological functional segregations and precursor evidence of AD development and heterogeneity
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