Hostname: page-component-848d4c4894-cjp7w Total loading time: 0 Render date: 2024-06-24T06:12:13.401Z Has data issue: false hasContentIssue false

Cognitive reserve moderates the negative effect of brain atrophy on cognitive efficiency in multiple sclerosis

Published online by Cambridge University Press:  01 July 2009

JAMES F. SUMOWSKI*
Affiliation:
Neuropsychology & Neuro Science Laboratory, Kessler Foundation Research Center, West Orange, New Jersey Department of Physical Medicine and Rehabilitation, New Jersey Medical School, UMDNJ, Newark, New Jersey
NANCY CHIARAVALLOTI
Affiliation:
Neuropsychology & Neuro Science Laboratory, Kessler Foundation Research Center, West Orange, New Jersey Department of Physical Medicine and Rehabilitation, New Jersey Medical School, UMDNJ, Newark, New Jersey
GLENN WYLIE
Affiliation:
Neuropsychology & Neuro Science Laboratory, Kessler Foundation Research Center, West Orange, New Jersey Department of Physical Medicine and Rehabilitation, New Jersey Medical School, UMDNJ, Newark, New Jersey
JOHN DELUCA
Affiliation:
Neuropsychology & Neuro Science Laboratory, Kessler Foundation Research Center, West Orange, New Jersey Department of Physical Medicine and Rehabilitation, New Jersey Medical School, UMDNJ, Newark, New Jersey Department of Neurology and Neurosciences, New Jersey Medical School, UMDNJ, Newark, New Jersey
*
*Correspondence and reprint requests to: James F. Sumowski, Neuropsychology & Neuroscience Laboratory, Kessler Foundation Research Center, 300 Executive Drive, Suite 10, West Orange, New Jersey 07052. E-mail: jsumowski@kesslerfoundation.net

Abstract

According to the cognitive reserve hypothesis, neuropsychological expression of brain disease is attenuated among persons with higher education or premorbid intelligence. The current research examined cognitive reserve in multiple sclerosis (MS) by investigating whether the negative effect of brain atrophy on information processing (IP) efficiency is moderated by premorbid intelligence. Thirty-eight persons with clinically definite MS completed a vocabulary-based estimate of premorbid intelligence (Wechsler Vocabulary) and a composite measure of IP efficiency (Symbol Digit Modalities Test and Paced Auditory Serial Addition Task). Brain atrophy was estimated from measurements of third ventricle width using high-resolution anatomical brain magnetic resonance imaging (magnetization-prepared rapid gradient echo). In a hierarchical regression analysis controlling for age and depressive symptomatology, brain atrophy predicted worse IP efficiency (R2 = .23, p = .003) and cognitive reserve predicted better IP efficiency (R2 = .13, p = .013), but these effects were moderated by an Atrophy × Cognitive Reserve interaction (R2 = .15, p = .004). The negative effect of brain atrophy on IP efficiency was attenuated at higher levels of reserve, such that MS subjects with higher reserve were better able to withstand MS neuropathology without suffering cognitive impairment. Results help explain the incomplete and inconsistent relationship between brain atrophy and IP efficiency in previous research. (JINS, 2009, 15, 606–612.)

Type
Research Articles
Copyright
Copyright © The International Neuropsychological Society 2009

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

REFERENCES

Alexander, G.E., Furey, M.L., Grady, C.L., Pietrini, P., Brady, D.R., Mentis, M.J., & Schapiro, M.B. (1997). Association of premorbid intellectual function with cerebral metabolism in Alzheimer’s disease: Implications for the cognitive reserve hypothesis. The American Journal of Psychiatry, 154(2), 165172.Google ScholarPubMed
Beck, A.T., Steer, R.A., & Brown, G.K. (1996). Beck Depression Inventory-II. San Antonio, TX: The Psychological Corporation.Google Scholar
Benedict, R.H., Bruce, J.M., Dwyer, M.G., Abdelrahman, N., Hussein, S., Weinstock-Guttman, B., Garg, N., Munschauer, F., & Zivadinov, R. (2006a). Neocortical atrophy, third ventricular width, and cognitive dysfunction in multiple sclerosis. Archives of Neurology, 63(9), 13011306.CrossRefGoogle ScholarPubMed
Benedict, R.H., Cookfair, D, Gavett, R, Gunther, M., Munschauer, F., Garg, N., & Weinstock-Guttman, B. (2006b). Validity of the minimal assessment of cognitive function in multiple sclerosis. Journal of the International Neuropsychological Society, 12(4), 549558.CrossRefGoogle ScholarPubMed
Benedict, R.H., Fischer, J.S., Archibald, C.J., Arnett, P.A., Beatty, W.W., Bobholz, J., Chelune, G.J., Fisk, J.D., Langdon, D.W., Caruso, L., Foley, R., LaRocca, N.G., Vowels, L., Weinstein, A., DeLuca, J., Rao, S.M., & Munschauer, F. (2002). Minimal neuropsychological assessment of MS patients: A consensus approach. The Clinical Neuropsychologist, 16(3), 381397.CrossRefGoogle ScholarPubMed
Benedict, R.H., Weinstock-Guttman, B., Fishman, I., Sharma, J., Tjoa, C.W., & Bakshi, R. (2004). Prediction of neuropsychological impairment in multiple sclerosis: Comparison of conventional magnetic resonance imaging measures of atrophy and lesion burden. Archives of Neurology, 61(2), 226230.CrossRefGoogle ScholarPubMed
Bennett, D.A., Wilson, R.S., Schneider, J.A., Evans, D.A., Mendes de Leon, C.F., Arnold, S.E., Barnes, L.L., & Bienias, J.L. (2003). Education modifies the relation of AD pathology to level of cognitive function in older persons. Neurology, 60, 19091915.CrossRefGoogle ScholarPubMed
Bermel, R. & Bakshi, R. (2006). The measurement and clinical relevance of brain atrophy in multiple sclerosis. Lancet Neurology, 5(2), 158170.CrossRefGoogle ScholarPubMed
Bland, J.M. & Altman, D.G. (1986). Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 1, 307310.CrossRefGoogle ScholarPubMed
Bleecker, M.L., Ford, D.P., Celio, M.A., Vaughan, C.G., & Lindgren, K.N. (2007). Impact of cognitive reserve on the relationship of lead exposure and neurobehavioral performance. Neurology, 69(5), 470476.CrossRefGoogle ScholarPubMed
Bonnet, M.C., Deloire, M.S., Salort, E., Dousset, V., Petry, K.G., & Brochet, B. (2006). Evidence of cognitive compensation associated with educational level in early relapsing remitting multiple sclerosis. Journal of the Neurological Sciences, 251(1–2), 2328.CrossRefGoogle ScholarPubMed
Chiaravalloti, N.D. & DeLuca, J. (2008). Cognitive impairment in multiple sclerosis. Lancet Neurology, 7(12), 11391151.CrossRefGoogle ScholarPubMed
Christodoulou, C., Krupp, L.B., Liang, Z., Huang, W., Melville, P.Roque, C., Scherl, W.F., Morgan, T., MacAllister, W.S., Li, L., Tudorica, L.A., Li, X., Roche, P., & Peyster, R. (2003). Cognitive performance and MR markers of cerebral injury in cognitively impaired MS patients. Neurology, 60(11), 17931798.CrossRefGoogle Scholar
Crystal, H., Dickson, D., Fuld, P., Masur, D., Scott, R., Mehler, M., Masdeu, J., Kawas, C., Aronson, M., & Wolfson, L. (1988). Clinico-pathologic studies in dementia: Nondemented subjects with pathologically confirmed Alzheimer’s disease. Neurology, 38(11), 16821687.CrossRefGoogle ScholarPubMed
Cutter, G.R., Baier, M.L., Rudick, R.A., Cookfair, D.L., Fischer, J.S., Petkau, J., Syndulko, K., Weinshenker, B.G., Antel, J.P., Confavreux, C., Ellison, G.W., Lublin, F., Miller, A.E., Rao, S.M., Reingold, S., Thompson, A., & Willoughby, E. (1999). Development of a multiple sclerosis functional composite as a clinical trial outcome measure. Brain, 122(5), 871882.CrossRefGoogle ScholarPubMed
Dufouil, C., Alperovitch, A., & Tzourio, C. (2003). Influence of education on the relationship between white matter lesions and cognition. Neurology, 60, 831836.CrossRefGoogle ScholarPubMed
Elkins, J.S., Longstreth, W.T., Monolio, T.A., Newman, A.B., Bhadelia, R.A., & Johnson, S.C. (2006). Education and the cognitive decline associated with MRI-defined brain infarct. Neurology, 67, 435440.CrossRefGoogle ScholarPubMed
Gyldensted, C. (1977). Measurements of the normal ventricular system and hemispheric sulci of 100 adults with computed tomography. Neuroradiology, 14, 183192.CrossRefGoogle ScholarPubMed
Hauser, S.L., Dawson, D.M., Lehrich, J.R., Beal, M.F., Kevy, S.V., Propper, R.D., Mills, J.A., & Weiner, H.L. (1983). Intensive immunosuppression in progressive multiple sclerosis. A randomized, three-arm study of high-dose intravenous cyclophosphamide, plasma exchange, and ACTH. The New England Journal of Medicine, 308, 173180.CrossRefGoogle ScholarPubMed
Hillary, F.G., Chiaravalloti, N.D., Ricker, J.H., Steffener, J., Bly, B.M., Lange, G., Liu, W.C., Kalnin, A.J., & DeLuca, J. (2003). An investigation of working memory rehearsal in multiple sclerosis using fMRI. Journal of Clinical and Experimental Neuropsychology, 25(7), 965978.CrossRefGoogle ScholarPubMed
Houtchens, M.K., Benedict, R.H., Killiany, R., Sharma, J., Jaisani, Z., Singh, B., Weinstock Guttman, B., Guttmann, C.R., & Bakshi, R. (2007).Thalamic atrophy and cognition in multiple sclerosis. Neurology, 69(12), 12131223.CrossRefGoogle ScholarPubMed
Katzman, R., Terry, R., DeTeresa, R., Brown, T., Davies, P., Fuld, P., Renbing, X., & Peck, A. (1988). Clinical, pathological, and neurochemical changes in dementia: A subgroup with preserved mental status and numerous neocortical plaques. Annals of Neurology, 23(2), 138144.CrossRefGoogle ScholarPubMed
Lezak, M.D. (2004). Neuropsychological assessment (4th ed.). New York: Oxford University Press.Google Scholar
McDonald, W.I., Compston, A., Edan, G., Goodkin, D., Hartung, H.P., Lublin, F.D., McFarland, H.F., Paty, D.W., Polman, C.H., Reingold, S.C., Sandberg-Wollheim, M., Sibley, W., Thompson, A., van den Noort, S., Weinshenker, B.Y., & Wolinsky, J.S. (2001). Recommended diagnostic criteria for multiple sclerosis: Guidelines from the international panel on the diagnosis of multiple sclerosis. Annals of Neurology, 50(1), 121127.CrossRefGoogle ScholarPubMed
Morgen, K., Sammer, G., Courtney, S.M., Wolters, T., Melchior, H., Blecker, C.R., Oschmann, P., Kaps, M., & Vaitl, D. (2007). Distinct mechanisms of altered brain activation in patients with multiple sclerosis. Neuroimage, 37(3), 937946.CrossRefGoogle ScholarPubMed
Neubauer, A.C., Fink, A., & Schrausser, D.G. (2002). Intelligence and neural efficiency: The influence of task content and sex on the brain-IQ relationship. Intelligence, 30(6), 515536.CrossRefGoogle Scholar
O’Brien, A.R., Chiaravalloti, N., Goverover, Y., & DeLuca, J. (2008). Evidence-based cognitive rehabilitation for persons with multiple sclerosis: A review of the literature. Archives of Physical Medicine and Rehabilitation, 89(4), 761769.CrossRefGoogle ScholarPubMed
Peyser, J.M., Rao, S.M., LaRocca, N.G., & Kaplan, E. (1990). Guidelines for neuropsychological research in multiple sclerosis. Archives of Neurology, 47(1), 9497.CrossRefGoogle ScholarPubMed
Price, J.L. & Morris, J.C. (1999). Tangles and plaques in nondemented aging and “preclinical” Alzheimer’s disease. Annals of Neurology, 45(3), 358368.3.0.CO;2-X>CrossRefGoogle ScholarPubMed
Rao, S.M. & the Cognitive Function Study Group of the National Multiple Sclerosis Society. (1990). A manual for the Brief Repeatable Battery of Neuropsychological Tests in multiple sclerosis. Milwaukee, WI: Medical College of Wisconsin.Google Scholar
Rao, S.M., Leo, G.J., Bernardin, L., & Unverzagt, F. (1991). Cognitive dysfunction in multiple sclerosis. I. Frequency, patterns, and prediction. Neurology, 41(5), 685691.CrossRefGoogle ScholarPubMed
Roe, C.M., Mintun, M.A., D’Angelo, G., Xiong, C., Grant, E.A., & Morris, J.C. (2008). Alzheimer disease and cognitive reserve: Variation of education effect with carbon 11 labeled Pittsburgh compound B uptake. Archives of Neurology, 65(11), 14671471.CrossRefGoogle ScholarPubMed
Sanfilipo, M.P., Benedict, R.H., Weinstock-Guttman, B., & Bakshi, R. (2006). Gray and white matter brain atrophy and neuropsychological impairment in multiple sclerosis. Neurology, 66(5), 685692.CrossRefGoogle ScholarPubMed
Scarmeas, N., Levy, G., Tang, M.X., Manly, J., & Stern, Y. (2001). Influence of leisure activity on the incidence of Alzheimer’s disease. Neurology, 57(12), 22362242.CrossRefGoogle ScholarPubMed
Siegert, R.J. & Abernethy, D.A. (2005). Depression in multiple sclerosis: A review. Journal of Neurology, Neurosurgery, and Psychiatry, 76, 469475.CrossRefGoogle ScholarPubMed
Smith, A. (1982). Symbol Digit Modalities Test manual. Los Angeles, CA: Western Psychological Services.Google Scholar
Stern, Y. (2002). What is cognitive reserve? Theory and research application of the reserve concept. Journal of the International Neuropsychological Society, 8(3), 448460.CrossRefGoogle ScholarPubMed
Stern, Y. (2006). Cognitive reserve and Alzheimer disease. Alzheimer Disease and Associated Disorders, 20(3 Suppl 2), S69S74.CrossRefGoogle ScholarPubMed
Stern, Y., Alexander, G.E., Prohovnik, I., & Mayeux, R. (1992). Inverse relationship between education and parietotemporal perfusion deficit in Alzheimer’s disease. Annals of Neurology, 32(3), 371375.CrossRefGoogle ScholarPubMed
Stern, Y., Habeck, C., Moeller, J., Scarmeas, N., Anderson, K.E., Hilton, H.J., Flynn, J., Sackeim, H., & van Heertum, R. (2005). Brain networks associated with cognitive reserve in healthy young and old adults. Cerebral Cortex, 15(4), 394402.CrossRefGoogle ScholarPubMed
Stern, Y., Tang, M.X., Denaro, J., & Mayeux, R. (1995). Increased risk of mortality in Alzheimer’s disease patients with more advanced educational and occupational attainment. Annals of Neurology, 37(5), 590595.CrossRefGoogle ScholarPubMed
Sumowski, J.F., Chiaravalloti, N., & DeLuca, J. (in press). Cognitive reserve protects against cognitive dysfunction in multiple sclerosis. Journal of Clinical and Experimental Neuropsychology.Google Scholar
Sumowski, J.F., Chiaravalloti, N., Wylie, G., Genova, H., & DeLuca, J. (2008). Functional neuroimaging of cognitive reserve in multiple sclerosis. Boston, MA: American Psychological Association.CrossRefGoogle Scholar
Sweet, L.H., Rao, S.M., Primeau, M., Durgerian, S., & Cohen, R.A. (2006). Functional magnetic resonance imaging response to increased verbal working memory demands among patients with multiple sclerosis. Human Brain Mapping, 27(1), 2836.CrossRefGoogle ScholarPubMed
Verghese, J., Lipton, R.B., Katz, M.J., Hall, C.B., Derby, C.A., Kuslansky, G., Ambrose, A.F., Sliwinski, M., & Buschke, H. (2003). Leisure activities and the risk of dementia in the elderly. The New England Journal of Medicine, 348(25), 25082516.CrossRefGoogle ScholarPubMed
Wechsler, D. (1999). Wechsler Abbreviated Scale of Intelligence. San Antonio, TX: The Psychological Corporation.Google Scholar
Wilson, R.S., Mendes De Leon, C.F., Barnes, L.L., Schneider, J.A., Bienias, J.L., Evans, D.A., & Bennett, D.A. (2002). Participation in cognitively stimulating activities and risk of incident Alzheimer disease. The Journal of the American Medical Association, 287(6), 742748.CrossRefGoogle ScholarPubMed
Wishart, H.A., Saykin, A.J., McDonald, B.C., Mamourian, A.C., Flashman, L.A., Schuschu, K.R., Ryan, K.A., Fadul, C.E., & Kasper, L.H. (2004). Brain activation patterns associated with working memory in relapsing-remitting MS. Neurology, 62(2), 234238.CrossRefGoogle ScholarPubMed