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The correlation of everyday cognition test scores and the progression of Alzheimer’s disease: a data analytics study

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Abstract

The process of diagnosing dementia conditions, especially Alzheimer’s disease, and the cognitive tests that are involved in this process, are important areas of study. Everyday Cognition (ECog) is one test that can be used as part of Alzheimer’s disease diagnosis to measure cognitive decline in different areas. In this study, we investigate two versions of the ECog test: the study partner reported version (ECogSP), and the patient reported version (ECogPT). We compare these, using statistical analysis and machine learning techniques, to create classification models to demonstrate the progression in ECog scores over time by using the Alzheimer’s Disease Neuroimaging Initiative longitudinal data repository (ADNI); participants are classed with having normal cognition, mild cognitive impairment, or Alzheimer’s disease. We found that participants who are diagnosed with Alzheimer’s disease at baseline, or during a subsequent visit, tend to self-report consistent ECogPT scores over time indicating no change in cognitive ability. However, study partners tend to report higher and increasing ECogSP scores on behalf of participants in the same diagnosis category; this would indicate a degradation in the participant’s cognitive ability over time, consistent with the progress of Alzheimer’s disease.

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References

  1. ADNI. Alzheimer’s disease neuroimaging initiative. 2017. https://adni.loni.usc.edu/about/#core-container. Accessed 22 Oct 2019.

  2. Bakkour A, Morris JC, Wolk DA, Dickerson BC. The cortical signature of prodromal AD: regional thinning predicts mild AD dementia. Neurology. 2009;72:1048–55.

    Article  Google Scholar 

  3. Balsis S, Benge JF, Lowe DA, Geraci L, Doody RS. How do scores on the ADAS-Cog, MMSE, and CDR-SOB correspond? Clin Neuropsychol. 2015;29(7):1002–9.

    Article  Google Scholar 

  4. Bates DM, Pinheiro JC. Linear and nonlinear mixed-effects models. Appl Stat Agric. 1998. https://doi.org/10.4148/2475-7772.1273.

    Article  Google Scholar 

  5. Bergeron D, Flynn K, Verret L, Poulin S, Bouchard RW, Bocti C, Fülöp T, Lacombe G, Gauthier S, Nasreddine Z, Laforce RJ. Multicenter validation of an MMSE-Mo CA conversion table. J Am Geriatr Soc. 2017;65(5):1067–72.

    Article  Google Scholar 

  6. Breiman L. Random forests. Machine Learning. 2001;45(1):5–32.

    Article  Google Scholar 

  7. Carr DB, Gray S, Baty J, Morris JC. The value of study partner versus individual’s complaints of memory impairment in early dementia. Neurology. 2000;55(11):1724–7.

    Article  Google Scholar 

  8. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–57.

    Article  Google Scholar 

  9. Cohen W. Fast effective rule induction. In: Prieditis A, Russell S, editors. Proceedings of the 12th international conference on machine learning, ICML. Tahoe City: Morgan Kaufmann; 1995. p. 115–23.

  10. Davatzikos C, Bhatt P, Shaw LM, Batmanghelich KN, Trojanowski JQ. Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiol Aging. 2011;32(12):2322–e19.

    Article  Google Scholar 

  11. Davatzikos C, Xu F, An Y, Fan Y, Resnick SM. Longitudinal progression of Alzheimer’s-like patterns of atrophy in normal older adults: The SPARE-AD index. Brain. 2009;132(8):2026–35.

    Article  Google Scholar 

  12. Desikan RS, Cabral HJ, Settecase F, Hess CP, Dillon WP, Glastonbury CM, Weiner MW, Schmansky NJ, Salat DH, Fischl B, The Alzheimer’s Disease Neuroimaging Initiative. Automated MRI measures predict progression to Alzheimer's disease. Neurobiol Aging. 2010;31(8):1364–74. https://doi.org/10.1016/j.neurobiolaging.2010.04.023.

    Article  Google Scholar 

  13. Evans MC, Barnes J, Nielsen C, Kim LG, Clegg SL, Blair M, Leung KK, Douiri A, Boyes RG, Ourselin S, Fox NC. Volume changes in Alzheimer’s disease and mild cognitive impairment: cognitive associations. Eur Radiol. 2010;20(3):674–82.

    Article  Google Scholar 

  14. Farias ST, Mungas D, Reed BR, Cahn-Weiner D, Jagust W, Baynes K, DeCarli C. The measurement of everyday cognition (ECog): scale development and psychometric properties. Neuropsychology. 2008;22(4):531.

    Article  Google Scholar 

  15. Folstein M, Folstein SE, McHugh P. “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–98. https://doi.org/10.1016/0022-3956(75)90026-6.

    Article  Google Scholar 

  16. Frank E, Witten I. Generating accurate rule sets without global optimisation. In: Proceedings of the fifteenth international conference on machine learning, Madison, WI; 1998. p. 144–51.

  17. Geuze E, Vermetten E, Bremner JD. MR-based in vivo hippocampal volumetrics: 2. Findings in neuropsychiatric disorders. Mol Psychiatry. 2005;10(2):160–84. https://doi.org/10.1038/sj.mp.4001579.

    Article  Google Scholar 

  18. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA data mining software: An update. ACM SIGKDD Explor Newsl. 2009;11(1):10–8.

    Article  Google Scholar 

  19. Ito K, Hutmacher MM, Corrigan BW. Modeling of functional assessment questionnaire (FAQ) as continuous bounded data from the ADNI database. J Pharmacokinet Pharmacodyn. 2012;39(6):601–18.

    Article  Google Scholar 

  20. Jack CR Jr, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW, Petersen RC, Trojanowski JQ. Hypothetical model of dynamic biomarkers of the Alzheimer's pathological cascade. Lancet Neurol. 2010;9(1):119–28. https://doi.org/10.1016/S1474-4422(09)70299-6.

    Article  Google Scholar 

  21. Jack CR, Lowe VJ, Weigand SD, Wiste HJ, Senjem ML, Knopman DS, Shiung MM, Gunter JL, Boeve BF, Kemp BJ, Weiner M, Petersen RC, The Alzheimer's Disease Neuroimaging Initiative. Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer's disease: implications for sequence of pathological events in Alzheimer's disease. Brain. 2009;132(5):1355–65. https://doi.org/10.1093/brain/awp062.

    Article  Google Scholar 

  22. Killiany RJ, Hyman BT, Gomez-Isla T, Moss MB, Kikinis R, Jolesz F, Tanzi R, Jones K, Albert MS. MRI measures of entorhinal cortex vs hippocampus in preclinical AD. Neurology. 2002;58(8):1188–96. https://doi.org/10.1212/WNL.58.8.1188.

    Article  Google Scholar 

  23. Ministry of Health NZ. Dementia, treatment. 2018. Retrieved from https://www.health.govt.nz/your-health/conditions-and-treatments/diseases-and-illnesses/dementia. Accessed 4 Jan 2020.

  24. Moradi E, Hallikainen I, Hänninen T, Tohka J, Alzheimer’s Disease Neuroimaging Initiative. Rey's auditory verbal learning test scores can be predicted from whole brain MRI in Alzheimer’s disease. NeuroImage Clinical. 2017;13:415–27.

    Article  Google Scholar 

  25. Mueller SG, Weiner MW, Thal LJ, Petersen RC, Jack C, Jagust W, Trojanowski JQ, Toga AW, Beckett L. The Alzheimer’s disease neuroimaging initiative. Neuroimaging Clin N Am. 2005;15(4):869–xii. https://doi.org/10.1016/j.nic.2005.09.008.

    Article  Google Scholar 

  26. Pfeffer RI, Kurosaki TT, Harrah HC, Chance JM, Filos S. Measurement of functional activities in older adults in the community. J Gerontol. 1982;37(3):323–9. https://doi.org/10.1093/geronj/37.3.323.

    Article  Google Scholar 

  27. Picard RR, Cook RD. Cross-validation of regression models. J Am Stat Assoc. 1984;79(387):575–83.

    Article  MathSciNet  Google Scholar 

  28. Quinlan JR. C4. 5: programs for machine learning. Amsterdam: Elsevier; 2014.

    Google Scholar 

  29. Rabin LA, Wang C, Katz MJ, Derby CA, Buschke H, Lipton RB. Predicting Alzheimer’s Disease: Neuropsychological tests, self-reports, and study partner reports of cognitive difficulties. J Am Geriatr Soc. 2012;60(6):1128–34.

    Article  Google Scholar 

  30. Rey A. L’examen psychologique dans les cas d’encéphalopathie traumatique. Arch Psychol. 1941;28:286–340.

    Google Scholar 

  31. Rosen W, Mohs R, Davis K. A new rating scale for Alzheimer’s disease. Am J Psychiatry. 1984;141(11):1356–64. https://doi.org/10.1176/ajp.141.11.1356.

    Article  Google Scholar 

  32. Schmidt M. Rey Auditory verbal learning test: a handbook. Los Angeles, CA: Western Psychological Services; 1996.

    Google Scholar 

  33. Schuff N, Woerner N, Boreta L, Kornfield T, Shaw LM, Trojanowski JQ, Thompson PM, Jack CR Jr, The Alzheimer’s Disease Neuroimaging Initiative. MRI of hippocampal volume loss in early Alzheimer’s disease in relation to ApoE genotype and biomarkers. Brain. 2009;132(4):1067–77. https://doi.org/10.1093/brain/awp007.

    Article  Google Scholar 

  34. The Alzheimer’s Disease Prediction of Longitudinal Evolution (TADPOLE). TADPOLE-Home. 2019. https://tadpole.grand-challenge.org/. Accessed 5 Dec 2019.

  35. Varon D, Barker W, Loewenstein D, Greig M, Bohorquez A, Santos I, Shen Q, Harper M, Vallejo-Luces D, R. Visual rating and volumetric measurement of medial temporal atrophy in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort: baseline diagnosis and the prediction of MCI outcome. Int J Geriatr Psychiatry. 2015;30(2):192–200. https://doi.org/10.1002/gps.4126.

    Article  Google Scholar 

  36. Vemuri P, Wiste H, Weigand S, Knopman D, Trojanowski J, Shaw L, Bernstein MA, Aisen PS, Weiner M, Petersen RC, Jack CR Jr, Alzheimer’s Disease Neuroimaging Initiative. Serial MRI and CSF biomarkers in normal aging, MCI, and AD. Neurology. 2010;75(2):143–51. https://doi.org/10.1212/WNL.0b013e3181e7ca82.

    Article  Google Scholar 

  37. Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Liu E, Morris JC, Petersen RC, Saykin AJ, Schmidt ME, Shaw L, Siuciak JA, Soares H, Toga AW, Trojanowski JQ, Si JA. The Alzheimer’s Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimer’s Dement. 2012;8(10):S1–S68. https://doi.org/10.1016/j.nic.2005.09.008.

    Article  Google Scholar 

  38. World Health Organization. (2019). Dementia. Retrieved from https://www.who.int/news-room/fact-sheets/detail/dementia. Accessed 11 Oct 2019.

  39. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B (Stat Methodol). 2005;67(2):301–20.

    Article  MathSciNet  Google Scholar 

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Correspondence to Fadi Thabtah.

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Table 3 Dataset features

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Thabtah, F., Spencer, R. & Ye, Y. The correlation of everyday cognition test scores and the progression of Alzheimer’s disease: a data analytics study. Health Inf Sci Syst 8, 24 (2020). https://doi.org/10.1007/s13755-020-00114-8

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