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Published VersionCC BY V4.0, Open Access
Journal article
Latent Class and Transition Analysis of Alzheimer's Disease Data
Frontiers in Computer Science, Vol.2, pp.1-13
2020
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 2,132 individuals over a 3-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 >0.90 for Class 1 and 3 and probability >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. We acknowledge that the proposed framework is sophisticated and time-consuming. However, given the severe neurodegenerative nature of AD, we argue that clinicians should prioritize 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.
Details
- Title
- Latent Class and Transition Analysis of Alzheimer's Disease Data
- Authors
- Hany Alashwal (Author) - United Arab Emirates UniversityThierno M. O. Diallo (Author) - Western Sydney UniversityRichard Tindle (Author) - Charles Sturt UniversityAhmed A Moustafa (Author) - Western Sydney UniversityAlzheimer's Disease Neuroimaging Initiative (Research Group)
- Publication details
- Frontiers in Computer Science, Vol.2, pp.1-13
- Publisher
- Frontiers Research Foundation
- Date published
- 2020
- DOI
- 10.3389/fcomp.2020.551481
- ISSN
- 2624-9898
- Copyright note
- Copyright © 2020 Alashwal, Diallo, Tindle, Moustafa and for the Alzheimer’s Disease Neuroimaging Initiative. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
- Organisation Unit
- Indigenous and Transcultural Research Centre; University of the Sunshine Coast, Queensland; School of Health and Behavioural Sciences - Legacy
- Language
- English
- Record Identifier
- 99557707902621
- Output Type
- Journal article
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