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Effect of Formal Education on Vascular Cognitive Impairment after Stroke: A Meta-analysis and Study in Young-Stroke Patients

Published online by Cambridge University Press:  09 January 2017

Roy P.C. Kessels ,
Willem Sake Eikelboom ,
Pauline Schaapsmeerders ,
Noortje A.M. Maaijwee ,
Renate M. Arntz ,
Ewoud J. van Dijk  and
Frank-Erik de Leeuw
Roy P.C. Kessels*
Affiliation:
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands Department of Medical Psychology, Radboud University Medical Center, Nijmegen, the Netherlands Vincent van Gogh Institute for Psychiatry, Venray, the Netherlands
Willem Sake Eikelboom
Affiliation:
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
Pauline Schaapsmeerders
Affiliation:
Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands
Noortje A.M. Maaijwee
Affiliation:
Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands
Renate M. Arntz
Affiliation:
Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands
Ewoud J. van Dijk
Affiliation:
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands
Frank-Erik de Leeuw
Affiliation:
Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands
*
Correspondence and reprint requests to: R.P.C. Kessels, Radboud University, Donders Institute for Brain, Cognition and Behaviour, Montessorilaan 3, 6525 HR Nijmegen, The Netherlands. E-mail: [email protected]
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Abstract

Objectives: The extent of vascular cognitive impairment (VCI) after stroke varies greatly across individuals, even when the same amount of brain damage is present. Education level is a potentially protective factor explaining these differences, but results on its effects on VCI are inconclusive. Methods: First, we performed a meta-analysis on formal education and VCI, identifying 21 studies (N=7770). Second, we examined the effect of formal education on VCI in young-stroke patients who were cognitively assessed on average 11.0 (SD=8.2) years post-stroke (the FUTURE study cohort). The total sample consisted of 277 young-stroke patients with a mean age at follow-up 50.9 (SD=10.3). Age and education-adjusted expected scores were computed using 146 matched stroke-free controls. Results: The meta-analysis showed an overall effect size (z') of 0.25 (95% confidence interval [0.18–0.31]), indicating that formal education level had a small to medium effect on VCI. Analyses of the FUTURE data showed that the effect of education on post-stroke executive dysfunction was mediated by age (β age −0.015; p<.05). Below-average performance in the attention domain was more frequent for low-education patients (χ2(2)=9.8; p<.05). Conclusions: While education level was found to be related to post-stroke VCI in previous research, the effects were small. Further analysis in a large stroke cohort showed that these education effects were fully mediated by age, even in relatively young stroke patients. Education level in and of itself does not appear to be a valid indicator of cognitive reserve. Multi-indicator methods may be more valid, but have not been studied in relation to VCI. (JINS, 2017, 23, 223–238)

Keywords

Cognitive reserveIschemic strokeCerebrovascular diseaseEducationAgingReviewQuantitative meta-analysisYoung stroke
Type
Critical Reviews
Information
Journal of the International Neuropsychological Society , Volume 23 , Issue 3 , March 2017 , pp. 223 - 238
Copyright
Copyright © The International Neuropsychological Society 2017 

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References

REFERENCES

Studies labeled with * were included in the meta-analysis.Google Scholar
* Alladi, S., Bak, T.H., Mekala, S., Rajan, A., Chaudhuri, J.R., Mioshi, E., & Kaul, S. (2016). Impact of bilingualism on cognitive outcome after stroke. Stroke, 47, 258–261. doi: 10.1161/STROKEAHA.115.010418 Google Scholar
* Akinyemi, R.O., Allan, L., Owolabi, M.O., Akinyemi, J.O., Ogbole, G., Ajani, A., & Kalaria, R.N. (2014). Profile and determinants of vascular cognitive impairment in African stroke survivors: The CogFAST Nigeria Study. Journal of the Neurological Sciences, 346, 241–249. doi: 10.1016/j.jns.2014.08.042 Google Scholar
Barulli, D., & Stern, Y. (2013). Efficiency, capacity, compensation, maintenance, plasticity: Emerging concepts in cognitive reserve. Trends in Cognitive Sciences, 17, 502–509. doi: 10.1016/j.tics.2013.08.012 Google Scholar
Beasley, T.M., & Schumacker, R.E. (1995). Multiple regression approach to analyzing contingency tables: Post hoc and planned comparison procedures. The Journal of Experimental Education, 64(1), 79–93. doi: 10.1080/00220973.1995.9943797 Google Scholar
* Blanco-Rojas, L., Arboix, A., Canovas, D., Grau-Olivares, M., Morera, J.C., & Parra, O. (2013). Cognitive profile in patients with a first-ever lacunar infarct with and without silent lacunes: A comparative study. BMC Neurology, 13, 203. doi: 10.1186/1471-2377-13-203 Google Scholar
Bondi, M.W., Edmonds, E.C., Jak, A.J., Clark, L.R., Delano-Wood, L., McDonald, C.R., & Salmon, D.P. (2014). Neuropsychological criteria for mild cognitive impairment improves diagnostic precision, biomarker associations, and progression rates. Journal of Alzheimer’s Disease, 42, 275–289. doi: 10.3233/JAD-140276 Google Scholar
Brott, T., Adams, H.P. Jr., Olinger, C.P., Marler, J.R., Barsan, W.G., Biller, J., & Walker, M. (1989). Measurements of acute cerebral infarction: A clinical examination scale. Stroke, 20, 864–870. doi: 10.1167/01.STR.20.7.864 Google Scholar
* Chaudhari, T.S., Verma, R., Garg, R., Singh, M., Malhotra, H., & Sharma, P. (2014). Clinico- radiological predictors of vascular cognitive impairment (VCI) in patients with stroke: A prospective observational study. Journal of the Neurological Science, 340, 150–158. doi: 10.1016/j.jns.2014.03.018 0022-510X CrossRefGoogle ScholarPubMed
Clark-Carter, D. (2010). Quantitative psychological research: A student’s handbook (3rd ed.). Hove, UK: Psychology Press.Google Scholar
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159. doi: 10.1037//0033-2909.112.1.155 Google Scholar
De Groot, J.C., de Leeuw, F.E., Oudkerk, M., van Gijn, J., Hofman, A., Jolles, J., & Breteler, M.M.B. (2000). Cerebral white matter lesions and cognitive function: The Rotterdam Scan Study. Annals of Neurology, 47, 145–151. doi: 10.1002/1531-8249(200002)47:2<145::AID-ANA3>3.0.CO;2-P Google Scholar
Demeyere, N., Riddoch, M.J., Slavkova, E.D., Jones, K., Reckless, I., Mathieson, P., & Humphreys, G.W. (2016). Domain-specific versus generalized cognitive screening in acute stroke. Journal of Neurology, 263, 306–315. doi: 10.1007/s00415-015-7964-4 Google Scholar
Desmond, D.W. (2004). The neuropsychology of vascular cognitive impairment: Is there a specific cognitive deficit? Journal of the Neurological Sciences, 226, 3–7. doi: 10.1016/j.jns.2004.09.002 Google Scholar
Duits, A., & Kessels, R. (2014). Schatten van het premorbide functioneren. pp. 173–186. In M. Hendriks, R. Kessels, M. Gorissen, B. Schmand & A. Duits (Eds.), Neuropsychologische diagnostiek: De klinische praktijk [Neuropsychological assessment: Clinical practice] Amsterdam: Boom.Google Scholar
Fisher, R.A. (1915). Frequency distribution of the values of the correlation coefficient in samples from an indefinitely large population. Biometrika, 10, 507–521. doi: 10.2307/2331838 Google Scholar
Folstein, M., 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, 189–198.Google Scholar
Goldin, C. (1998). America’s graduation from high school: The evolution and spread of secondary schooling in the twentieth century. Journal of Economic History, 58, 345–374.Google Scholar
Gorelick, P.B., Scuteri, A., Black, S.E., Decarli, C., Greenberg, S.M., & Iadecola, C., . . . American Heart Association Stroke Council, Council on Epidemiology and Prevention, Council on Cardiovascular Nursing, Council on Cardiovascular Radiology and Intervention, and Council on Cardiovascular Surgery and Anesthesia. (2011). Vascular contributions to cognitive impairment and dementia: A statement for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 42, 2672–2713. doi: 10.1161/STR.0b013e3182299496 Google Scholar
Harrison, S.L., Sajjad, A., Bramer, W.M., Ikram, M.A., Tiemeier, H., & Stephan, B.C.. Exploring strategies to operationalize cognitive reserve: A systematic review of reviews. Journal of Clinical and Experimental Neuropsychology, 37, 253–264. doi: 10.1080/13803395.2014.1002759 Google Scholar
Hedges, L.V., & Olkin, I. (1985). Statistical methods for meta-analysis. Orlando, FL: Academic Press.Google Scholar
Hindle, J.V., Martyr, A., & Clare, L. (2014). Cognitive reserve in Parkinson’s disease: A systematic review and meta-analysis. Parkinsonism and Related Disorders, 20, 1–7.CrossRefGoogle ScholarPubMed
Hochstenbach, J.B., Den Otter, R.D., & Mulder, T.W. (2003). Cognitive recovery after stroke: A 2-year follow-up. Archives of Physical Medicine and Rehabilitation, 84, 1499–1504. doi: 10.1016/S0003-9993(03)00370-8 Google Scholar
Hochstenbach, J., Mulder, T., Van Limbeek, J., Donders, R., & Schoonderwaldt, H. (1998). Cognitive decline following stroke: A comprehensive study of cognitive decline following stroke. Journal of Clinical and Experimental Neuropsychology, 20, 503–517. doi: 10.1076/jcen.20.4.503.1471 Google Scholar
* Horstmann, S., Rizos, T., Rauch, G., Arden, C., & Veltkamp, R. (2014). Feasibility of the Montreal Cognitive Assessment in acute stroke patients. European Journal of Neurology, 21, 1387–1393. doi: 10.1111/ene.12505 Google Scholar
Houx, P.J., Jolles, J., & Vreeling, F.W. (1993). Stroop interference: Aging effects assessed with the Stroop Color-Word Test. Experimental Aging Research, 19, 209–224.CrossRefGoogle ScholarPubMed
* Huang, K.L., Chang, T.Y., Chang, C.H., Liu, H.L., Chang, Y.J., Liu, C.H., & Ho, M.Y. (2013). Relationships between ophthalmic artery flow direction and cognitive performance in patients with unilateral carotid artery stenosis. Journal of the Neurological Sciences, 336, 184–190. doi: 10.1016/j.jns.2013.10.037 Google Scholar
* Jacova, C., Pearce, L.A., Costello, R., McClure, L.A., Holliday, S.L., Hart, R.G., & Benavente, O.R. (2012). Cognitive impairment in lacunar strokes: The SPS3 trial. Annals of Neurology, 72, 351–362. doi: 10.1002/ana.23733 Google Scholar
* Jacova, C., Pearce, L.A., Roldan, A.M., Arauz, A., Tapia, J., Costello, R., & Benavente, O.R. (2015). Cognitive performance following lacunar stroke in Spanish-speaking patients: Results from the SPS3 trial. International Journal of Stroke, 10, 519–528. doi: 10.1111/ijs.12511 Google Scholar
* Jacquin, A., Binquet, C., Rouaud, O., Graule-Petot, A., Daubail, B., Osseby, G.V., & Béjot, Y. (2014). Post-stroke cognitive impairment: High prevalence and determining factors in a cohort of mild stroke. Journal of Alzheimer’s Disease, 40, 1029–1038. doi: 10.3233/JAD-131580 Google Scholar
Jokinen, H., Melkas, S., Ylikoski, R., Pohjasvaara, T., Kaste, M., Erkinjuntti, T., & Hietanen, M. (2015). Post-stroke cognitive impairment is common even after successful clinical recovery. European Journal of Neurology, 22, 1288–1294. doi: 10.1111/ene.12743 Google Scholar
Jones, R.N., Manly, J., Glymour, M.M., Rentz, D.M., Jefferson, A.L., & Stern, Y. (2011). Conceptual and measurement challenges in research on cognitive reserve. Journal of the International Neuropsychological Society, 17, 593–601. doi: 10.1017/S1355617710001748 Google Scholar
Khandaker, G.M., Barnett, J.H., White, I.R., & Jones, P.B. (2011). A quantitative meta-analysis of population-based studies of premorbid intelligence and schizophrenia. Schizophrenia Research, 132, 220–227. doi: 10.1016/j.schres.2011.06.017 CrossRefGoogle ScholarPubMed
Knoflach, M., Matosevic, B., Rücker, M., Furtner, M., Mair, A., Wille, G., & Willeit, J. (2012). Functional recovery after ischemic stroke–a matter of age: Data from the Austrian Stroke Unit Registry. Neurology, 78, 279–285. doi: 10.1212/WNL.0b013e31824367ab Google Scholar
León, I., García-García, J., & Roldán-Tapia, L. (2014). Estimating cognitive reserve in healthy adults using the cognitive reserve scale. PLOS One, 9(7), e102632. doi: 10.1371/journal.pone.0102632 Google Scholar
Lezak, M.D., Howieson, D.B., Bigler, E.D., & Tranel, D. (2012). Neuropsychological assessment (5th ed.). New York: Oxford University Press.Google Scholar
Lipsey, M.W., & Wilson, D.B. (2000). Practical meta-analysis. Thousand Oaks, CA: Sage.Google Scholar
MacKinnon, D.P., Lockwood, C.M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39, 99–128. doi: 10.1207/s15327906mbr3901_4 Google Scholar
Mahoney, F.I., & Barthel, D.W. (1965). Functional evaluation: The Barthel Index. Maryland State Medical Journal, 14, 61–65.Google Scholar
Mahurin, R.K., & Cooke, N. (1996). Verbal Series Attention Test: Clinical utility in the assessment of dementia. Clinical Neuropsychologist, 10, 43–53.Google Scholar
Martins Da Silva, A., Cavaco, S., Moreira, I., Bettencourt, A., Santos, E., Pinto, C., & Montalban, X. (2015). Cognitive reserve in multiple sclerosis: Protective effects of education. Multiple Sclerosis, 21, 1312–1321. doi: 10.1177/1352458515581874 CrossRefGoogle ScholarPubMed
Mathias, J.L., & Wheaton, P. (2015). Contribution of brain or biological reserve and cognitive or neural reserve to outcome after TBI: A meta-analysis (prior to 2015). Neuroscience and Biobehavioral Reviews, 55, 573–593. doi: 10.1016/j.neubiorev.2015.06.001 Google Scholar
McLaren, M.E., Szymkowicz, S.M., Kirton, J.W., & Dotson, V.M. (2015). Impact of education on memory deficits in subclinical depression. Archives of Clinical Neuropsychology, 30, 387–393. doi: 10.1093/arclin/acv038 Google Scholar
Meng, X., & D’Arcy, C. (2012). Education and dementia in the context of the cognitive reserve hypothesis: A systematic review with meta-analyses and qualitative analyses. PLOS One, 7(6), e38268. doi: 10.1371/journal.pone.0038268 Google Scholar
Mirza, S.S., Portegies, M.L., Wolters, F.J., Hofman, A., Koudstaal, P.J., Tiemeier, H., & Ikram, M.A. (2016). Higher education is associated with a lower risk of dementia after a stroke or TIA: The Rotterdam Study. Neuroepidemiolog, 46, 120–127. doi: 10.1159/000443649 Google Scholar
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D.G., The PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(6), e1000097. doi: 10.1371/journal.pmed1000097 Google Scholar
Moller, J.T., Cluitmans, P., Rasmussen, L.S., Houx, P., Rasmussen, H., Canet, J., & Gravenstein, J.S. (1998). Long-term postoperative cognitive dysfunction in the elderly ISPOCD1 study. Lancet, 351, 857–861. doi: 10.1016/S0140-6736(97)07382-0 Google Scholar
Nasreddine, Z.S., Phillips, N.A., Bédirian, V., Charbonneau, S., Whitehead, V., Collin, I., & Chertkow, H. (2005). The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society, 53, 695–699. doi: 10.1111/j.1532-5415.2005.53221.x Google Scholar
Ng, T.P., Feng, L., Lim, W.S., Chong, M.S., Lee, T.S., Yap, K.B., & Yap, P. (2015). Montreal Cognitive Assessment for screening mild cognitive impairment: Variations in test performance and scores by education in Singapore. Dementia and Geriatric Cognitive Disorders, 39, 176–185. doi: 10.1159/000368827 Google Scholar
Nucci, M., Mapelli, D., & Mondini, S. (2012). Cognitive Reserve Index questionnaire (CRIq): A new instrument for measuring cognitive reserve. Aging: Clinical and Experimental Research, 24, 218–226. doi: 10.3275/7800 Google Scholar
Nunnari, D., Bramanti, P., & Marino, S. (2014). Cognitive reserve in stroke and traumatic brain injury patients. Neurological Sciences, 35, 1513–1518. doi: 10.1007/s10072-014-1897-z Google Scholar
Nys, G.M., Van Zandvoort, M.J.E., De Kort, P.L., Jansen, B.P.W., Kappelle, L.J., & De Haan, E.H.F. (2005). Restrictions of the Mini-Mental State Examination in acute stroke. Archives of Clinical Neuropsychology, 20, 623–629. doi: 10.1212/01.WNL.0000152984.28420.5A Google Scholar
* Ojala-Oksala, J., Jonkinen, H., Kopsi, V., Lehtonen, K., Luukkonen, L., Paukkunen, A., & . . Oksala, N. (2012). Educational history is an independent predictor of cognitive deficits and long-term survival in postacute patients with mild to moderate ischemic stroke. Stroke, 43, 2931–2935. doi: 10.1161/STROKEAHA.112.667618 CrossRefGoogle ScholarPubMed
Opdebeeck, C., Martyr, A., & Clare, L. (2016). Cognitive reserve and cognitive function in healthy older people: A meta-analysis. Aging, Neuropsychology, and Cognition, 23, 40–60. doi: 10.1080/13825585.2015.1041450 Google Scholar
Osterrieth, P. (1944). Le test de copie d’une figure complexe: Contribution a l’étude de la perception et de la mémoire. Archives de Psychologie, 30, 206–353.Google Scholar
Park, D.C., & Reuter-Lorenz, P. (2009). The adaptive brain: Aging and neurocognitive scaffolding. Annual Review of Psychology, 60, 173–179. doi: 10.1146/annurev.psych.59.103006.093656 Google Scholar
* Pasi, M., Salvadori, E., Poggesi, A., Inzitari, D., & Pantoni, L. (2013). Factors predicting the Montreal Cognitive Assessment (MoCA) applicability and performances in a stroke unit. Journal of Neurology, 260, 1518–1526. doi: 10.1007/s00415-012-6819-5 Google Scholar
Pinter, D., Enzinger, C., & Fazekas, F. (2015). Cerebral small vessel disease, cognitive reserve and cognitive dysfunction. Journal of Neurology. doi: 10.1007/s00415-015-7776-6 Google Scholar
Preacher, K.J., & Hayes, A.F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36, 717–731. doi: 10.3758/BF03206553 Google Scholar
* Qu, Y., Zhuo, L., Li, N., Hu, Y., Chen, W., Zhou, Y., & Zhan, S. (2015). Prevalence of post-stroke cognitive impairment in China: A community-based, cross-sectional study. PLoS One, 10, e0122864. doi: 10.1371/journal.pone.0122864 Google Scholar
* Rasquin, S.M.C., Verhey, F.R.J., Van Oostenbrugge, R.J., Lousberg, R., & Lodder, J. (2004). Demographic and CT scan features related to cognitive impairment in the first year after stroke. Journal of Neurology, Neurosurgery, and Psychiatry, 75, 1562–1567. doi: 10.1136/jnnp.2003.024190 Google Scholar
Rosenberg, M.S., Adams, D.C., & Gurevitch, J. (2000). MetaWin: Statistical software for meta-analyses (Version 2.0). Sunderland, MA: Sinauer Associates.Google Scholar
Rosenthal, R. (1991). Meta-analytic procedures for social research (rev. ed.). Newbury Park, CA: Sage.Google Scholar
Rutten-Jacobs, L.C., Maaijwee, N.A., Arntz, R.M., Van Alebeek, M.E., Schaapsmeerders, P., Schoonderwaldt, H.C., & De Leeuw, F.-E. (2011). Risk factors and prognosis of young stroke. The FUTURE study: A prospective cohort study. Study rationale and protocol. BMC Neurology, 11, 109. doi: 10.1186/1471-2377-11-109 Google Scholar
* Sachdev, P.S., Brodaty, H., Valenzuela, M.J., Lorentz, L., Psychol, M.C., & Koschera, A. (2004). Progression of cognitive impairment in stroke patients. Neurology, 63, 1618–1623. doi: 10.1212/01.WNL.0000142964.83484.DE Google Scholar
Schaapsmeerders, P., Maaijwee, N.A., Van Dijk, E.J., Rutten-Jacobs, L.C., Arntz, R.M., Schoonderwaldt, H.C., & De Leeuw, F.-E. (2013). Long-term cognitive impairment after first-ever ischemic stroke in young adults. Stroke, 44, 1621–1628. doi: 10.1161/STROKEAHA.111.000792 Google Scholar
Schooler, C., Mulatu, M.S., & Oates, G. (1999). The continuing effects of substantively complex work on the intellectual functioning of older workers. Psychology and Aging, 14, 483–506. doi: 10.1037/0882-7974.14.3.483 Google Scholar
Stern, Y., Albert, S., Tang, M.X., & Tsai, W.Y. (1999). Rate of memory decline in AD is related to education and occupation: Cognitive reserve? Neurology., 53, 1942–1957. doi: 10.1212/WNL.53.9.1942 Google Scholar
Stern, Y. (2002). What is cognitive reserve? Theory and research application of the reserve concept. Journal of the International Neuropsychological Society, 8, 448–460. doi: 10.1017/S1355617702813248 Google Scholar
Sternberg, S. (1969). Memory-scanning: Mental processes revealed by reaction time experiments. American Scientist, 57, 421–457.Google Scholar
* Tang, W.K., Chan, S.S., Chiu, H.F., Ungvari, G.S., Wong, K.S., Kwok, T.C., & Ahuja, A.T. (2006). Frequency and clinical determinants of poststroke cognitive impairment in nondemented stroke patients. Journal of Geriatric Psychiatry and Neurology, 19, 65–71. doi: 10.1177/0891988706286230 Google Scholar
* Tham, W., Auchus, A.P., Thong, M., Goh, M.L., Chang, H.M., Wong, M.C., & Chen, C.P.H. (2002). Progression of cognitive impairment after stroke: One year results from a longitudinal study of Singaporean stroke patients. Journal of the Neurological Sciences, 203, 49–52. doi: 10.1016/S0022-510X(02)00260-5 Google Scholar
* Tu, Q., Ding, B., Yang, X., Bai, S., Tu, J.,.L.,.X., & Tang, X. (2014). The current situation on vascular cognitive impairment after ischemic stroke in Changsha. Archives of Gerontology and Geriatrics, 58, 236–247. doi: 10.1016/j.archger.2013.09.006 Google Scholar
Varona, J.F., Bermejo, F., Guerra, J.M., & Molina, J.A. (2004). Long-term prognosis of ischemic stroke in young adults. Study of 272 cases. Journal of Neurology, 251, 1507–1514. doi: 10.1007/s00415-004-0583-0 Google Scholar
Van der Elst, W., Van Boxtel, M.P., Van Breukelen, G.J., & Jolles, J. (2005). Rey’s verbal learning test: Normative data for 1855 healthy participants aged 24-81 years and the influence of age, sex, education, and mode of presentation. Journal of the International Neuropsychological Society, 11, 290–302. doi: 10.1017/s1355617705050344 Google Scholar
Van der Elst, W., Van Boxtel, M.P., Van Breukelen, G.J., & Jolles, J. (2006). Normative data for the Animal, Profession and Letter M Naming verbal fluency tests for Dutch speaking participants and the effects of age, education, and sex. Journal of the International Neuropsychological Society, 12, 80–89. doi: 1 0.1017/s1355617706060115 Google Scholar
Van Hooren, S.A., Valentijn, A.M., Bosma, H., Ponds, R.W., Boxtel, M.P., & Jolles, J. (2007). Cognitive functioning in healthy older adults aged 64–81: A cohort study into the effects of age, sex, and education. Aging Neuropsychology and Cognition, 14, 40–54. doi: 10.1080/138255890969483 Google Scholar
Van Swieten, J.C., Koudstaal, P.J., Visser, M.C., Schouten, H.J., & van Gijn, J. (1988). Interobserver agreement for the assessment of handicap in stroke patients. Stroke, 19, 604–607. doi: 10.1161/01.STR.19.5.604 Google Scholar
Vercoulen, J.H., Swanink, C.M., Fennis, J.F., Galama, J.M., van der Meer, J.W., & Bleijenberg, G. (1994). Dimensional assessment of chronic fatigue syndrome. Journal of Psychosomatic Research, 38, 383–392. doi: 10.1016/0022-3999(94)90099-X Google Scholar
Verhage, F. (1964). Intelligentie en leeftijd: Onderzoek bij Nederlanders van twaalf tot zevenenzeventig jaar. Assen, The Netherlands: Van Gorcum.Google Scholar
Zahodne, L.B., Glymour, M.M., Sparks, C., Bontempo, D., Dixon, R.A., MacDonald, S.W., & Manly, J.J. (2011). Education does not slow cognitive decline with aging: 12-year evidence from the Victoria Longitudinal Study. Journal of the International Neuropsychological Society, 17, 1039–1046. doi: 10.1017/S1355617711001044 Google Scholar
* Zhang, Y., Zhang, Z., Yang, B., Li, Y., Zhang, Q., Qu, Q., & Zhang, B. (2012). Incidence and risk factors of cognitive impairment 3 months after first-ever stroke: A cross-sectional study of 5 geographic areas of China. Journal of Huazhong University of Science and Technology [Medical Sciences], 32, 906–911. doi: 10.1007/s11596-012-1056-9 Google Scholar
* Zhang, M., Chen, M., Whang, Q., Yun, W., Zhang, Z., Yin, Q., & Zhu, W. (2013). Relationship between cerebral microbleeds and cognitive function in lacunar infarct. Journal of International Medical Research, 41, 347–355. doi: 10.1177/0300060513476448 Google Scholar
* Zhou, D.H., Wang, J.Y., Li, J., Deng, J., Gao, C., & Chen, M.R.E. (2005). Frequency and risk factors of vascular cognitive impairment three months after ischemic stroke in china: The Chongqing stroke study. Neuroepidemiology, 24, 87–95. doi: 10.1159/000081055 Google Scholar
Zhou, S., Zhu, J., Zhang, N., Wang, B., Li, T., Lv, X., & Wang, H. (2014). The influence of education on Chinese version of Montreal Cognitive Assessment in detecting amnesic mild cognitive impairment among older people in a Beijing rural community. Scientific World Journal, 2014, 689456. doi: 10.1155/2014/689456 Google Scholar
* Zieren, N., Dueringa, M., Petersb, N., Reyesc, S., Jouventc, E., Hervéc, D., & Dichgans, M. (2013). Education modifies the relation of vascular pathology to cognitive function: Cognitive reserve in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy. Neurobiology of Aging, 34, 400–407. doi: 10.1016/j.neurobiolaging.2012.04.01 Google Scholar
Zigmond, A.S., & Snaith, R.P. (1983). The Hospital Anxiety and Depression Scale. Acta Psychiatrica Scandinavica, 67, 361–370.Google Scholar