Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-23T20:31:49.991Z Has data issue: false hasContentIssue false

Four-Year Longitudinal Performance of a Population-Based Sample of Healthy Children on a Neuropsychological Battery: The NIH MRI Study of Normal Brain Development

Published online by Cambridge University Press:  13 December 2011

Deborah P. Waber*
Affiliation:
Division of Psychology, Department of Psychiatry, Children's Hospital Boston and Harvard Medical School, Boston, Massachusetts
Peter W. Forbes
Affiliation:
Clinical Research Program, Children's Hospital Boston, Boston, Massachusetts
C. Robert Almli
Affiliation:
Developmental Neuropsychobiology Laboratory, Programs in Occupational Therapy & Neuroscience, Departments of Neurology & Psychology, Washington University School of Medicine in St. Louis, St. Louis, Missouri
Emily A. Blood
Affiliation:
Clinical Research Program, Children's Hospital Boston, Boston, Massachusetts Department of Pediatrics, Children's Hospital Boston and Harvard Medical School, Boston, Massachusetts
*
Correspondence and reprint requests to: Deborah P. Waber, Department of Psychiatry, Children's Hospital Boston, 300 Longwood Avenue, Boston, Massachusetts 02115. E-mail: [email protected]

Abstract

The National Institutes of Health (NIH) Magnetic Resonance Imaging (MRI) Study of Normal Brain Development is a landmark study in which structural and metabolic brain development and behavior are followed longitudinally from birth to young adulthood in a population-based sample of healthy children. Cross-sectional findings from the neuropsychological test battery have been previously described (Waber et al., 2007). The present report details 4-year longitudinal neuropsychological outcomes for those children who were aged 6 to 18 years at baseline (N = 383), of whom 219 (57.2%) completed all 3 visits. Primary observations were (1) individual children displayed considerable variation in scores across visits on the same measures; (2) income-related differences were more prominent in the longitudinal than in the cross-sectional data; (3) no association between cognitive and behavioral measures and body mass index; and (4) several measures showed practice effects, despite the 2-year interval between visits. These data offer an unparalleled opportunity to observe normative performance and change over time on a set of standard and commonly used neuropsychological measures in a population-based sample of healthy children. They thus provide important background for the use and interpretation of these instruments in both research settings and clinical practice. (JINS, 2012, 18, 179–190)

Type
Research Articles
Copyright
Copyright © The International Neuropsychological Society 2011

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

Achenbach, T. (2001). Child Behavior Checklist (CBCL 6-18). Burlington, VT: University Associates in Psychiatry.Google Scholar
Basso, M.R., Carona, F.D., Lowery, N., Axelrod, B.N. (2002). Practice effects on the WAIS-III across 3- and 6-month intervals. Clinical Neuropsychologist, 16(1), 57.CrossRefGoogle ScholarPubMed
Bayley, N. (1949). Consistency and variability in the growth of intelligence from birth to eighteen years. Journal of Genetic Psychology, 75, 165196.Google Scholar
Bors, D.A., Vigneau, F. (2011). Sex differences on the mental rotation test: An analysis of item types. Learning and Individual Differences, 21(1), 129132.CrossRefGoogle Scholar
Brain Development Cooperative Group. (2011). Total and regional brain volumes in a population-based normative sample from 4 to 18 years: The NIH MRI Study of Normal Brain Development. Cerebral Cortex, [Epub ahead of print].Google Scholar
CeNeS. (1998). Cambridge neurospychological test automated battery (version 2.35). Cambridge, UK: CeNeS Cognition.Google Scholar
D'Angiulli, A., Herdman, A., Stapells, D., Hertzman, C. (2008). Children's event-related potentials of auditory selective attention vary with their socioeconomic status. Neuropsychology, 22(3), 293300.CrossRefGoogle ScholarPubMed
Delis, D., Kramer, J., Kaplan, E., Ober, B.A. (1994). California verbal learning test—Children's version. San Antonio, TX: The Psychological Corporation.Google Scholar
Donders, A.R., van der Heijden, G.J., Stignen, T., Moons, K.G. (2006). Review: A gentle introduction to imputation of missing values. Journal of Clinical Epidemiology, 59, 10871091.CrossRefGoogle ScholarPubMed
Elman, J.L. (2005). Connectionist models of cognitive development: Where next? Trends In Cognitive Sciences, 9(3), 111117.CrossRefGoogle ScholarPubMed
Engvig, A., Fjell, A.M., Westlye, L.T., Moberget, T., Sundseth, O., Larsen, V.A., Walhovd, K.B. (2010). Effects of memory training on cortical thickness in the elderly. Neuroimage, 52(4), 16671676.CrossRefGoogle ScholarPubMed
Evans, A.C. (2006). The NIH MRI study of normal brain development. Neuroimage, 30(1), 184202.CrossRefGoogle ScholarPubMed
Fitzmaurice, G.M., Laird, N.M., Ware, J.H. (2004). Applied longitudinal analysis. Hoboken, NJ: Wiley & Sons.Google Scholar
Francis, D.J., Fletcher, J.M., Stuebing, K.K., Lyon, G.R., Shaywitz, B.A., Shaywitz, S.E. (2005). Psychometric approaches to the identification of LD: IQ and achievement scores are not sufficient. Journal of Learning Disabilities, 38(2), 98108.CrossRefGoogle Scholar
Ganjavi, H., Lewis, J.D., Belloc, P., MacDonald, P.A., Waber, D.P., Evans, A.C., Karama, S., Brain Development Cooperative Group. (2011). Negative associations between corpus callosum midsagittal area and IQ in a representative sample of healthy children and adolescents. PLoS One, 6(5), e19698.CrossRefGoogle Scholar
Gardner, R.A., Broman, M. (1979). The Purdue Pegboard: Normative data on 1334 school children. Journal of Clinical Child Psychology, 8(3), 156162.CrossRefGoogle Scholar
Gioia, G.A., Isquith, P.K., Guy, S.C., Kenworthy, L. (2000). Behavior rating inventory of executive function. Odessa, FL: Psychological Assessment Resources.Google Scholar
Haier, R.J., Karama, S., Leyba, L., Jung, R.E. (2009). MRI assessment of cortical thickness and functional activity changes in adolescent girls following three months of practice on a visual-spatial task. BMC Research Notes, 2, 174174.CrossRefGoogle ScholarPubMed
Jensen, A.R., Reynolds, C.R. (1983). Sex differences on the WISC-R. Personality and Individual Differences, 4(2), 223226.CrossRefGoogle Scholar
Joshi, A.A., Leporé, N., Joshi, S.H., Lee, A.D., Barysheva, M., Stein, J.L., Thompson, P.M. (2011). The contribution of genes to cortical thickness and volume. Neuroreport, 22(3), 101105.CrossRefGoogle ScholarPubMed
Karama, S., Ad-Dab'bagh, Y., Haier, R.J., Deary, I.J., Lyttelton, O.C., Lepage, C., … Brain Development Cooperative Group. (2009). Positive association between cognitive ability and cortical thickness in a representative US sample of healthy 6 to 18 year-olds. Intelligence, 37(2), 145155.CrossRefGoogle Scholar
Korkman, M., Kirk, U., Kemp, S. (1997). NEPSY. New York: Psychological Corporation.Google Scholar
Li, Y., Dai, Q., Jackson, J.C., Zhang, J. (2008). Overweight is associated with decreased cognitive functioning among school-age children and adolescents. Obesity, 16(8), 18091815.CrossRefGoogle ScholarPubMed
Luders, E., Thompson, P., Narr, K., Zamanyan, A., Chou, Y.-Y., Gutman, B., Toga, A.W. (2011). The link between callosal thickness and intelligence in healthy children and adolescents. Neuroimage, 54(3), 18231830.CrossRefGoogle ScholarPubMed
Mackenbach, J.P., Stirbu, I., Roskam, A.J., Schaap, M.M., Menvielle, G., Leinsalu, M., Kunst, A.E. (2008). Socioeconomic inequalities in health in 22 European countries. N Engl J Med, 358, 24682481.CrossRefGoogle ScholarPubMed
Matarazzo, J.D., Herman, D.O. (1984). Base rate data for the WAIS-R: Test-retest stability and VIQ-PIQ differences. Journal of Clinical Neuropsychology, 6(4), 351366.CrossRefGoogle ScholarPubMed
McCall, R.B., Appelbaum, M.I., Hogarty, P.S. (1973). Developmental changes in mental performance. Monographs of the Society for Research in Child Development, 38(3), 184.CrossRefGoogle ScholarPubMed
McCartney, K., Burchinal, M.R., Bub, K.L. (2006). Best practices in quantitative methods for developmentalists. Monographs of the Society for Research in Child Development, 71(3), 1145.CrossRefGoogle ScholarPubMed
Mezzacappa, E. (2004). Alerting, orienting, and executive attention: Developmental properties and sociodemographic correlates in an epidemiological sample of young, urban children. Child Development, 75(5), 13731386.CrossRefGoogle Scholar
Muntaner, C., Eaton, W.W., Diala, C., Kessler, R.C., Sorlie, P.D. (1998). Social class, assets, organizational control and the prevalence of common groups of psychiatric disorders. Social Science & Medicine (1982), 47(12), 20432053.CrossRefGoogle ScholarPubMed
Noble, K.G., McCandliss, B.D., Farah, M.J. (2007). Socioeconomic gradients predict individual differences in neurocognitive abilities. Developmental Science, 10(4), 464480.CrossRefGoogle ScholarPubMed
Noble, K.G., Norman, M.F., Farah, M.J. (2005). Neurocognitive correlates of socioeconomic status in kindergarten children. Developmental Science, 8(1), 7487.CrossRefGoogle ScholarPubMed
Parisi, P., Verrotti, A., Paolino, M.C., Urbano, A., Bernabucci, M., Villa, M.P. (2010). Cognitive profile, parental education and BMI in children: Reflections on common neuroendrocrinobiological roots. Journal of Pediatric Endocrinology and Metabolism, 23(11), 11331141.CrossRefGoogle ScholarPubMed
Reite, M., Cullum, C.M., Stocker, J., Teale, P., Kozora, E. (1993). Neuropsychological test performance and MEG-based brain lateralization: Sex differences. Brain Research Bulletin, 32(3), 325328.CrossRefGoogle ScholarPubMed
Rubin, D. (1976). Inference and missing data. Biometrika, 63(3), 581592.CrossRefGoogle Scholar
Rubin, D. (1987). Multiple imputation for nonresponse in surveys. New York: John Wiley & Sons.CrossRefGoogle Scholar
Shafer, J. (1997). Analysis of incomplete multivariate data. New York: Chapman & Hall.CrossRefGoogle Scholar
Siders, A., Kaufman, A.S., Reynolds, C.R. (2006). Do practice effects on Wechsler's performance subtests relate to children's general ability, memory, learning ability, or attention? Applied Neuropsychology, 13(4), 242250.CrossRefGoogle ScholarPubMed
Sirois, P.A., Posner, M., Stehbens, J.A., Loveland, K.A., Nichols, S., Donfield, S., … Hemophilia Growth and Development Study. (2002). Quantifying practice effects in longitudinal research with the WISC-R and WAIS-R: A study of children and adolescents with hemophilia and male siblings without hemophilia. Journal of Pediatric Psychology, 27(2), 121131.CrossRefGoogle ScholarPubMed
Thelen, E., Smith, L.B. (1998). Dynamic systems theories. In W. Damon & R.M. Lerner (Eds.), Handbook of child psychology (5th ed., Vol 1). Theoretical models of human development. New York: John Wiley & Sons.Google Scholar
Tiffin, J., Asher, E.J. (1948). The Purdue pegboard: Norms and studies of reliability and validity. Journal of Applied Psychology, 32, 234247.CrossRefGoogle ScholarPubMed
Tuma, J.M., Appelbaum, A.S. (1980). Reliability and practice effects of WISC-R IQ estimates in a normal population. Educational and Psychological Measurement, 40, 671678.CrossRefGoogle Scholar
Turkheimer, E., Haley, A., Waldron, M., D'Onofrio, B., Gottesman, I.I. (2003). Socioeconomic status modifies heritability of IQ in young children. Psychological Science, 14(6), 623628.CrossRefGoogle ScholarPubMed
Waber, D.P. (2010). Rethinking learning disabilities: Understanding children who struggle in school. New York: Guilford.Google Scholar
Waber, D.P., Carlson, D., Mann, M., Merola, J., Moylan, P. (1984). SES-related aspects of neuropsychological performance. Child Development, 55(5), 18781886.CrossRefGoogle ScholarPubMed
Waber, D.P., de Moor, C., Forbes, P.W., Almli, C.R., Botteron, K.N., … Brain Development Cooperative Group. (2007). The NIH MRI study of normal brain development: Performance of a population based sample of healthy children aged 6 to 18 years on a neuropsychological battery. Journal of the International Neuropsychological Society, 13(5), 729746.CrossRefGoogle ScholarPubMed
Wechsler, D. (1991). Wechsler intelligence scale for children. Third edition. New York: Psychological Corporation.Google Scholar
Wechsler, D. (1999). Wechsler abbreviated scale of intelligence. New York: Psychological Corporation.Google Scholar
Woodcock, R.W., McGrew, K.S., Mather, N. (2001). Woodcock-Johnson III. Itasca, IL: Riverside Publishing.Google Scholar