Measurement invariance testing of longitudinal neuropsychiatric test scores distinguishes pathological from normative cognitive decline and highlights its potential in early detection research.
cognitive decline
composite
latent factors
measurement invariance
neuropsychiatric test battery
Journal
Journal of neuropsychology
ISSN: 1748-6653
Titre abrégé: J Neuropsychol
Pays: England
ID NLM: 101468753
Informations de publication
Date de publication:
06 2022
06 2022
Historique:
pubmed:
15
12
2021
medline:
14
6
2022
entrez:
14
12
2021
Statut:
ppublish
Résumé
Alzheimer's disease (AD) is a growing challenge worldwide, which is why the search for early-onset predictors must be focused as soon as possible. Longitudinal studies that investigate courses of neuropsychological and other variables screen for such predictors correlated to mild cognitive impairment (MCI). However, one often neglected issue in analyses of such studies is measurement invariance (MI), which is often assumed but not tested for. This study uses the absence of MI (non-MI) and latent factor scores instead of composite variables to assess properties of cognitive domains, compensation mechanisms, and their predictability to establish a method for a more comprehensive understanding of pathological cognitive decline. An exploratory factor analysis (EFA) and a set of increasingly restricted confirmatory factor analyses (CFAs) were conducted to find latent factors, compared them with the composite approach, and to test for longitudinal (partial-)MI in a neuropsychiatric test battery, consisting of 14 test variables. A total of 330 elderly (mean age: 73.78 ± 1.52 years at baseline) were analyzed two times (3 years apart). EFA revealed a four-factor model representing declarative memory, attention, working memory, and visual-spatial processing. Based on CFA, an accurate model was estimated across both measurement timepoints. Partial non-MI was found for parameters such as loadings, test- and latent factor intercepts as well as latent factor variances. The latent factor approach was preferable to the composite approach. The overall assessment of non-MI latent factors may pose a possible target for this field of research. Hence, the non-MI of variances indicated variables that are especially suited for the prediction of pathological cognitive decline, while non-MI of intercepts indicated general aging-related decline. As a result, the sole assessment of MI may help distinguish pathological from normative aging processes and additionally may reveal compensatory neuropsychological mechanisms.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
324-352Informations de copyright
© 2021 The Authors. Journal of Neuropsychology published by John Wiley & Sons Ltd on behalf of The British Psychological Society.
Références
Abbott, A. (2011). Dementia: a problem for our age. Nature, 475, S2-S4.
American Psychiatric Association. (2014). Diagnostisches und statistisches manual psychischer Störungen-DSM-5®. Göttingen, Germany: Hogrefe Verlag.
Arnáiz, E., & Almkvist, O. (2003). Neuropsychological features of mild cognitive impairment and preclinical Alzheimer's disease. Acta Neurologica Scandinavica. Supplementum, 179, 34-41.
Avila, J. F., Rentería, M. A., Witkiewitz, K., Verney, S. P., Vonk, J. M. J., & Manly, J. J. (2020). Measurement invariance of neuropsychological measures of cognitive aging across race/ethnicity by sex/gender groups. Neuropsychology, 34(1), 3-14. https://doi.org/10.1037/neu0000584.
Aschenbrenner, S., Tucha, O., & Lange, K. W. (2000). Regensburger Wortflüssigkeits-Test: RWT. Göttingen: Hogrefe, Verlag für Psychologie.
Bates, D., Maechler, M., Bolker, B., & Walker, S. (2014). Ime4: Linear Mixed-EffectsMoels Using Eigen and S4. In.
Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Beck Depression inventory-II. San Antonio, 78, 490-498.
Berndt, A. E., & Williams, P. C. (2013). Hierarchical regression and structural equation modeling: two useful analyses for life course research. Family and Community Health, 36, 4-18.
Bertola, L., Benseñor, I. M., Gross, A. L., Caramelli, P., Barreto, S. M., Moreno, A. B., … Suemoto, C. K. (2021). Longitudinal measurement invariance of neuropsychological tests in a diverse sample from the ELSA-Brasil study. Brazilian Journal of Psychiatry, 43, 254-261.
Bickel, H. (2001). Dementia in advanced age: estimating incidence and health care costs. Zeitschrift Fur Gerontologie Und Geriatrie, 34, 108-115.
Byrne, B. M., & Crombie, G. (2003). Modeling and testing change: an introduction to the latent growth curve model. Understanding Statistics, 2, 177-203.
Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9, 233-255.
Cooper, C., Sommerlad, A., Lyketsos, C. G., & Livingston, G. (2015). Modifiable predictors of dementia in mild cognitive impairment: a systematic review and meta-analysis. American Journal of Psychiatry, 172, 323-334.
Dowling, N. M., Hermann, B., La Rue, A., & Sager, M. A. (2010). Latent structure and factorial invariance of a neuropsychological test battery for the study of preclinical Alzheimer's disease. Neuropsychology, 24, 742-756.
Felt, J. M., Depaoli, S., & Tiemensma, J. (2017). Latent growth curve models for biomarkers of the stress response. Frontiers in Neuroscience, 11, 315.
Fimm, B., & Zimmermann, P. (2001). Testbatterie zur Aufmerksamkeitsprüfung (TAP). In: Version.
Gao, C., Shi, D., & Maydeu-Olivares, A. (2020). Estimating the maximum likelihood root mean square error of approximation (RMSEA) with non-normal data: a Monte-Carlo study. Structural Equation Modeling: A Multidisciplinary Journal, 27, 192-201.
Gao, S., Mokhtarian, P. L., & Johnston, R. A. (2008). Nonnormality of data in structural equation models. Transportation Research Record, 2082, 116-124.
Grimm, K. J., & Ram, N. (2009). Nonlinear growth models in Mplus and SAS. Structural Equation Modeling: A Multidisciplinary Journal, 16, 676-701.
Haberstumpf, S., Seidel, A., Lauer, M., Polak, T., Deckert, J., & Herrmann, M. J. (2020). Neuronal correlates of the visual-spatial processing measured with functional near-infrared spectroscopy in healthy elderly individuals. Neuropsychologia, 148, 107650.
Härting, C., Markowitsch, H., Neufeld, H., Calabrese, P., Deisinger, K., & Kessler, J. (2000). WMS-R Wechsler gedächtnistest-revidierte fassung. Bern, Switzerland: Hans Huber.
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, 128-137.
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. American Journal of Geriatric Psychiatry, 22, 1364-1374.
Helmstaedter, C., Lendt, M., & Lux, S. (2001). Verbaler Lern-und Merkfähigkeitstest: VLMT; Manual. Göttingen: Beltz-Test.
Hendrix, J. A., Finger, B., Weiner, M. W., Frisoni, G. B., Iwatsubo, T., Rowe, C. C., … Carrillo, M. C. (2015). The worldwide alzheimer's disease neuroimaging initiative: an update. Alzheimer's and Dementia: the Journal of the Alzheimer's Association, 11, 850-859.
Jahn, H. (2013). Memory loss in Alzheimer's disease. Dialogues in Clinical Neuroscience, 15, 445-454.
Katzorke, A., Zeller, J. B. M., Müller, L. D., Lauer, M., Polak, T., Deckert, J., & Herrmann, M. J. (2018). Decreased hemodynamic response in inferior frontotemporal regions in elderly with mild cognitive impairment. Psychiatry Res Neuroimaging, 274, 11-18.
Katzorke, A., Zeller, J. B. M., Müller, L. D., Lauer, M., Polak, T., Reif, A., … Herrmann, M. J. (2017). Reduced activity in the right inferior frontal gyrus in Elderly APOE-E4 carriers during a verbal fluency task. Frontiers in Human Neuroscience, 11, 46.
Kline, R. B. (2005). Principles and practice of structural equation modeling, Vol. 2. New York, NY: Guilford.
Koscik, R. L., Berman, S. E., Clark, L. R., Mueller, K. D., Okonkwo, O. C., Gleason, C. E., … Johnson, S. C. (2016). Intraindividual cognitive variability in middle age predicts cognitive impairment 8-10 years later: results from the wisconsin registry for Alzheimer’s prevention. Journal of the International Neuropsychological Society, 22, 1016-1025.
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2015). Package ‘lmertest’. R Package Version, 2, 734.
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. (2017). lmerTest package: tests in linear mixed effects models. Journal of Statistical Software, 82, 1-26.
Lai, K. (2018). Estimating standardized SEM parameters given nonnormal data and incorrect model: methods and comparison. Structural Equation Modeling: A Multidisciplinary Journal, 25, 600-620.
Lei, M., & Lomax, R. G. (2005). The effect of varying degrees of nonnormality in structural equation modeling. Structural Equation Modeling, 12, 1-27.
Ma, Y., Carlsson, C. M., Wahoske, M. L., Blazel, H. M., Chappell, R. J., Johnson, S. C., Asthana, S., & Gleason, C. E. (2021). Latent Factor Structure and Measurement Invariance of the NIH Toolbox Cognition Battery in an Alzheimer's Disease Research Sample. J Int Neuropsychol Soc, 27(5), 412-425. https://doi.org/10.1017/s1355617720000922
Makkar, S. R., Lipnicki, D. M., Crawford, J. D., Kochan, N. A., Castro-Costa, E., Lima-Costa, M. F., … Sachdev, P. (2020). APOE ε4 and the influence of sex, age, vascular risk factors, and ethnicity on cognitive decline. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 75, 1863-1873.
Mayeux, R. (2010). Early Alzheimer's disease. New England Journal of Medicine, 362, 2194-2201.
Meyers, J. E., & Meyers, K. R. (1996). Rey Complex Figure Test and Recognition Trial Supplemental Norms for Children and Adults. Psychological Assessment Resources.
Moreira, P. S., Santos, N., Castanho, T., Amorim, L., Portugal-Nunes, C., Sousa, N., & Costa, P. (2018). Longitudinal measurement invariance of memory performance and executive functioning in healthy aging. PLoS One, 13, e0204012.
Mitchell, M. B., Shaughnessy, L. W., Shirk, S. D., Yang, F. M., & Atri, A. (2012). Neuropsychological test performance and cognitive reserve in healthy aging and the Alzheimer's disease spectrum: a theoretically driven factor analysis. J Int Neuropsychol Soc, 18(6), 1071-1080. https://doi.org/10.1017/s1355617712000859
Mungas, D., Widaman, K. F., Reed, B. R., & Tomaszewski Farias, S. (2011). Measurement invariance of neuropsychological tests in diverse older persons. Neuropsychology, 25, 260-269.
National Institute of Mental Health. (2011). NIMH Research Domain Criteria (RDoC).
Nestor, P. J., Fryer, T. D., & Hodges, J. R. (2006). Declarative memory impairments in Alzheimer's disease and semantic dementia. NeuroImage, 30, 1010-1020.
Oort, F. J. (2005). Using structural equation modeling to detect response shifts and true change. Quality of Life Research, 14, 587-598.
Park, D. C., & Festini, S. B. (2017). Theories of memory and aging: a look at the past and a glimpse of the future. Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 72, 82-90.
Petersen, R. C. (2000). Mild cognitive impairment: transition between aging and Alzheimer's disease. Neurologia, 15, 93-101.
Polak, T., Herrmann, M. J., Müller, L. D., Zeller, J. B. M., Katzorke, A., Fischer, M., … Deckert, J. (2017). Near-infrared spectroscopy (NIRS) and vagus somatosensory evoked potentials (VSEP) in the early diagnosis of Alzheimer's disease: rationale, design, methods, and first baseline data of the Vogel study. Journal of Neural Transmission, 124, 1473-1488.
Prince, M., Bryce, R., Albanese, E., Wimo, A., Ribeiro, W., & Ferri, C. P. (2013). The global prevalence of dementia: a systematic review and metaanalysis. Alzheimer's and Dementia: the Journal of the Alzheimer's Association, 9, 63-75.e62.
R Core Team. (2016). Vienna: R Foundation for Statistical Computing.
Rahmadi, R., Groot, P., van Rijn, M. H., van den Brand, J. A., Heins, M., Knoop, H., & Heskes, T. (2018). Causality on longitudinal data: stable specification search in constrained structural equation modeling. Statistical Methods in Medical Research, 27, 3814-3834.
Riedel, W. J., & Blokland, A. (2015). Declarative memory. Handbook of Experimental Pharmacology, 228, 215-236.
Rosseel, Y. (2012). Lavaan: An R package for structural equation modeling and more. Version 0.5-12 (BETA). Journal of Statistical Software, 48, 1-36.
Rowe, J. B. (2010). Connectivity analysis is essential to understand neurological disorders. Frontiers in Systems Neuroscience, 4, 144.
Sayegh, P., & Knight, B. G. (2014). Functional assessment and neuropsychiatric inventory questionnaires: measurement invariance across hispanics and non-Hispanic whites. Gerontologist, 54(3), 375-386. https://doi.org/10.1093/geront/gnt026
Schmitt, N., Golubovich, J., & Leong, F. T. (2011). Impact of measurement invariance on construct correlations, mean differences, and relations with external correlates: an illustrative example using Big Five and RIASEC measures. Assessment, 18, 412-427.
Schumacker, R. E., & Lomax, R. G. (2004). A beginner's guide to structural equation modeling (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
Statistisches Bundesamt (2018). Bildungsstand der Bevölkerung - Ergebnisse des Mikrozensus 2017. Retrieved from https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bildung-Forschung-Kultur/Bildungsstand/Publikationen/Downloads-Bildungsstand/bildungsstand-bevoelkerung-5210002177004.pdf;jsessionid=356E153EF23D5F8006CEC57A4B85AF16.internet8731?__blob=publicationFile
Tabachnick, B. G., & Fidell, L. S. (1996). Using multivariate statistics. Northridge, CA: Harper Collins.
Van de Schoot, R., Lugtig, P., & Hox, J. (2012). A checklist for testing measurement invariance. European Journal of Developmental Psychology, 9, 486-492.
Tuokko, H. A., Chou, P. H., Bowden, S. C., Simard, M., Ska, B., & Crossley, M. (2009). Partial measurement equivalence of French and English versions of the Canadian Study of Health and Aging neuropsychological battery. J Int Neuropsychol Soc, 15(3), 416-425. https://doi.org/10.1017/s1355617709090602
Wicherts, J. M. (2016). The importance of measurement invariance in neurocognitive ability testing. The Clinical Neuropsychologist, 30, 1006-1016.
Willett, J. B., & Sayer, A. G. (1994). Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychological Bulletin, 116, 363.
Williams, B. D., Chandola, T., & Pendleton, N. (2018). An application of Bayesian measurement invariance to modelling cognition over time in the English longitudinal study of ageing. International Journal of Methods in Psychiatric Research, 27, e1749.
Winblad, B., Wimo, A., Engedal, K., Soininen, H., Verhey, F., Waldemar, G., … Schindler, R. (2006). 3-year study of donepezil therapy in Alzheimer's disease: effects of early and continuous therapy. Dementia and Geriatric Cognitive Disorders, 21, 353-363.
World Health Organization [WHO] (2016). World health statistics 2016: monitoring health for the SDGs sustainable development goals. Geneva, Switzerland: World Health Organization.
World Health Organization [WHO] (2019). International Classification of Diseases (ICD). Version updated in 2019. World Health Organization. Retrieved from https://icd.who.int/browse10/2019/en
World Medical Association (2013). World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA, 310, 2191-2194.
Xu, W., Tan, L., Wang, H. F., Jiang, T., Tan, M. S., Tan, L., … Yu, J. T. (2015). Meta-analysis of modifiable risk factors for Alzheimer's disease. Journal of Neurology, Neurosurgery and Psychiatry, 86, 1299-1306.
Yilmaz, F. N. (2019). Comparison of different estimation methods used in confirmatory factor analyses in non-normal data: a Monte Carlo study. International Online Journal of Educational Sciences, 11, 131-140.
Zeller, J. B. M., Katzorke, A., Müller, L. D., Breunig, J., Haeussinger, F. B., Deckert, J., … Herrmann, M. J. (2019). Reduced spontaneous low frequency oscillations as measured with functional near-infrared spectroscopy in mild cognitive impairment. Brain Imaging and Behavior, 13, 283-292.