Abstract
We sought to develop and evaluate a composite memory score from the neuropsychological battery used in the Alzheimer’s Disease (AD) Neuroimaging Initiative (ADNI). We used modern psychometric approaches to analyze longitudinal Rey Auditory Verbal Learning Test (RAVLT, 2 versions), AD Assessment Schedule - Cognition (ADAS-Cog, 3 versions), Mini-Mental State Examination (MMSE), and Logical Memory data to develop ADNI-Mem, a composite memory score. We compared RAVLT and ADAS-Cog versions, and compared ADNI-Mem to RAVLT recall sum scores, four ADAS-Cog-derived scores, the MMSE, and the Clinical Dementia Rating Sum of Boxes. We evaluated rates of decline in normal cognition, mild cognitive impairment (MCI), and AD, ability to predict conversion from MCI to AD, strength of association with selected imaging parameters, and ability to differentiate rates of decline between participants with and without AD cerebrospinal fluid (CSF) signatures. The second version of the RAVLT was harder than the first. The ADAS-Cog versions were of similar difficulty. ADNI-Mem was slightly better at detecting change than total RAVLT recall scores. It was as good as or better than all of the other scores at predicting conversion from MCI to AD. It was associated with all our selected imaging parameters for people with MCI and AD. Participants with MCI with an AD CSF signature had somewhat more rapid decline than did those without. This paper illustrates appropriate methods for addressing the different versions of word lists, and demonstrates the additional power to be gleaned with a psychometrically sound composite memory score.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Crane, P. K., Narasimhalu, K., Gibbons, L. E., Mungas, D. M., Haneuse, S., Larson, E. B., et al. (2008). Item response theory facilitated cocalibrating cognitive tests and reduced bias in estimated rates of decline. Journal of Clinical Epidemiology, 61(10), 1018–1027 e1019.
De Meyer, G., Shapiro, F., Vanderstichele, H., Vanmechelen, E., Engelborghs, S., De Deyn, P. P., et al. (2010). Diagnosis-independent Alzheimer disease biomarker signature in cognitively normal elderly people. Archives of Neurology, 67(8), 949–956. doi:10.1001/archneurol.2010.179.
Fjell, A. M., Walhovd, K. B., Amlien, I., Bjornerud, A., Reinvang, I., Gjerstad, L., et al. (2008). Morphometric changes in the episodic memory network and tau pathologic features correlate with memory performance in patients with mild cognitive impairment. [Research Support, Non-U.S. Gov’t]. AJNR. American Journal of Neuroradiology, 29(6), 1183–1189. doi:10.3174/ajnr.A1059.
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198.
Jack, C. R., Jr., Bernstein, M. A., Borowski, B. J., Gunter, J. L., Fox, N. C., Thompson, P. M., et al. (2010a). Update on the magnetic resonance imaging core of the Alzheimer’s disease neuroimaging initiative. Alzheimers Dement, 6(3), 212–220. doi:10.1016/j.jalz.2010.03.004.
Jack, C. R., Jr., Knopman, D. S., Jagust, W. J., Shaw, L. M., Aisen, P. S., Weiner, M. W., et al. (2010b). Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurology, 9(1), 119–128.
Jack, C. R., Jr., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., et al. (2008). The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging, 27(4), 685-691. doi:10.1002/jmri.21049.
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 and Associated Disorders, 25(1), 73–84.
McDonald, R. P. (1999). Test theory: a unified treatment. Mahwah: Erlbaum.
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984). Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology, 34(7), 939–944.
Millsap, R. E. (2011). Statistical approaches to measurement invariance: Routledge.
Mohs, R. C., Knopman, D., Petersen, R. C., Ferris, S. H., Ernesto, C., Grundman, M., et al. (1997). Development of cognitive instruments for use in clinical trials of antidementia drugs: additions to the Alzheimer’s Disease Assessment Scale that broaden its scope. The Alzheimer’s Disease Cooperative Study. Alzheimer Disease and Associated Disorders, 11(Suppl 2), S13–21.
Morris, J. C. (1993). The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology, 43(11), 2412–2414.
Mungas, D., & Reed, B. R. (2000). Application of item response theory for development of a global functioning measure of dementia with linear measurement properties. Statistics in Medicine, 19(11–12), 1631–1644.
Murphy, E. A., Holland, D., Donohue, M., McEvoy, L. K., Hagler, D. J., Jr., Dale, A. M., et al. (2010). Six-month atrophy in MTL structures is associated with subsequent memory decline in elderly controls. [Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov’t]. NeuroImage, 53(4), 1310–1317. doi:10.1016/j.neuroimage.2010.07.016.
Muthén, L., & Muthén, B. (2006). Mplus users guide. Version 4.1 ed. Los Angeles: Muthen and Muthen.
Reeve, B. B., Hays, R. D., Bjorner, J. B., Cook, K. F., Crane, P. K., Teresi, J. A., et al. (2007). Psychometric evaluation and calibration of health-related quality of life item banks: plans for the Patient-Reported Outcomes Measurement Information System (PROMIS). Med Care, 45(5 Suppl 1), S22–31.
Reise, S. P., Morizot, J., & Hays, R. D. (2007). The role of the bifactor model in resolving dimensionality issues in health outcomes measures. Quality of life research: an international journal of quality of life aspects of treatment, care and rehabilitation, 16 Suppl 1, 19–31.
Reise, S. P., Widaman, K. F., & Pugh, R. H. (1993). Confirmatory factor analysis and item response theory: two approaches for exploring measurement invariance. Psychological Bulletin, 114(3), 552–66.
Rey, A. (1964). L’examen clinique en psychologie. Paris: Presses Universitaires de France.
Van Petten, C., Plante, E., Davidson, P. S., Kuo, T. Y., Bajuscak, L., & Glisky, E. L. (2004). Memory and executive function in older adults: relationships with temporal and prefrontal gray matter volumes and white matter hyperintensities. [Clinical Trial Research Support, U.S. Gov’t, P.H.S.]. Neuropsychologia, 42(10), 1313–1335. doi:10.1016/j.neuropsychologia.2004.02.009.
Walhovd, K. B., Fjell, A. M., Amlien, I., Grambaite, R., Stenset, V., Bjornerud, A., et al. (2009). Multimodal imaging in mild cognitive impairment: metabolism, morphometry and diffusion of the temporal-parietal memory network. NeuroImage, 45(1), 215–223. doi:10.1016/j.neuroimage.2008.10.053.
Wechsler, D. (1987). WMS-R: Wechsler Memory Scale—Revised manual. NY: Psychological Corporation / HBJ.
Wouters, H., van Gool, W. A., Schmand, B., & Lindeboom, R. (2008). Revising the ADAS-cog for a more accurate assessment of cognitive impairment. Alzheimer Disease and Associated Disorders, 22(3), 236–244.
Yonelinas, A. P., Widaman, K., Mungas, D., Reed, B., Weiner, M. W., & Chui, H. C. (2007). Memory in the aging brain: doubly dissociating the contribution of the hippocampus and entorhinal cortex. Hippocampus, 17(11), 1134–1140. doi:10.1002/hipo.20341.
Acknowledgment
Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott; Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Amorfix Life Sciences Ltd.; AstraZeneca; Bayer HealthCare; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory of Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation. Data management and the specific analyses reported here wer\e supported by NIH grant R01 AG029672 (Paul Crane, PI), P50 AG05136 (Murray Raskind, PI), and R13 AG030995 (Dan Mungas, PI).
Author information
Authors and Affiliations
Consortia
Corresponding author
Additional information
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.ucla.edu/research/active-investigators/
Electronic supplementary material
Below is the link to the electronic supplementary material.
Appendix Fig. 1
Scatter plot of baseline memory single factor and bi-factor scores, stratified by diagnostic group (PDF 32 kb)
Appendix Table 1
Recoding of scores with more than 10 categories for ADNI-Mem. (PDF 46 kb)
Appendix Table 2
Factor loadings for the two versions of the RAVLT (PDF 37 kb)
Appendix Table 3
Factor loadings for the three versions of the ADAS-Cog (PDF 38 kb)
Rights and permissions
About this article
Cite this article
Crane, P.K., Carle, A., Gibbons, L.E. et al. Development and assessment of a composite score for memory in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Brain Imaging and Behavior 6, 502–516 (2012). https://doi.org/10.1007/s11682-012-9186-z
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11682-012-9186-z