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Age of Onset as a Moderator of Cognitive Decline in Pediatric-Onset Multiple Sclerosis

Published online by Cambridge University Press:  17 July 2014

Banafsheh Hosseini
Affiliation:
Department of Psychology, York University, Toronto, Canada
David B. Flora
Affiliation:
Department of Psychology, York University, Toronto, Canada
Brenda L. Banwell
Affiliation:
Neurosciences and Mental Health Program, The Hospital for Sick Children, Toronto, Canada Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
Christine Till*
Affiliation:
Department of Psychology, York University, Toronto, Canada Neurosciences and Mental Health Program, The Hospital for Sick Children, Toronto, Canada
*
Correspondence and reprint requests to: Christine Till, Department of Psychology, York University, 4700 Keele Street, Toronto, Ontario, M3J 1P3, E-mail: ctill@yorku.ca

Abstract

Cognitive impairment is often reported in pediatric-onset multiple sclerosis (MS). Using serial cognitive data from 35 individuals with pediatric-onset MS, this study examined how age at disease-onset and proxies of cognitive reserve may impact cognitive maturation over the course of childhood and adolescence. Neuropsychological evaluations were conducted at baseline and up to four more assessments. Of the 35 participants, 7 completed only one assessment, 5 completed two assessments, 13 completed three assessments, 10 completed four or more assessments. Growth curve modeling was used to assess longitudinal trajectories on the Trail Making Test-Part B (TMT-B) and the Symbol Digit Modalities (SDMT; oral version) and to examine how age at disease onset, baseline Full Scale IQ, and social status may moderate rate of change on these measures. Mean number of evaluations completed per patient was 2.8. Younger age at disease onset was associated with a greater likelihood of cognitive decline on both the TMT-B (p=.001) and SDMT (p=.005). Baseline IQ and parental social status did not moderate any of the cognitive trajectories. Findings suggest that younger age at disease-onset increases the vulnerability for disrupted performance on measures of information processing, visual scanning, perceptual/motor speed, and working memory. Proxies of cognitive reserve did not protect against the progression of decline on these measures. Young patients with MS should be advised to seek follow-up cognitive evaluation to assess cognitive maturation and to screen for the potential late emergence of cognitive deficits. (JINS, 2014, 20, 1–9)

Type
Research Articles
Copyright
Copyright © The International Neuropsychological Society 2014 

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References

Amato, M. P., Goretti, B., Ghezzi, A., Lori, S., Zipoli, V., Portaccio, E., Trojano, M. (2008). Cognitive and psychosocial features of childhood and juvenile MS. Neurology, 70(20), 18911897.CrossRefGoogle ScholarPubMed
Amato, M. P., Goretti, B., Ghezzi, A., Lori, S., Zipoli, V., Moiola, L., Trojano, M. (2010). Cognitive and psychosocial features in childhood and juvenile MS Two-year follow-up. Neurology, 75(13), 11341140.CrossRefGoogle ScholarPubMed
Anderson, V., Jacobs, R., Spencer-Smith, M., Coleman, L., Anderson, P., Williams, J., Leventer, R. (2009). Journal of Pediatric Psychology, 35(7), 716727.CrossRefGoogle Scholar
Archibald, C. J., & Fisk, J. D. (2000). Information processing efficiency in patients with multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 22(5), 686701.CrossRefGoogle ScholarPubMed
Banwell, B. L., & Anderson, P. E. (2005). The cognitive burden of multiple sclerosis in children. Neurology, 64(5), 891894.CrossRefGoogle ScholarPubMed
Barr, W. B. (2003). Neuropsychological testing of high school athletes: Preliminary norms and test-retest indices. Archives of Clinical Neuropsychology, 18, 91101.Google ScholarPubMed
Barratt, W. (2006). The Barratt simplified measure of social status (BSMSS): Measuring SES. Unpublished manuscript. Indiana State University. Retrieved from http://wbarratt. indstate. edu/socialclass/Barratt_Simplifed_Measure_of_Social_Status. pdf.Google Scholar
Benedict, R. H. B., Duquin, J. A., Jurgensen, S., Rudick, R. A., Feitcher, J., Munschauer, F. E., Weinstock-Guttman, B. (2008). Repeated assessment of neuropsychological deficits in multiple sclerosis using the Symbol Digit Modalities Test and the MS Neuropsychological Screening Questionnaire. Multiple Sclerosis, 14(7), 940946.CrossRefGoogle ScholarPubMed
Benedict, R. H., Morrow, S. A., Cookfair, D., & Schretlen, D. J. (2010). Cognitive reserve moderates decline in information processing speed in multiple sclerosis patients. Journal of the International Neuropsychological Society, 16(5), 829835.CrossRefGoogle ScholarPubMed
Boiko, A., Vorobeychik, G., Paty, D., Devonshire, V., & Sadovnik, D. (2002). Early onset multiple sclerosis: A longitudinal study. Neurology, 59(7), 10061010.CrossRefGoogle ScholarPubMed
DeLuca, J., Chelune, G. J., Tulsky, D. S., Lengenfelder, J., & Chiaravalloti, N. D. (2004). Is speed of processing or working memory the primary information processing deficit in multiple sclerosis? Journal of Clinical and Experimental Neuropsychology, 26(4), 550562.CrossRefGoogle ScholarPubMed
Demaree, H. A., DeLuca, J., Gaudino, E. A., & Diamond, B. J. (1999). Speed of information processing as a key deficit in multiple sclerosis: Implications for rehabilitation. Journal of Neurology, Neurosurgery & Psychiatry, 67(5), 661663.CrossRefGoogle ScholarPubMed
Dennis, M., Yeates, K. O., Taylor, H. G., & Fletcher, J. M. (2006). Brain reserve capacity, cognitive reserve capacity, and age-based functional plasticity after congenital and acquired brain injury in children. In Y. Stern (Ed.), Cognitive reserve: Theory and applications (pp. 5383). New York: Psychology.Google Scholar
Drew, M. A., Starkey, N. J., & Isler, R. B. (2009). Examining the link between information processing speed and executive functioning in multiple sclerosis. Archives of Clinical Neuropsychology, 24(1), 4758.CrossRefGoogle ScholarPubMed
Farmer, J. E., Kanne, S. M., Haut, J. S., Williams, J., Johnstone, B., & Kirk, K. (2002). Memory functioning following traumatic brain injury in children with premorbid learning problems. Developmental Neuropsychology, 22(2), 455469.CrossRefGoogle ScholarPubMed
Fay, T. B., Yeates, K. O., Taylor, H. G., Bangert, B., Dietrich, A., Nuss, K. E., Wright, M. (2010). Cognitive reserve as a moderator of postconcussive symptoms in children with complicated and uncomplicated mild traumatic brain injury. Journal of the International Neuropsychological Society, 16(1), 94105.CrossRefGoogle ScholarPubMed
Ghezzi, A., Deplano, V., Faroni, J., Grasso, M. G., Liguori, M., Marrosu, G., Zaffaroni, M. (1997). Multiple sclerosis in childhood: clinical features of 149 cases. Multiple sclerosis, 3(1), 4346.CrossRefGoogle ScholarPubMed
Hart, S. A., Petrill, S. A., Deater Keckard, K., & Thompson, L. A. (2007). SES and CHAOS as environmental mediators of cognitive ability: A longitudinal genetic analysis. Intelligence, 35, 233242.CrossRefGoogle ScholarPubMed
Holland, A. A., Graves, D., Greenberg, B. M., & Harder, L. L. (2014). Fatigue, emotional functioning, and executive dysfunction in pediatric multiple sclerosis. Child Neuropsychology, 20(1), 7185.CrossRefGoogle ScholarPubMed
Kesler, S. R., Tanaka, H., & Koovakkattu, K. (2010). Cognitive reserve and brain volumes in pediatric acute lymphoblastic leukemia. Brain Imaging and Behavior, 4, 256269.CrossRefGoogle ScholarPubMed
Krupp, L. B., Banwell, B., & Tenembaum, S. (2007). Consensus definitions proposed for pediatric multiple sclerosis and related disorders. Neurology, 68(16 Suppl. 2), S7S12.CrossRefGoogle ScholarPubMed
Leavitt, V. M., Lengenfelder, J., Moore, N. B., Chiaravalloti, N. D., & DeLuca, J. (2011). The relative contributions of processing speed and cognitive load to working memory accuracy in multiple sclerosis. Journal of Clinical and Experimental Neuropsychology, 33(5), 580586.CrossRefGoogle ScholarPubMed
MacAllister, W. S., Christodoulou, C., Milazzo, M., & Krupp, L. B. (2007). Longitudinal neuropsychological assessment in pediatric multiple sclerosis. Developmental Neuropsychology, 32(2), 625644.CrossRefGoogle ScholarPubMed
MacAllister, W. S., Belman, A L., & Milazzo, M. (2005). Cognitive functioning in children and adolescents with multiple sclerosis. Neurology, 64(8), 14221425.CrossRefGoogle ScholarPubMed
Marin, S. E., Banwell, B. B., & Till, C. (2012). Cognitive trajectories in 4 patients with pediatric-onset multiple sclerosis serial evaluation over a decade. Journal of Child Neurology, 28(12), 15771586.CrossRefGoogle Scholar
Marrie, R. A., Horwitz, R., Cutter, G., Tyry, T., Campagnolo, D., & Vollmer, T. (2008). Comorbidity, socioeconomic status and multiple sclerosis. Multiple Sclerosis, 14, 10911098.CrossRefGoogle ScholarPubMed
Portaccio, E., Goretti, B., Lori, S., Zipoli, V., Centorrino, S., Ghezzi, A., Amato, M. P., for the Multiple Sclerosis Study Group of the Italian Neurological Society. (2009). The brief neuropsychological battery for children: a screening tool for cognitive impairment in childhood and juvenile multiple sclerosis. Multiple Sclerosis, 15, 620626.CrossRefGoogle ScholarPubMed
Reitan, R. M. (1959). A manual for the administrating and scoring of the Trail Making Test. Indianapolis: Indiana University Press.Google Scholar
Sánchez-Cubillo, I., Periáňez, J. A., Adrover-Roig, D., Rodríguez-Sánchez, J. M., Ríos-Lago, M., Tirapu, J., & Barceló, F. (2009). Construct validity of the Trail Making Test: Role of task-switching, working memory, inhibition/interference control, and visuomotor abilities. Journal of the International Neuropsychological Society, 15, 438450.CrossRefGoogle ScholarPubMed
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Cambridge: Oxford University Press.CrossRefGoogle Scholar
Smith, A. (Ed.). (2002). Symbol Digit Modalities Test (SDMT). Los Angeles: Western Psychological Services.Google Scholar
Statz, P. (1993). Brain reserve capacity on symptom onset after brain injury: A formulation and review of evidence for threshold theory. Neuropsychology, 7(3), 273295.CrossRefGoogle 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
Strauss, E., Sherman, E. M. S., & Spreen, O. (2006). A compendium of neuropsychological tests: Administration, norms, and commentary (3rd ed.). New York: Oxford University Press.Google Scholar
Sumowski, J. F., Chiaravalloti, N., Wylie, G. R., & DeLuca, J. (2009). Cognitive reserve moderates the negative effect of brain atrophy on cognitive efficiency in multiple sclerosis. Journal of International Neuropsychiatry, 15(2), 606612.Google ScholarPubMed
Sumowski, J. F., Wylie, G. R., DeLuca, J., & Chiaravalloti, N. (2010). Intellectual enrichment is linked to cerebral efficiency in multiple sclerosis: functional magnetic resonance imaging evidence for cognitive reserve. Brain, 133(2), 362374.CrossRefGoogle ScholarPubMed
Till, C., Ghassemi, R., Aubert-Broche, B., Kerbrat, A., Collins, D. L., Narayanan, S., Banwell, B. L. (2011). MRI correlates of cognitive impairment in childhood-onset multiple sclerosis. Neuropsychology, 25(3), 319332.CrossRefGoogle ScholarPubMed
Till, C., Ho, C., Dudani, A., Garcia-Lorenzo, D., Collins, D. L., & Banwell, B. L. (2012). Magnetic resonance imaging predictors of executive functioning in patients with pediatric-onset multiple sclerosis. Archives of Clinical Neuropsychology, 27(5), 459509.CrossRefGoogle ScholarPubMed
Till, C., Racine, N., Araujo, D., Narayanan, S., Collins, D. L., Aubert-Broche, B., Banwell, B. (2013). Changes in cognitive performance over a 1-year period in children and adolescents with multiple sclerosis. Neuropsychology, 27(2), 210219.CrossRefGoogle Scholar
Wechsler, D. (1997). Wechsler Adult Intelligence Scale–third edition: Administration and scoring manual. San Antonio, TX: Psychological Corporation.Google Scholar
Wechsler, D. (1991). WISC-III: Wechsler intelligence scale for children. San Antonio, TX: Psychological Corporation.Google Scholar
Weiss, L. G., Saklofske, D. H., Prifitera, A., Chen, H. Y. & Hildebrand, D. (1999). The calculation of the WISC-III General Ability Index using Canadian norms. Canadian Journal of School Psychology, 14(2), 110.CrossRefGoogle Scholar
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