Toward Digitally Screening and Profiling AD: A GAMLSS Approach of MemTrax in China

Overview

Journal Alzheimers Dement

Specialties Neurology
Psychiatry

Date 2023 Sep 1

PMID 37654085

Authors

Wanwan Liu

Ling Yu

Qiuqiong Deng

Yunrong Li

Peiwen Lu

Jie Yang

Fei Chen

Feng Li

Xianbo Zhou

Michael F Bergeron

John Wesson Ashford

Qun Xu

Affiliations

Soon will be listed here.

Abstract

Purposes: To establish a normative range of MemTrax (MTx) metrics in the Chinese population.

Methods: The correct response percentage (MTx-%C) and mean response time (MTx-RT) were obtained and the composite scores (MTx-Cp) calculated. Generalized additive models for location, shape and scale (GAMLSS) were applied to create percentile curves and evaluate goodness of fit, and the speed-accuracy trade-off was investigated.

Results: 26,633 subjects, including 13,771 (51.71%) men participated in this study. Age- and education-specific percentiles of the metrics were generated. Q tests and worm plots indicated adequate fit for models of MTx-RT and MTx-Cp. Models of MTx-%C for the low and intermediate education fit acceptably, but not well enough for a high level of education. A significant speed-accuracy trade-off was observed for MTx-%C from 72 to 94.

Conclusions: GAMLSS is a reliable method to generate smoothed age- and education-specific percentile curves of MTx metrics, which may be adopted for mass screening and follow-ups addressing Alzheimer's disease or other cognitive diseases.

Highlights: GAMLSS was applied to establish nonlinear percentile curves of cognitive decline. Subjects with a high level of education demonstrate a later onset and slower decline of cognition. Speed-accuracy trade-off effects were observed in a subgroup with moderate accuracy. MemTrax can be used as a mass-screen instrument for active cognition health management advice.

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References

Cholerton B, Weiner M, Nosheny R, Poston K, Mackin R, Tian L . Cognitive Performance in Parkinson's Disease in the Brain Health Registry. J Alzheimers Dis. 2019; 68(3):1029-1038. PMC: 6497062. DOI: 10.3233/JAD-181009. View

Sabbagh M, Boada M, Borson S, Chilukuri M, Doraiswamy P, Dubois B . Rationale for Early Diagnosis of Mild Cognitive Impairment (MCI) Supported by Emerging Digital Technologies. J Prev Alzheimers Dis. 2020; 7(3):158-164. DOI: 10.14283/jpad.2020.19. View

Ashford J, Clifford J, Anand S, Bergeron M, Ashford C, Bayley P . Correctness and response time distributions in the MemTrax continuous recognition task: Analysis of strategies and a reverse-exponential model. Front Aging Neurosci. 2022; 14:1005298. PMC: 9682919. DOI: 10.3389/fnagi.2022.1005298. View

Zygouris S, Tsolaki M . Computerized cognitive testing for older adults: a review. Am J Alzheimers Dis Other Demen. 2014; 30(1):13-28. PMC: 10852880. DOI: 10.1177/1533317514522852. View

Rigby R, Stasinopoulos D . Automatic smoothing parameter selection in GAMLSS with an application to centile estimation. Stat Methods Med Res. 2013; 23(4):318-32. DOI: 10.1177/0962280212473302. View

Scarmeas N, Albert S, Manly J, Stern Y . Education and rates of cognitive decline in incident Alzheimer's disease. J Neurol Neurosurg Psychiatry. 2006; 77(3):308-16. PMC: 2077720. DOI: 10.1136/jnnp.2005.072306. View

Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S . Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020; 396(10248):413-446. PMC: 7392084. DOI: 10.1016/S0140-6736(20)30367-6. View

Arenaza-Urquijo E, Wirth M, Chetelat G . Cognitive reserve and lifestyle: moving towards preclinical Alzheimer's disease. Front Aging Neurosci. 2015; 7:134. PMC: 4530312. DOI: 10.3389/fnagi.2015.00134. View

Murman D . The Impact of Age on Cognition. Semin Hear. 2016; 36(3):111-21. PMC: 4906299. DOI: 10.1055/s-0035-1555115. View

10.

Ashford J, Shan M, Butler S, Rajasekar A, Schmitt F . Temporal quantification of Alzheimer's disease severity: 'time index' model. Dementia. 1995; 6(5):269-80. DOI: 10.1159/000106958. View

11.

Gunstad J, Paul R, Brickman A, Cohen R, Arns M, Roe D . Patterns of cognitive performance in middle-aged and older adults: A cluster analytic examination. J Geriatr Psychiatry Neurol. 2006; 19(2):59-64. DOI: 10.1177/0891988705284738. View

12.

Bergeron M, Landset S, Zhou X, Ding T, Khoshgoftaar T, Zhao F . Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment. J Alzheimers Dis. 2020; 77(4):1545-1558. PMC: 7683062. DOI: 10.3233/JAD-191340. View

13.

Liu X, Chen X, Zhou X, Shang Y, Xu F, Zhang J . Validity of the MemTrax Memory Test Compared to the Montreal Cognitive Assessment in the Detection of Mild Cognitive Impairment and Dementia due to Alzheimer's Disease in a Chinese Cohort. J Alzheimers Dis. 2021; 80(3):1257-1267. DOI: 10.3233/JAD-200936. View

14.

Raber J, Huang Y, Ashford J . ApoE genotype accounts for the vast majority of AD risk and AD pathology. Neurobiol Aging. 2004; 25(5):641-50. DOI: 10.1016/j.neurobiolaging.2003.12.023. View

15.

Stern Y . Cognitive reserve in ageing and Alzheimer's disease. Lancet Neurol. 2012; 11(11):1006-12. PMC: 3507991. DOI: 10.1016/S1474-4422(12)70191-6. View

16.

. 2016 Alzheimer's disease facts and figures. Alzheimers Dement. 2016; 12(4):459-509. DOI: 10.1016/j.jalz.2016.03.001. View

17.

Amieva H, Mokri H, Le Goff M, Meillon C, Jacqmin-Gadda H, Foubert-Samier A . Compensatory mechanisms in higher-educated subjects with Alzheimer's disease: a study of 20 years of cognitive decline. Brain. 2014; 137(Pt 4):1167-75. DOI: 10.1093/brain/awu035. View

18.

Royston P, Wright E . Goodness-of-fit statistics for age-specific reference intervals. Stat Med. 2000; 19(21):2943-62. DOI: 10.1002/1097-0258(20001115)19:21<2943::aid-sim559>3.0.co;2-5. View

19.

Lemus-Zuniga L, Navarro-Pardo E, Moret-Tatay C, Pocinho R . Serious games for elderly continuous monitoring. Methods Mol Biol. 2014; 1246:259-67. DOI: 10.1007/978-1-4939-1985-7_16. View

20.

Zhou X, Ashford J . Advances in screening instruments for Alzheimer's disease. Aging Med (Milton). 2020; 2(2):88-93. PMC: 6880670. DOI: 10.1002/agm2.12069. View