Latent class and transition analysis of Alzheimer’s disease data

Ahmed A. Moustafa, Richard Tindle, Hany Ashwell, Thierno M O Diallo

Research output: Contribution to journalArticlepeer-review

Abstract

This study uses independent latent class analysis (LCA) and latent transition analysis (LTA) to explore accurate diagnosis and disease status change of a big Alzheimer’s disease Neuroimaging Initiative (ADNI) data of 2132 individuals over a three-year period. The data includes clinical and neural measures of controls (CN), individuals with subjective memory complains (SMC), early-onset mild cognitive impairment (EMCI), late-onset mild cognitive impairment (LMCI), and Alzheimer’s disease (AD). LCA at each time point yielded 3 classes: Class 1 is mostly composed of individuals from CN, SMC and EMCI groups; Class 2 represents individuals from LMCI and AD groups with improved scores on memory, clinical, and neural measures; in contrast, Class 3 represents LMCI and from AD individuals with deteriorated scores on memory, clinical, and neural measures. However, 63 individuals from Class 1 were diagnosed as AD patients. This could be misdiagnosis, as their conditional probability of belonging to Class 1 (0.65) was higher than that of Class 2 (0.27) and Class 3 (0.08). LTA results showed that individuals had a higher probability of staying in the same class over time with probability greater than 0.90 for Class 1 and 3 and probability greater than 0.85 for Class 2. Individuals from Class 2, however, transitioned to Class 1 from time 2 to time 3 with a probability of 0.10. Other transition probabilities were not significant. Lastly, further analysis showed that individuals in Class 2 who moved to Class 1 have different memory, clinical, and neural measures to other individuals in the same class. Our findings thus shed light on both the accurate diagnosis and development of AD. We acknowledge that the proposed framework is sophisticated and time-consuming;. However, given the severe neurodegenerative nature of AD, we argue that clinicians should prioritise an accurate diagnosis. Our findings show that LCA can provide a more accurate prediction for classifying and identifying the progression of AD compared to traditional clinical cut-off measures on neuropsychological assessments.
Original languageEnglish
JournalFrontiers in Computer Science
Publication statusPublished - 2020

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