Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-04T18:28:04.817Z Has data issue: false hasContentIssue false
  • English
  • Français

Neuropsychological Test Performance and Cognitive Reserve in Healthy Aging and the Alzheimer's Disease Spectrum: A Theoretically Driven Factor Analysis

Published online by Cambridge University Press:  08 October 2012

Meghan B. Mitchell ,
Lynn W. Shaughnessy ,
Steven D. Shirk ,
Frances M. Yang  and
Alireza Atri
Meghan B. Mitchell*
Affiliation:
Geriatric Research Education and Clinical Center, Edith Nourse Rogers Memorial Veterans Hospital, Bedford, Massachusetts Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts Harvard Medical School, Boston, Massachusetts
Lynn W. Shaughnessy
Affiliation:
Harvard Medical School, Boston, Massachusetts Department of Psychiatry, Massachusetts Mental Health Center, Boston, Massachusetts Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, Massachusetts
Steven D. Shirk
Affiliation:
Geriatric Research Education and Clinical Center, Edith Nourse Rogers Memorial Veterans Hospital, Bedford, Massachusetts Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
Frances M. Yang
Affiliation:
Harvard Medical School, Boston, Massachusetts Institute for Aging Research, Hebrew Senior Life, Boston, Massachusetts Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
Alireza Atri
Affiliation:
Geriatric Research Education and Clinical Center, Edith Nourse Rogers Memorial Veterans Hospital, Bedford, Massachusetts Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts Harvard Medical School, Boston, Massachusetts
*
Correspondence and reprint requests to: Meghan Mitchell, 200 Springs Road, 182b, Edith Nourse Rogers Memorial Veterans Hospital, Bedford, MA 01730. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Accurate measurement of cognitive function is critical for understanding the disease course of Alzheimer's disease (AD). Detecting cognitive change over time can be confounded by level of premorbid intellectual function or cognitive reserve and lead to under- or over-diagnosis of cognitive impairment and AD. Statistical models of cognitive performance that include cognitive reserve can improve sensitivity to change and clinical efficacy. We used confirmatory factor analysis to test a four-factor model composed of memory/language, processing speed/executive function, attention, and cognitive reserve factors in a group of cognitively healthy older adults and a group of participants along the spectrum of amnestic mild cognitive impairment to AD (aMCI-AD). The model showed excellent fit for the control group (χ2 = 100; df = 78; CFI = .962; RMSEA = .049) and adequate fit for the aMCI-AD group (χ2 = 1750; df = 78; CFI = .932; RMSEA = .085). Although strict invariance criteria were not met, invariance testing to determine if factor structures are similar across groups yielded acceptable absolute model fits and provide evidence in support of configural, metric, and scalar invariance. These results provide further support for the construct validity of cognitive reserve in healthy and memory impaired older adults. (JINS, 2012, 18, 1–10)

Keywords

Mild cognitive impairmentBrain reserveExecutive functionMemory functionDementiaCognition
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 18 , Issue 6 , November 2012 , pp. 1071 - 1080
Copyright
Copyright © The International Neuropsychological Society 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alzheimer's Association. (2011). 2011 Alzheimer's disease facts and figures. Alzheimer's & Dementia, 4(2), 110–133.Google Scholar
Atri, A., Rountree, S.D., Lopez, O.L., Doody, R.S. (2012). Validity, significance, strengths, limitations, and evidentiary value of real-world clinical data for combination therapy in Alzheimer's disease: Comparison of efficacy and effectiveness studies. Neuro-degenerative Diseases, 10, 170–174.CrossRefGoogle ScholarPubMed
Atri, A., Shaughnessy, L.W., Locascio, J.J., Growdon, J.H. (2008). Long-term course and effectiveness of combination therapy in Alzheimer disease. Alzheimer Disease and Associated Disorders, 22(3), 209–221.CrossRefGoogle ScholarPubMed
Beekly, D.L., Ramos, E.M., Lee, W.W., Deitrich, W.D., Jacka, M.E., Wu, J., Kukull, W.A. (2007). The National Alzheimer's Coordinating Center (NACC) database: The uniform data set. Alzheimer Disease and Associated Disorders, 21(3), 249–258.CrossRefGoogle Scholar
Bentler, P.M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246.CrossRefGoogle ScholarPubMed
Bentler, P., Chou, C. (1988). Practical issues in structural equation modeling. In J. Long (Ed.), Common problems/proper solutions: Avoiding error in quantitative research. Newbury Park, CA: Sage.Google Scholar
Blom, G. (1958). Statistical estimates and transformed beta variables. New York: John Wiley & Sons, Inc.Google Scholar
Browne, M., Cudek, R. (1993). Alternative ways of assessing model fit. In K. Bollen & J. Long (Eds.), Testing structural equation models (pp. 136–162). Thousand Oaks, CA: Sage.Google Scholar
Cheung, G.W., Rensvold, R.B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9(2), 233–255.CrossRefGoogle Scholar
Cohen, J.D. (1988). Statistical power analysis for the behavioral sciences, 2nd edition. Hillsdale, NJ: L. Erlbaum Associates.Google Scholar
DeKosky, S. (2003). Early intervention is key to successful management of Alzheimer disease. Alzheimer Disease and Associated Disorders, 17(Suppl. 4), S99–S104.CrossRefGoogle ScholarPubMed
Dickerson, B.C., Bakkour, A., Salat, D.H., Feczko, E., Pacheco, J., Greve, D.N., Buckner, R.L. (2009). The cortical signature of Alzheimer's disease: Regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals. Cerebral Cortex, 19(3), 497–510.CrossRefGoogle ScholarPubMed
Embretson, S.E., Reise, S.P. (2000). Item response theory for psychologists. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Gallo, J.J., Anthony, J.C., Muthén, B.O. (1994). Age differences in the symptoms of depression: A latent trait analysis. Journals of Gerontology, Psychological Sciences, 49(6), 251–264.CrossRefGoogle ScholarPubMed
Grober, E., Lipton, R.B., Hall, C., Crystal, H. (2000). Memory impairment on free and cued selective reminding predicts dementia. Neurology, 54(4), 827–832.CrossRefGoogle ScholarPubMed
Grober, E., Sanders, A.E., Hall, C., Lipton, R.B. (2010). Free and cued selective reminding identifies very mild dementia in primary care. Alzheimer Disease and Associated Disorders, 24(3), 284–290.CrossRefGoogle ScholarPubMed
Grober, E., Sliwinski, M. (1991). Development and validation of a model for estimating premorbid verbal intelligence in the elderly. Journal of Clinical and Experimental Neuropsychology, 13(6), 933–949.CrossRefGoogle Scholar
Hauser, R.M., Goldberger, A.S. (1971). The treatment of unobservable variables in path analysis. In H.L. Costner (Ed.), Sociological methodology (pp. 81–117). San Francisco: Jossey-Bass.Google Scholar
Hayden, K.M., Jones, R.N., Zimmer, C., Plassman, B.L., Browndyke, J.N., Pieper, C., Welsh-Bohmer, K.A. (2011). Factor structure of the National Alzheimer's Coordinating Centers Uniform Dataset Neuropsychological Battery: An evaluation of invariance between and within groups over time. Alzheimer Disease and Associated Disorders, 25(2), 128–137.CrossRefGoogle ScholarPubMed
Hu, L., Bentler, P. (1998). Fit indices in covariance structure analysis: Sensitivity to underparameterized model misspecifications. Psychological Methods, 4, 424–453.CrossRefGoogle Scholar
Johnson, D., Storandt, M., Morris, J., Langford, Z., Galvin, J. (2008). Cognitive profiles in dementia: Alzheimer disease vs healthy brain aging. Neurology, 71(22), 1783–1789.CrossRefGoogle ScholarPubMed
Lee, J.H. (2003). Genetic evidence for cognitive reserve: Variations in memory and related cognitive functions. Journal of Clinical and Experimental Neuropsychology, 25(5), 594–613.CrossRefGoogle ScholarPubMed
Locascio, J., Atri, A. (2011). An overview of longitudinal data analysis methods for neurological research. Dementia and Geriatric Cognitive Disorders Extra, 1, 330–357.CrossRefGoogle ScholarPubMed
Mack, W.J., Freed, D.M., Williams, B.W., Henderson, V.W. (1992). Boston Naming Test: Shortened versions for use in Alzheimer's disease. Journal of Gerontology, 47(3), P154–P158.CrossRefGoogle ScholarPubMed
Morris, J.C. (1993). The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology, 43(11), 2412–2414.CrossRefGoogle ScholarPubMed
Morris, J.C., Heyman, A., Mohs, R.C., Hughes, J.P., van Belle, G., Fillenbaum, G., Clark, C. (1989). The Consortium to Establish a Registry for Alzheimer's Disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer's disease. Neurology, 39(9), 1159–1165.Google Scholar
Morris, J.C., Weintraub, S., Chui, H.C., Cummings, J., Decarli, C., Ferris, S., Kukull, W.A. (2006). The Uniform Data Set (UDS): Clinical and cognitive variables and descriptive data from Alzheimer Disease Centers. Alzheimer Disease and Associated Disorders, 20(4), 210–216.CrossRefGoogle ScholarPubMed
Muthén, B. (1998). The development of heavy drinking and alcohol related problems from ages 18 to 37 in a U.S. National Sample. Los Angeles: Graduate School of Education and Information Studies.Google Scholar
Muthén, L.K., Muthén, B.O. (1998–2010). Mplus user's guide: Sixth Edition. Los Angeles, CA: Muthén & Muthén.Google Scholar
Muthén, B., Khoo, S.-T., Francis, D. (1998). Multi-stage analysis of sequential developmental processes to study reading progress: New methodological developments using general growth mixture modeling (CSE Technical Report No. 489). Los Angeles: UCLA Graduate School of Education and Information Studies.Google Scholar
Muthén, B.O. (1989). Latent variable modeling in heterogeneous populations. Meetings of Psychometric Society (1989, Los Angeles, California and Leuven, Belgium). Psychometrika, 54(4), 557–585.CrossRefGoogle Scholar
Patterson, K.E., Graham, N., Hodges, J.R. (1994). Reading in dementia of the Alzheimer type: A preserved ability? Neuropsychology, 8(3), 395–412.CrossRefGoogle Scholar
Persson, C., Wallin, A., Levander, S., Minthon, L. (2009). Changes in cognitive domains during three years in patients with Alzheimer's disease treated with donepezil. BMC Neurology, 9(1), 7.CrossRefGoogle ScholarPubMed
Rami, L., Solé-Padullés, C., Fortea, J., Bosch, B., Lladó, A., Antonell, A., Molinuevo, J.L. (2012). Applying the new research diagnostic criteria: MRI findings and neuropsychological correlations of prodromal AD. International Journal of Geriatric Psychiatry, 27, 127–134.CrossRefGoogle ScholarPubMed
Reitan, R.M., Wolfson, D. (1985). The Halstead-Reitan Neuropsychological Test Battery (2nd ed.). Tucson, AZ: Neuropsychology Press.Google Scholar
Rentz, D.M., Huh, T.J., Faust, R.R., Budson, A.E., Scinto, L.F., Sperling, R.A., Daffner, K.R. (2004). Use of IQ-adjusted norms to predict progressive cognitive decline in highly intelligent older individuals. Neuropsychology, 18, 38–49.CrossRefGoogle ScholarPubMed
Rentz, D.M., Locascio, J.J., Becker, J.A., Moran, E.K., Eng, E., Buckner, R.L., Johnson, K.A. (2010). Cognition, reserve, and amyloid deposition in normal aging. Annals of Neurology, 67(3), 353–364.CrossRefGoogle ScholarPubMed
Roe, C.M., Mintun, M.A., Ghoshal, N., Williams, M.M., Grant, E.A., Marcus, D.S., Morris, J.C. (2010). Alzheimer disease identification using amyloid imaging and reserve variables: Proof of concept. Neurology, 75(1), 42–48.CrossRefGoogle ScholarPubMed
Salthouse, T., Atkinson, T., Berish, D. (2003). Executive functioning as a potential mediator of age-related cognitive decline in normal adults. Journal of Experimental Psychology. General, 132(4), 566–594.CrossRefGoogle ScholarPubMed
Satorra, A., Bentler, P. (2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66(4), 507–514.CrossRefGoogle Scholar
Satz, P., Cole, M.A., Hardy, D.J., Rassovsky, Y. (2011). Brain and cognitive reserve: Mediator(s) and construct validity, a critique. Journal of Clinical and Experimental Neuropsychology, 33(1), 121–130.CrossRefGoogle Scholar
Shirk, S., Mitchell, M., Shaughnessy, L., Sherman, J., Locascio, J., Weintraub, S., Atri, A. (2011). A web-based normative calculator for the uniform data set (UDS) neuropsychological test battery. Alzheimers Research & Therapy, 3(6), 32.CrossRefGoogle ScholarPubMed
Siedlecki, K., Stern, Y., Reuben, A., Sacco, R., Elkind, M., Wright, C. (2009). Construct validity of cognitive reserve in a multiethnic cohort: The Northern Manhattan Study. Journal of the International Neuropsychological Society, 15(04), 558–569.CrossRefGoogle Scholar
Stern, Y. (2009). Cognitive reserve. Neuropsychologia, 47, 2015–2028.CrossRefGoogle ScholarPubMed
Stern, Y., Gurland, B., Tatemichi, T.K., Tang, M.X., Wilder, D., Mayeux, R. (1994). Influence of education and occupation on the incidence of Alzheimer's disease. Journal of the American Medical Association, 271(13), 1004–1010.CrossRefGoogle ScholarPubMed
Stern, Y., Zarahn, E., Habeck, C., Holtzer, R., Rakitin, B., Kumar, A., Brown, T. (2008). A common neural network for cognitive reserve in verbal and object working memory in young but not old. Cerebral Cortex, 18(4), 959.CrossRefGoogle Scholar
Tinklenberg, J., Brooks, J.O., Tanke, E.D., Khalid, K., Poulsen, S.L., Kraemer, H.C., Yesavage, J.A. (1990). Factor analysis and preliminary validation of the Mini-Mental State Examination from a longitudinal perspective. International Psychogeriatrics, 2(02), 123–134.CrossRefGoogle ScholarPubMed
Wechsler, D. (1987). Wechsler adult intelligence scale-revised. San Antonio: Psychological Corporation.Google Scholar
Wechsler, D.A. (1987). Wechsler memory scale - revised. San Antonio: Psychological Corporation.Google Scholar
Weintraub, S., Salmon, D., Mercaldo, N., Ferris, S., Graff-Radford, N.R., Chui, H., Morris, J.C. (2009). The Alzheimer's Disease Centers’ Uniform Data Set (UDS): The neuropsychologic test battery. Alzheimer Disease and Associated Disorders, 23(2), 91–101.CrossRefGoogle ScholarPubMed
Wilson, R., Scherr, P., Schneider, J., Tang, Y., Bennett, D. (2007). Relation of cognitive activity to risk of developing Alzheimer disease. Neurology, 69(20), 1911–1920.CrossRefGoogle ScholarPubMed
Zec, R., Landreth, E., Vicari, S., Belman, J., Feldman, E., Andrise, A., Kumar, V. (1992). Alzheimer Disease Assessment Scale: A subtest analysis. Alzheimer Disease and Associated Disorders, 6(3), 164–181.CrossRefGoogle Scholar