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Cognitive Phenotypes of Older Adults with Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment: The Czech Brain Aging Study

Published online by Cambridge University Press:  03 November 2020

Dylan J. Jester Open the ORCID record for Dylan J. Jester [Opens in a new window] ,
Ross Andel Open the ORCID record for Ross Andel [Opens in a new window] ,
Katerina Cechová Open the ORCID record for Katerina Cechová [Opens in a new window] ,
Jan Laczó ,
Ondrej Lerch ,
Hana Marková ,
Tomás Nikolai Open the ORCID record for Tomás Nikolai [Opens in a new window] ,
Martin Vyhnálek  and
Jakub Hort
Dylan J. Jester*
Affiliation:
School of Aging Studies, University of South Florida, Tampa, FL33612, USA
Ross Andel
Affiliation:
School of Aging Studies, University of South Florida, Tampa, FL33612, USA Department of Neurology, Memory Clinic, 2nd Faculty of Medicine, Charles University and Motol University Hospital, Prague, 150 06, Czech Republic International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, 656 91, Czech Republic
Katerina Cechová
Affiliation:
Department of Neurology, Memory Clinic, 2nd Faculty of Medicine, Charles University and Motol University Hospital, Prague, 150 06, Czech Republic International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, 656 91, Czech Republic
Jan Laczó
Affiliation:
Department of Neurology, Memory Clinic, 2nd Faculty of Medicine, Charles University and Motol University Hospital, Prague, 150 06, Czech Republic International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, 656 91, Czech Republic
Ondrej Lerch
Affiliation:
Department of Neurology, Memory Clinic, 2nd Faculty of Medicine, Charles University and Motol University Hospital, Prague, 150 06, Czech Republic International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, 656 91, Czech Republic
Hana Marková
Affiliation:
Department of Neurology, Memory Clinic, 2nd Faculty of Medicine, Charles University and Motol University Hospital, Prague, 150 06, Czech Republic International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, 656 91, Czech Republic
Tomás Nikolai
Affiliation:
Department of Neurology, Memory Clinic, 2nd Faculty of Medicine, Charles University and Motol University Hospital, Prague, 150 06, Czech Republic International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, 656 91, Czech Republic
Martin Vyhnálek
Affiliation:
Department of Neurology, Memory Clinic, 2nd Faculty of Medicine, Charles University and Motol University Hospital, Prague, 150 06, Czech Republic International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, 656 91, Czech Republic
Jakub Hort
Affiliation:
Department of Neurology, Memory Clinic, 2nd Faculty of Medicine, Charles University and Motol University Hospital, Prague, 150 06, Czech Republic International Clinical Research Center, St. Anne’s University Hospital Brno, Brno, 656 91, Czech Republic
*
*Correspondence and reprint requests to: Dylan Jester, School of Aging Studies, University of South Florida, 13301 Bruce B. Downs Blvd., MHC 1318, Tampa, FL33612, USA. E-mail: [email protected]
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Abstract

Objective:

To compare cognitive phenotypes of participants with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI), estimate progression to MCI/dementia by phenotype and assess classification error with machine learning.

Method:

Dataset consisted of 163 participants with SCD and 282 participants with aMCI from the Czech Brain Aging Study. Cognitive assessment included the Uniform Data Set battery and additional tests to ascertain executive function, language, immediate and delayed memory, visuospatial skills, and processing speed. Latent profile analyses were used to develop cognitive profiles, and Cox proportional hazards models were used to estimate risk of progression. Random forest machine learning algorithms reported cognitive phenotype classification error.

Results:

Latent profile analysis identified three phenotypes for SCD, with one phenotype performing worse across all domains but not progressing more quickly to MCI/dementia after controlling for age, sex, and education. Three aMCI phenotypes were characterized by mild deficits, memory and language impairment (dysnomic aMCI), and severe multi-domain aMCI (i.e., deficits across all domains). A dose–response relationship between baseline level of impairment and subsequent risk of progression to dementia was evident for aMCI profiles after controlling for age, sex, and education. Machine learning more easily classified participants with aMCI in comparison to SCD (8% vs. 21% misclassified).

Conclusions:

Cognitive performance follows distinct patterns, especially within aMCI. The patterns map onto risk of progression to dementia.

Keywords

Subjective cognitive complaintsMild cognitive impairmentTransition to dementiaMachine learningNeuropsychological performanceProspective cohort study
Type
Regular Research
Information
Journal of the International Neuropsychological Society , Volume 27 , Issue 4 , April 2021 , pp. 329 - 342
Copyright
Copyright © INS. Published by Cambridge University Press, 2020

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References

REFERENCES

Albert, M.S., DeKosky, S.T., Dickson, D., Dubois, B., Feldman, H.H., Fox, N.C., … Petersen, R.C. (2011). The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 270–279. doi: 10.1016/j.jalz.2011.03.008 CrossRefGoogle Scholar
Ali, J.I., Smart, C.M., & Gawryluk, J.R. (2018). Subjective cognitive decline and APOE ε4: A systematic review. Journal of Alzheimer’s Disease, 65(1), 303–320. doi: 10.3233/JAD-180248 CrossRefGoogle Scholar
Amariglio, R.E., Becker, J.A., Carmasin, J., Wadsworth, L.P., Lorius, N., Sullivan, C., … Sperling, R.A. (2012). Subjective cognitive complaints and amyloid burden in cognitively normal older individuals. Neuropsychologia, 50(12), 2880–2886. doi: 10.1016/j.neuropsychologia.2012.08.011 CrossRefGoogle ScholarPubMed
Balash, Y., Mordechovich, M., Shabtai, H., Giladi, N., Gurevich, T., & Korczyn, A. (2013). Subjective memory complaints in elders: Depression, anxiety, or cognitive decline? Acta Neurologica Scandinavica, 127(5), 344–350. doi: 10.1111/ane.12038 CrossRefGoogle ScholarPubMed
Beck, A.T., Epstein, N., Brown, G., & Steer, R.A. (1988). An inventory for measuring clinical anxiety: Psychometric properties. Journal of Consulting and Clinical Psychology, 56(6), 893. doi: 10.1037/0022-006X.56.6.893 CrossRefGoogle ScholarPubMed
Benedict, R.H., Schretlen, D., Groninger, L., Dobraski, M., & Shpritz, B. (1996). Revision of the brief visuospatial memory test: Studies of normal performance, reliability, and validity. Psychological Assessment, 8(2), 145. doi: 10.1037/1040-3590.8.2.145 CrossRefGoogle Scholar
Benton, A. (1969). Development of a multilingual aphasia battery: Progress and problems. Journal of the Neurological Sciences, 9(1), 39–48. doi: 10.1016/0022-510X(69)90057-4 CrossRefGoogle ScholarPubMed
Bessi, V., Mazzeo, S., Padiglioni, S., Piccini, C., Nacmias, B., Sorbi, S., & Bracco, L. (2018). From subjective cognitive decline to Alzheimer’s disease: The predictive role of neuropsychological assessment, personality traits, and cognitive reserve. A 7-year follow-up study. Journal of Alzheimer’s Disease, 63(4), 1523–1535. doi: 10.3233/JAD-171180 CrossRefGoogle ScholarPubMed
Bezdicek, O., Motak, L., Axelrod, B.N., Preiss, M., Nikolai, T., Vyhnalek, M., … Ruzicka, E. (2012). Czech version of the trail making test: Normative data and clinical utility. Archives of Clinical Neuropsychology, 27(8), 906–914. doi: 10.1093/arclin/acs084 CrossRefGoogle ScholarPubMed
Bezdicek, O., Stepankova, H., Moták, L., Axelrod, B.N., Woodard, J.L., Preiss, M., … Poreh, A. (2014). Czech version of rey auditory verbal learning test: Normative data. Aging, Neuropsychology, and Cognition, 21(6), 693–721. doi: 10.1080/13825585.2013.865699 CrossRefGoogle ScholarPubMed
Blanken, A.E., Jang, J.Y., Ho, J.K., Edmonds, E.C., Han, S.D., Bangen, K.J., & Nation, D.A. (2020). Distilling heterogeneity of mild cognitive impairment in the national Alzheimer coordinating center database using latent profile analysis. JAMA Network Open, 3(3), e200413–e200413. doi: 10.1001/jamanetworkopen.2020.0413 CrossRefGoogle ScholarPubMed
Bondi, M.W., Edmonds, E.C., Jak, A.J., Clark, L.R., Delano-Wood, L., McDonald, C.R., … Galasko, D. (2014). Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates. Journal of Alzheimer’s Disease, 42(1), 275–289. doi: 10.3233/JAD-140276 CrossRefGoogle ScholarPubMed
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.CrossRefGoogle Scholar
Clark, L.R., Delano-Wood, L., Libon, D.J., McDonald, C.R., Nation, D.A., Bangen, K.J., … Bondi, M.W. (2013). Are empirically-derived subtypes of mild cognitive impairment consistent with conventional subtypes? Journal of the International Neuropsychological Society, 19(6), 635–645. doi: 10.1017/S1355617713000313 CrossRefGoogle ScholarPubMed
Cohen, M.J., Ricci, C.A., Kibby, M.Y., & Edmonds, J.E. (2000). Developmental progression of clock face drawing in children. Child Neuropsychology, 6(1), 64–76. doi: 10.1076/0929-7049(200003)6:1;1-B;FT064 CrossRefGoogle ScholarPubMed
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, NJ, USA: Lawrence Erlbaum Associates.Google Scholar
Damian, M., Hausner, L., Jekel, K., Richter, M., Froelich, L., Almkvist, O., … Frisoni, G.B. (2013). Single-domain amnestic mild cognitive impairment identified by cluster analysis predicts Alzheimer’s disease in the European prospective DESCRIPA study. Dementia and Geriatric Cognitive Disorders, 36(1–2), 1–19. doi: 10.1159/000348354 CrossRefGoogle ScholarPubMed
Delano-Wood, L., Bondi, M.W., Sacco, J., Abeles, N., Jak, A.J., Libon, D.J., & Bozoki, A. (2009). Heterogeneity in mild cognitive impairment: Differences in neuropsychological profile and associated white matter lesion pathology. Journal of the International Neuropsychological Society, 15(6), 906–914. doi: 10.1017/S1355617709990257 CrossRefGoogle ScholarPubMed
Dufouil, C., Fuhrer, R., & Alpérovitch, A. (2005). Subjective cognitive complaints and cognitive decline: Consequence or predictor? The epidemiology of vascular aging study. Journal of the American Geriatrics Society, 53(4), 616–621. doi: 10.1111/j.1532-5415.2005.53209.x CrossRefGoogle ScholarPubMed
Edmonds, E.C., Delano-Wood, L., Clark, L.R., Jak, A.J., Nation, D.A., McDonald, C.R., … Salmon, D.P. (2015). Susceptibility of the conventional criteria for mild cognitive impairment to false-positive diagnostic errors. Alzheimer’s & Dementia, 11(4), 415–424. doi: 10.1016/j.jalz.2014.03.005 CrossRefGoogle ScholarPubMed
Goodglass, H., Kaplan, E., & Weintraub, S. (1983). Boston naming test. Philadelphia, PA, USA: Lea & Febiger.Google Scholar
Gorelick, P.B., Scuteri, A., Black, S.E., DeCarli, C., Greenberg, S.M., Iadecola, C., … Petersen, R.C. (2011). Vascular contributions to cognitive impairment and dementia: A statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 42(9), 2672–2713. doi: 10.1161/STR.0b013e3182299496 CrossRefGoogle ScholarPubMed
Harrell, F.E. (2019). Package ‘Hmisc’. CRAN2018, 235–236.Google Scholar
Hayden, K.M., Kuchibhatla, M., Romero, H.R., Plassman, B.L., Burke, J.R., Browndyke, J.N., & Welsh-Bohmer, K.A. (2014). Pre-clinical cognitive phenotypes for Alzheimer disease: A latent profile approach. The American Journal of Geriatric Psychiatry, 22(11), 1364–1374. doi: 10.1016/j.jagp.2013.07.008 CrossRefGoogle ScholarPubMed
Hort, J., Laczó, J., Vyhnálek, M., Bojar, M., Bureš, J., & Vlček, K. (2007). Spatial navigation deficit in amnestic mild cognitive impairment. Proceedings of the National Academy of Sciences, 104(10), 4042–4047. doi: 10.1073/pnas.0611314104 CrossRefGoogle ScholarPubMed
Hughes, C.P., Berg, L., Danziger, W., Coben, L.A., & Martin, R.L. (1982). A new clinical scale for the staging of dementia. The British Journal of Psychiatry, 140(6), 566–572. doi: 10.1192/bjp.140.6.566 CrossRefGoogle ScholarPubMed
Jack, C.R., Bennett, D.A., Blennow, K., Carrillo, M.C., Dunn, B., Haeberlein, S.B., … Liu, E. (2018). NIA-AA research framework: Toward a biological definition of Alzheimer’s disease. Alzheimer’s & Dementia, 14(4), 535–562. doi: 10.1016/j.jalz.2018.02.018 CrossRefGoogle ScholarPubMed
Jessen, F., Amariglio, R.E., Buckley, R.F., van der Flier, W.M., Han, Y., Molinuevo, J.L., … Sikkes, S.A. (2020). The characterisation of subjective cognitive decline. The Lancet Neurology, 19(3), 271–278. doi: 10.1016/S1474-4422(19)30368-0 CrossRefGoogle ScholarPubMed
Jessen, F., Amariglio, R.E., Van Boxtel, M., Breteler, M., Ceccaldi, M., Chételat, G., … Van Der Flier, W.M. (2014). A conceptual framework for research on subjective cognitive decline in preclinical Alzheimer’s disease. Alzheimer’s & Dementia, 10(6), 844–852. doi: 10.1016/j.jalz.2014.01.001 CrossRefGoogle ScholarPubMed
Kassambara, A., Kosinski, M., Biecek, P., & Fabian, S. (2017). Package ‘survminer’. Drawing Survival Curves using ‘ggplot2’.(R package version 0.3. 1.).Google Scholar
Köhler, S., Hamel, R., Sistermans, N., Koene, T., Pijnenburg, Y.A., van der Flier, W.M., … Ramakers, I. (2013). Progression to dementia in memory clinic patients without dementia: A latent profile analysis. Neurology, 81(15), 1342–1349. doi: 10.1212/WNL.0b013e3182a82536 CrossRefGoogle ScholarPubMed
Laczó, J., Vlček, K., Vyhnálek, M., Vajnerová, O., Ort, M., Holmerová, I., … Hort, J. (2009). Spatial navigation testing discriminates two types of amnestic mild cognitive impairment. Behavioural Brain Research, 202(2), 252–259. doi: 10.1016/j.bbr.2009.03.041 CrossRefGoogle ScholarPubMed
Lebedev, A., Westman, E., Van Westen, G., Kramberger, M., Lundervold, A., Aarsland, D., … Tsolaki, M. (2014). Random Forest ensembles for detection and prediction of Alzheimer’s disease with a good between-cohort robustness. NeuroImage: Clinical, 6, 115–125. doi: 10.1016/j.nicl.2014.08.023 CrossRefGoogle ScholarPubMed
Lehmann, C., Koenig, T., Jelic, V., Prichep, L., John, R.E., Wahlund, L.-O., … Dierks, T. (2007). Application and comparison of classification algorithms for recognition of Alzheimer’s disease in electrical brain activity (EEG). Journal of Neuroscience Methods, 161(2), 342–350. doi: 10.1016/j.jneumeth.2006.10.023 CrossRefGoogle Scholar
Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18–22.Google Scholar
Libon, D.J., Xie, S.X., Eppig, J., Wicas, G., Lamar, M., Lippa, C., … Swenson, R. (2010). The heterogeneity of mild cognitive impairment: A neuropsychological analysis. Journal of the International Neuropsychological Society, 16(1), 84–93. doi: 10.1017/S1355617709990993 CrossRefGoogle ScholarPubMed
Llano, D.A., Laforet, G., & Devanarayan, V. (2011). Derivation of a new ADAS-cog composite using tree-based multivariate analysis: Prediction of conversion from mild cognitive impairment to Alzheimer disease. Alzheimer Disease & Associated Disorders, 25(1), 73–84. doi: 10.1097/WAD.0b013e3181f5b8d8 CrossRefGoogle ScholarPubMed
Machulda, M.M., Lundt, E.S., Albertson, S.M., Kremers, W.K., Mielke, M.M., Knopman, D.S., … Petersen, R.C. (2019). Neuropsychological subtypes of incident mild cognitive impairment in the Mayo clinic study of aging. Alzheimer’s & Dementia, 15(7), 878–887. doi: 10.1016/j.jalz.2019.03.014 CrossRefGoogle ScholarPubMed
Maroco, J., Silva, D., Rodrigues, A., Guerreiro, M., Santana, I., & de Mendonça, A. (2011). Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Research Notes, 4(1), 299. doi: 10.1186/1756-0500-4-299 CrossRefGoogle ScholarPubMed
Mazancova, A.F., Nikolai, T., Stepankova, H., Kopecek, M., & Bezdicek, O. (2017). The reliability of clock drawing test scoring systems modeled on the normative data in healthy aging and nonamnestic mild cognitive impairment. Assessment, 24(7), 945–957. doi: 10.1177/1073191116632586 CrossRefGoogle ScholarPubMed
Mazzeo, S., Padiglioni, S., Bagnoli, S., Bracco, L., Nacmias, B., Sorbi, S., & Bessi, V. (2019). The dual role of cognitive reserve in subjective cognitive decline and mild cognitive impairment: A 7-year follow-up study. Journal of Neurology, 266(2), 487–497. doi: 10.1007/s00415-018-9164-5 CrossRefGoogle ScholarPubMed
McGuinness, B., Barrett, S.L., McIlvenna, J., Passmore, A.P., & Shorter, G.W. (2015). Predicting conversion to dementia in a memory clinic: A standard clinical approach compared with an empirically defined clustering method (latent profile analysis) for mild cognitive impairment subtyping. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, 1(4), 447–454. doi: 10.1016/j.dadm.2015.10.003 Google Scholar
Meyers, J.E., & Meyers, K.R. (1995). Rey complex figure test and recognition trial professional manual. Lutz, FL, USA: Psychological Assessment Resources.Google Scholar
Mitchell, A.J., Beaumont, H., Ferguson, D., Yadegarfar, M., & Stubbs, B. (2014). Risk of dementia and mild cognitive impairment in older people with subjective memory complaints: Meta-analysis. Acta Psychiatrica Scandinavica, 130(6), 439–451. doi: 10.1111/acps.12336 CrossRefGoogle ScholarPubMed
Mitchell, A.J., & Shiri-Feshki, M. (2009). Rate of progression of mild cognitive impairment to dementia–meta-analysis of 41 robust inception cohort studies. Acta Psychiatry Scandinavica, 119(4), 252–265. doi: 10.1111/j.1600-0447.2008.01326.x CrossRefGoogle Scholar
Nettiksimmons, J., DeCarli, C., Landau, S., Beckett, L., & Alzheimer’s Disease Neuroimaging Initiative. (2014). Biological heterogeneity in ADNI amnestic mild cognitive impairment. Alzheimer’s & Dementia, 10(5), 511–521. doi: 10.1016/j.jalz.2013.09.003 CrossRefGoogle ScholarPubMed
Nikolai, T., Stepankova, H., Kopecek, M., Sulc, Z., Vyhnalek, M., & Bezdicek, O. (2018). The uniform data set, Czech version: Normative data in older adults from an international perspective. Journal of Alzheimer’s Disease, 61(3), 1233–1240. doi: 10.3233/JAD-170595 CrossRefGoogle ScholarPubMed
Nikolai, T., Štěpánková, H., Michalec, J., Bezdíček, O., Horáková, K., Marková, H., … Kopeček, M. (2015). Testy verbální fluence, česká normativní studie pro osoby vyššího věku. Česká a Slovenská Neurologie a Neurochirurgie, 78/111(3), 292–299. doi: 10.14735/amcsnn2015292 CrossRefGoogle Scholar
Osterrieth, P. (1944). Le test de copie d’une figure complexe [The test of copying a complex figure]. Archives de Psychologie, 30, 206–356.Google Scholar
Petersen, R.C., Smith, G.E., Waring, S.C., Ivnik, R.J., Tangalos, E.G., & Kokmen, E. (1999). Mild cognitive impairment: Clinical characterization and outcome. Archives of Neurology, 56(3), 303–308. doi: 10.1001/archneur.56.3.303 CrossRefGoogle ScholarPubMed
Preiss, M., Kalivodová, Z., Kundrátová, I., Mrlinová, L., Ježková, T., Kubů, M., & Houbová, P. (2002). Test verbální fluence–vodítka pro všeobecnou dospělou populaci. Psychiatrie, 6(2), 74–77.Google Scholar
Ramirez, J., Chaves, R., Gorriz, J., Lopez, M., Lvarez, I.Á., Salas-Gonzalez, D., … Padilla, P. (2009). Computer aided diagnosis of the Alzheimer’s disease combining spect-based feature selection and random forest classifiers. Paper presented at the 2009 IEEE Nuclear Science Symposium Conference Record (NSS/MIC). doi: 10.1109/NSSMIC.2009.5401968 CrossRefGoogle Scholar
Reitan, R.M. (1958). Validity of the trail making test as an indicator of organic brain damage. Perceptual and Motor Skills, 8(3), 271–276. doi: 10.2466/PMS.8.7.271-276 CrossRefGoogle Scholar
Scrucca, L., Fop, M., Murphy, T.B., & Raftery, A.E. (2016). mclust 5: Clustering, classification and density estimation using Gaussian finite mixture models. The R journal, 8(1), 289.CrossRefGoogle ScholarPubMed
Selnes, P., Aarsland, D., Bjørnerud, A., Gjerstad, L., Wallin, A., Hessen, E., … Kjærvik, V.K. (2013). Diffusion tensor imaging surpasses cerebrospinal fluid as predictor of cognitive decline and medial temporal lobe atrophy in subjective cognitive impairment and mild cognitive impairment. Journal of Alzheimer’s Disease, 33(3), 723–736. doi: 10.3233/JAD-2012-121603 CrossRefGoogle ScholarPubMed
Sheardova, K., Vyhnalek, M., Nedelska, Z., Laczo, J., Andel, R., Marciniak, R., … Hort, J. (2019). Czech Brain Aging Study (CBAS): Prospective multicentre cohort study on risk and protective factors for dementia in the Czech Republic. BMJ Open, 9(12). doi: 10.1136/bmjopen-2019-030379 CrossRefGoogle ScholarPubMed
Štěpánková, H., Nikolai, T., Lukavský, J., Bezdíček, O., Vrajová, M., & Kopeček, M. (2015). Mini-mental state examination–česká normativní studie. Ceska a Slovenska Neurologie a Neurochirurgie, 78(111), 1.Google Scholar
Stern, Y. (2012). Cognitive reserve in ageing and Alzheimer’s disease. The Lancet Neurology, 11(11), 1006–1012. doi: 10.1016/S1474-4422(12)70191-6 CrossRefGoogle ScholarPubMed
Tein, J.Y., Coxe, S., & Cham, H. (2013). Statistical power to detect the correct number of classes in latent profile analysis. Structural Equation Modeling: A Multidisciplinary Journal, 20(4), 640–657. doi: 10.1080/10705511.2013.824781 CrossRefGoogle ScholarPubMed
Therneau, T.M., & Lumley, T. (2015). Package ‘survival’. R Top Doc, 128, 112.Google Scholar
Verma, M., & Howard, R.J. (2012). Semantic memory and language dysfunction in early Alzheimer’s disease: A review. International Journal of Geriatric Psychiatry, 27(12), 1209–1217. doi: 10.1002/gps.3766 CrossRefGoogle ScholarPubMed
Wechsler, D. & De Lemos, M.M. (1981). Wechsler adult intelligence scale-revised. New York, NY, USA: Harcourt Brace Jovanovich.Google Scholar
Wechsler, D. III (1997). WMS-III administration and scoring manual. San Antonio, TX: The Psychological Corporation.Google Scholar
Wolfsgruber, S., Kleineidam, L., Wagner, M., Mösch, E., Bickel, H., Lϋhmann, D., … Brettschneider, C. (2016). Differential risk of incident Alzheimer’s disease dementia in stable versus unstable patterns of subjective cognitive decline. Journal of Alzheimer’s Disease, 54(3), 1135–1146. doi: 10.3233/JAD-160407 CrossRefGoogle ScholarPubMed
Yaffe, K., Petersen, R.C., Lindquist, K., Kramer, J., & Miller, B. (2006). Subtype of mild cognitive impairment and progression to dementia and death. Dementia and Geriatric Cognitive Disorders, 22(4), 312–319. doi: 10.1159/000095427 CrossRefGoogle ScholarPubMed
Yesavage, J.A., Brink, T.L., Rose, T.L., Lum, O., Huang, V., Adey, M., & Leirer, V.O. (1982). Development and validation of a geriatric depression screening scale: A preliminary report. Journal of Psychiatric Research, 17(1), 37–49. doi: 10.1016/0022-3956(82)90033-4 CrossRefGoogle ScholarPubMed
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