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Role of Neurocognitive Factors in Academic Fluency for Children and Adults With Spina Bifida Myelomeningocele

Published online by Cambridge University Press:  13 March 2019

Paul T. Cirino ,
Paulina A. Kulesz ,
Amanda E. Child ,
Ashley L. Ware ,
Marcia A. Barnes ,
Jack M. Fletcher  and
Maureen Dennis
Paul T. Cirino*
Affiliation:
Department of Psychology and Texas Institute for Measurement, Evaluation, and Statistics, University of Houston, Houston, Texas
Paulina A. Kulesz
Affiliation:
Department of Psychology and Texas Institute for Measurement, Evaluation, and Statistics, University of Houston, Houston, Texas
Amanda E. Child
Affiliation:
Department of Psychology and Texas Institute for Measurement, Evaluation, and Statistics, University of Houston, Houston, Texas
Ashley L. Ware
Affiliation:
Department of Psychology and Texas Institute for Measurement, Evaluation, and Statistics, University of Houston, Houston, Texas
Marcia A. Barnes
Affiliation:
University of Texas at Austin
Jack M. Fletcher
Affiliation:
Department of Psychology and Texas Institute for Measurement, Evaluation, and Statistics, University of Houston, Houston, Texas
Maureen Dennis
Affiliation:
Program in Neuroscience and Mental Health, The Hospital for Sick Children, Toronto, Canada Department of Surgery, University of Toronto, Canada
*
Correspondence and reprint requests to: Paul T. Cirino, Department of Psychology, Texas Institute for Measurement, Evaluation, and Statistics (TIMES), University of Houston, Houston, TX 77204. E-mail: pcirino@uh.edu
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Abstract

Objectives: Fluency is a major problem for individuals with neurodevelopmental disorders, including fluency deficits for academic skills. The aim of this study was to determine neurocognitive predictors of academic fluency within and across domains of reading, writing, and math, in children and adults, with and without spina bifida. In addition to group differences, we expected some neurocognitive predictors (reaction time, inattention) to have similar effects for each academic fluency outcome, and others (dexterity, vocabulary, nonverbal reasoning) to have differential effects across outcomes. Methods: Neurocognitive predictors were reaction time, inattention, dexterity, vocabulary, and nonverbal reasoning; other factors included group (individuals with spina bifida, n=180; and without, n=81), age, and demographic and untimed academic content skill covariates. Univariate and multivariate regressions evaluated hypotheses. Results: Univariate regressions were significant and robust (R2=.78, .70, .73, for reading, writing, and math fluency, respectively), with consistent effects of covariates, age, reaction time, and vocabulary; group and group moderation showed small effect sizes (<2%). Multivariate contrasts showed differential prediction across academic fluency outcomes for reaction time and vocabulary. Conclusions: The novelty of the present work is determining neurocognitive predictors for an important outcome (academic fluency), within and across fluency domains, across population (spina bifida versus typical), over a large developmental span, in the context of well-known covariates. Results offer insight into similarities and differences regarding prediction of different domains of academic fluency, with implications for addressing academic weakness in spina bifida, and for evaluating similar questions in other neurodevelopmental disorders. (JINS, 2019, 25, 249–265)

Keywords

Spina bifidaAcademic fluencyNeurocognitive predictorsNeurodevelopment
Type
Regular Research
Information
Journal of the International Neuropsychological Society , Volume 25 , Issue 3 , March 2019 , pp. 249 - 265
Copyright
Copyright © The International Neuropsychological Society 2019 

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References

REFERENCES

Abbott, R.D., & Berninger, V.W. (1993). Structural equation modeling of relationships among developmental skills and writing skills in primary- and intermediate-grade writers. Journal of Educational Psychology, 85(3), 478–508. https://doi.org/10.1037/0022-0663.85.3.478 Google Scholar
Agopian, A.J., Canfield, M.A., Olney, R.S., Lupo, P.J., Ramadhani, T., Mitchell, L.E., … National Birth Defects Prevention Study. (2012). Spina bifida subtypes and sub‐phenotypes by maternal race/ethnicity in the National Birth Defects Prevention Study. American Journal of Medical Genetics Part A, 158(1), 109–115. https://doi.org/10.1002/ajmg.a.34383 Google Scholar
American Psychiatric Association. (2000). Diagnostic and Statistical Manual of Mental Disorders (4th ed. text revision). Washington, DC: American Psychiatric Association.Google Scholar
Ashcraft, M.H., & Christy, K.S. (1995). The frequency of arithmetic facts in elementary texts: Addition and multiplication in grades 1–6. Journal for Research in Mathematics Education, 26(5), 396–421. https://www.jstor.org/stable/749430 Google Scholar
Ayr, L.K., Yeates, K.O., & Enrile, B.G. (2005). Arithmetic skills and their cognitive correlates in children with acquired and congenital brain disorder. Journal of the International Neuropsychological Society, 11(3), 249–262. https://doi.org/10.1017/S1355617705050307 Google Scholar
Badian, N.A. (1999). Persistent arithmetic, reading, or arithmetic and reading disability. Annals of Dyslexia, 49(1), 43–70. https://doi.org/10.1007/s11881-999-0019-8 Google Scholar
Barkovich, A.J. (2005). Pediatric neuroimaging (4th ed.). Philadelphia, PA: Lippincott Williams & Wilkins. https://doi.org/10.1097/00008480-199012000-00002 Google Scholar
Barnes, M.A., Dennis, M., & Hetherington, R. (2004). Reading and writing skills in young adults with spina bifida and hydrocephalus. Journal of the International Neuropsychological Society, 10(5), 655–663. https://doi.org/10.1017/S1355617704105055 Google Scholar
Barnes, M.A., Faulkner, H.J., & Dennis, M. (2001). Poor reading comprehension despite fast word decoding in children with hydrocephalus. Brain and Language, 76, 35–44. https://doi.org/10.1006/brln.2000.2389 Google Scholar
Barnes, M.A., Raghubar, K.P., English, L., Williams, J.M., Taylor, H., & Landry, S. (2014). Longitudinal mediators of achievement in mathematics and reading in typical and atypical development. Journal of Experimental Child Psychology, 119, 1–16. https://doi.org/10.1016/j.jecp.2013.09.006 Google Scholar
Barnes, M.A., Stubbs, A., Raghubar, K.P., Agostino, A., Taylor, H., Landry, S., … Smith-Chant, B. (2011). Mathematical skills in 3- and 5-year-olds with spina bifida and their typically developing peers: A longitudinal approach. Journal of the International Neuropsychological Society, 17(3), 431–444. https://doi.org/10.1017/S1355617711000233.Mathematical Google Scholar
Barnes, M.A., Wilkinson, M., Khemani, E., Boudesquie, A., Dennis, M., & Fletcher, J.M. (2006). Arithmetic processing in children with spina bifida: Calculation accuracy, strategy use, and fact retrieval fluency. Journal of Learning Disabilities, 39(2), 174–187. https://doi.org/10.1177/00222194060390020601 Google Scholar
Barquero, L.A., Sefcik, A.M., Cutting, L.E., & Rimrodt, S.L. (2015). Teaching reading to children with neurofibromatosis type 1: A clinical trial with random assignment to different approaches. Developmental Medicine & Child Neurology, 57(12), 1150–1158.Google Scholar
Berninger, V.W., Cartwright, A.C., Yates, C.M., Swanson, H.L., & Abbott, R.D. (1994). Developmental skills related to writing and reading acquisition in the intermediate grades: Shared and unique functional systems. Reading and Writing, 6(1991), 161–196. https://doi.org/10.1007/BF01026911 Google Scholar
Berninger, V.W., & Rutberg, J. (1992). Relationship of finger function to beginning writing: Application of diagnosis to writing disabilities. Developmental Medicine and Child Neurology, 34, 198–215.Google Scholar
Berninger, V.W., Yates, C., Cartwright, A., Rutberg, J., Remy, E., & Abbott, R. (1992). Lower-level developmental skills in beginning writing. Reading and Writing, 4(3), 257–280. https://doi.org/10.1007/BF01027151 Google Scholar
Boulet, S., Gambrell, D., Shin, M., Honein, M., & Mathews, T. (2009). Racial/ethnic differences in the birth prevalence of spina bifida -- United States, 1995-2005. Journal of the Americal Medical Association, 301(201), 2203–2204.Google Scholar
Branum-Martin, L., Fletcher, J.M., & Stuebing, K.K. (2013). Classification and identification of reading and math disabilities: The special case of comorbidity. Journal of Learning Disabilities, 46(6), 490–499. https://doi.org/10.1177/0022219412468767 Google Scholar
Buckhalt, J.A. (1991). Reaction time measures of processing speed: Are they yielding new information about intelligence? Personality and Individual Differences, 12(7), 683–688. https://doi.org/10.1016/0191-8869(91)90223-X Google Scholar
Bugden, S., Price, G.R., McLean, D.A., & Ansari, D. (2012). The role of the left intraparietal sulcus in the relationship between symbolic number processing and children’s arithmetic competence. Developmental Cognitive Neuroscience, 2(4), 448–457. https://doi.org/10.1016/j.dcn.2012.04.001 Google Scholar
Burmeister, R., Hannay, H.J., Copeland, K., Fletcher, J.M., Boudousquie, A., & Dennis, M. (2005). Attention problems and executive functions in children with spina bifida and hydrocephalus. Child Neuropsychology, 11(3), 265–283. https://doi.org/10.1080/092970490911324 Google Scholar
Bussing, R., Fernandez, M., Harwood, M., Wei Hou, W., Garvan, C.W., Eyberg, S.M., … Eyberg, S.M. (2008). Parent and teacher SNAP-IV ratings of attention deficit/hyperactivity disorder symptoms: Psychometric properties and normative rating from a school district sample. Assessment, 15(3), 317–328. https://doi.org/10.1177/1073191107313888.Parent Google Scholar
Camarata, S., & Woodcock, R. (2006). Sex differences in processing speed: Developmental effects in males and females. Intelligence, 34(3), 231–252. https://doi.org/10.1016/j.intell.2005.12.001 Google Scholar
Catts, H.W., Fey, M.E., Tomblin, J.B., & Zhang, X. (2002). A longitudinal investigation of reading outcomes in children with language impairments. Journal of Speech, Language, and Hearing Research, 45, 1142–1157. https://doi:10.1044/1092-4388(2002/093)Google Scholar
Catts, H.W., Gillispie, M., Leonard, L.B., Kail, R.V., & Miller, C.A. (2002). The role of speed of processing, rapid naming, and phonological awareness in reading achievement. Journal of Learning Disabilities, 35(6), 509–524. https://doi.org/10.1177/00222194020350060301 Google Scholar
Chard, D.J., Vaughn, S., & Tyler, B.J. (2002). A synthesis of research on effective interventions for building reading fluency with elementary students with learning disabilities. Journal of Learning Disabilities, 35(5), 386–406.Google Scholar
Chen, Q., & Li, J. (2014). Association between individual differences in non-symbolic number acuity and math performance: A meta-analysis. Acta Psychologica, 148, 163–172. https://doi.org/10.1016/j.actpsy.2014.01.016 Google Scholar
Child, A.E., Cirino, P.T., Fletcher, J.M., Willcutt, E.G., & Fuchs, L.S. (2018). A cognitive dimensional approach to understanding shared and unique contributions to reading, math, and attention skills. Journal of Learning Disabilities. https://doi.org/10.1177/0022219418775115 Google Scholar
Christensen, C.A. (2005). The role of orthographic-motor integration in the production of creative and well-structured written text for students in secondary school. Educational Psychology, 25(5), 441–453. https://doi.org/10.1080/01443410500042076 Google Scholar
Cirino, P.T., Child, A.E., & Macdonald, K. (2018). Longitudinal overlap of reading and math and their predictors. Contemporary Educational Psychology, 54, 99–111. https://doi.org/10.1016/j.cedpsych.2018.06.002 Google Scholar
Cirino, P.T., Fletcher, J.M., Ewing-Cobbs, L., Barnes, M.A., & Fuchs, L.S. (2007). Cognitive arithmetic differences in learning difficulty groups and the role of behavioral inattention. Learning Disabilities Research & Practice, 22(1), 25–35. https://doi.org/10.1111/j.1540-5826.2007.00228.x Google Scholar
Cirino, P.T., Israelian, M.K., Morris, M.K., & Morris, R.D. (2005). Evaluation of the double-deficit hypothesis in college students referred for learning difficulties. Journal of Learning Disabilities, 38(1), 29–43. http://www.ncbi.nlm.nih.gov/pubmed/15727327 Google Scholar
Cirino, P.T., Morris, M., & Morris, R. (2002). Neuropsychological concomitants of calculation skills in college students referred for learning difficulties. Developmental Neuropsychology, 21(2), 201–218. https://doi.org/10.1207/S15326942DN2102_6 Google Scholar
Cirino, P.T., Romain, M.A., Barth, A.E., Tolar, T.D., Fletcher, J.M., & Vaughn, S. (2013). Reading skill components and impairments in middle school struggling readers. Reading and Writing, 26(7), 1059–1086. https://doi.org/10.1007/s11145-012-9406-3 Google Scholar
Codding, R.S., Burns, M.K., & Lukito, G. (2011). Meta-analysis of acquisition and fluency math interventions with instructional and frustration level skills: Evidence for a skill-by-treatment interaction. School Psychology Review, 39(1), 69–83 Google Scholar
Compton, D.L., Fuchs, L.S., Fuchs, D., Lambert, W., & Hamlett, C. (2012). The cognitive and academic profiles of reading and mathematics learning disabilities. Journal of Learning Disabilities, 45(1), 79–95. https://doi.org/10.1177/0022219410393012 Google Scholar
Conners, C.K., Erhardt, D., & Sparrow, E.P. (1998). Conners’ Adult ADHD Rating Scales (CAARS). Toronto, ON: Multi-Health Systems, Inc.Google Scholar
Copp, A.J., Adzick, N.S., Chitty, L.S., Fletcher, J.M., Grayson, N., & Shaw, G.M. (2015). Spina bifida. Nature Reviews. Disease Primers, 1, 15007. https://doi.org/10.1038/nrdp.2015.7 Google Scholar
Coughlin, J., & Montague, M. (2011). The effects of cognitive strategy instruction on the mathematical problem solving of adolescents with spina bifida. Journal of Special Education, 45(3), 171–183. https://doi.org/10.1177/0022466910363913 Google Scholar
Deak, K.L., Siegel, D.G., George, T.M., Gregory, S., Ashley‐Koch, A., Speer, M.C., & NTD Collaborative Group. (2008). Further evidence for a maternal genetic effect and a sex‐influenced effect contributing to risk for human neural tube defects. Birth Defects Research Part A: Clinical and Molecular Teratology, 82(10), 662–669. https://doi:10.1002/bdra.20511Google Scholar
DeBono, T., Hosseini, A., Cairo, C., Ghelani, K., Tannock, R., & Toplak, M.E. (2012). Written expression performance in adolescents with attention-deficit/hyperactivity disorder (ADHD). Reading and Writing, 25(6), 1403–1426. https://doi.org/10.1007/s11145-011-9325-8 Google Scholar
Dennis, M., & Barnes, M.A. (2002). Math and numeracy in young adults with spina bifida and hydrocephalus. Developmental Neuropsychology, 21(2), 141–155. https://doi.org/10.1207/S15326942DN2102_2 Google Scholar
Dennis, M., & Barnes, M.A. (2010). The cognitive phenotype of spina bifida meningomyelocele. Developmental Disabilities Research Reviews, 16(1), 31–39. https://doi.org/10.1002/ddrr.89.The Google Scholar
Dennis, M., Cirino, P.T., Simic, N., Juranek, J., Taylor, W.P., & Fletcher, J.M. (2016). White and grey matter relations to simple, choice, and cognitive reaction time in spina bifida. Brain Imaging and Behavior, 10(1), 238–251. https://doi.org/10.1007/s11682-015-9388-2 Google Scholar
Dennis, M., Edelstein, K., Hetherington, R., Copeland, K., Frederick, J., Blaser, S.E., … Fletcher, J.M. (2004). Neurobiology of perceptual and motor timing in children with spina bifida in relation to cerebellar volume. Brain, 127(6), 1292–1301. https://doi.org/10.1093/brain/awh154 Google Scholar
Dennis, M., Landry, S.H., Barnes, M., & Fletcher, J.M. (2006). A model of neurocognitive function in spina bifida over the life span. Journal of the International Neuropsychological Society, 12(2), 285–296. https://doi.org/10.1017/S1355617706060371 Google Scholar
Dennis, M., Salman, M.S., Juranek, J., & Fletcher, J.M. (2010). Cerebellar motor function in spina bifida meningomyelocele. Cerebellum, 9(4), 484–498. https://doi.org/10.1007/s12311-010-0191-8 Google Scholar
Detrait, E.R., George, T.M., Etchevers, H.C., Gilbert, J.R., Vekemans, M., & Speer, M.C. (2005). Human neural tube defects: Developmental biology, epidemiology, and genetics. Neurotoxicology and Teratology, 27(3), 515–524. https://doi.org/10.1016/j.ntt.2004.12.007.Human Google Scholar
Fletcher, J.M., Copeland, K., Frederick, J.A., Blaser, S.E., Kramer, L.A., Northrup, H., … Dennis, M. (2005). Spinal lesion level in spina bifida: A source of neural and cognitive heterogeneity. Journal of Neurosurgery, 102(3 Suppl), 268–279. https://doi.org/10.3171/ped.2005.102.3.0268 Google Scholar
Fletcher, J.M., Lyon, G.R., Fuchs, L.S., & Barnes, M.A. (in press). Learning Disabilties: From Identification to Intervention (2nd Ed.). New York: Guilford.Google Scholar
Fletcher, J.M., Ostermaier, K.K., Cirino, P.T., & Dennis, M. (2008). Neurobehavioral outcomes in spina bifida: Processes versus outcomes. Journal of Pediatric Rehabilitation Medicine, 1(4), 311–324.Google Scholar
Floyd, R.G., McGrew, K.S., & Evans, J.J. (2008). The relative contributions of the Cattell-Horn-Carroll cognitive abilities in explaining writing achievement during childhood and adolescence. Psychology in the Schools, 45(2), 132–144. https://doi.org/10.1002/pits Google Scholar
Fuchs, L.S., Fuchs, D., Compton, D.L., Powell, S.R., Seethaler, P.M., Capizzi, A.M., … Fletcher, J.M. (2006). The cognitive correlates of third-grade skill in årithmetic, algorithmic computation, and arithmetic word problems. Journal of Educational Psychology, 98(1), 29–43. https://doi.org/10.1037/0022-0663.98.1.29 Google Scholar
Fuchs, L.S., Geary, D.C., Compton, D.L., Fuchs, D., Hamlett, C.L., Seethaler, P.M., … Schatschneider, C. (2011). Do different types of school mathematics development depend on different constellations of numerical versus general cognitive abilities? Developmental Psychology, 46(6), 1731–1746. https://doi.org/10.1037/a0020662.Do Google Scholar
Fuchs, L.S., Geary, D.C., Fuchs, D., Compton, D.L., & Hamlett, C.L. (2016). Pathways to third-grade calculation versus word-reading competence: Are they more alike or different? Child Development, 87(2), 558–567. https://doi.org/10.1111/cdev.12474.Google Scholar
Fuchs, L.S., Powell, S.R., Seethaler, P.M., Fuchs, D., Hamlett, C.L., Cirino, P.T., & Fletcher, J.M. (2010). A framework for remediating number combination deficits. Exceptional Children, 76(2), 135–156.Google Scholar
Gaub, M., & Carlson, C.L. (1997). Gender differences in ADHD: A meta-analysis and critical review. Journal of the American Academy of Child and Adolescent Psychiatry, 36(8), 1036–1045. https://doi.org/10.1097/00004583-199708000-00011 Google Scholar
Geary, D.C. (2006). Development of mathematical understanding. Handbook of Child Psychology, (November), 777–810. https://doi.org/10.1002/9780470147658.chpsy0218 Google Scholar
Geary, D.C., Saults, S.J., Liu, F., & Hoard, M.K. (2000). Sex differences in spatial cognition, computational fluency, and arithmetical reasoning. Journal of Experimental Child Psychology, 77(4), 337–353. https://doi.org/10.1006/jecp.2000.2594 Google Scholar
Graham, S., Berninger, V.W., Abbot, R.D., Abbot, S.P., & Whitaker, D. (1997). Role of mechanics in composing of elementary school students: A new methodological approach. Journal of Educational Psychology, 89(1), 170–182. https://doi.org/10.1037/0022-0663.89.1.170 Google Scholar
Graham, S., Fishman, E.J., Reid, R., & Hebert, M. (2016). Writing characteristics of students with inattention deficit hyperactive disorder: A meta-analysis. Learning Disabilities Research and Practice, 31(2), 75–89. https://doi.org/10.1111/ldrp.12099 Google Scholar
Graham, S., & Harris, K.R. (2000). The role of self-regulation and transcription skills in writing and writing development. Educational Psychologist, 35(1), 3–12.Google Scholar
Halberda, J., Mazzocco, M.M., & Feigenson, L. (2008). Individual differences in non-verbal number acuity correlate with maths achievement. Nature, 455(7213), 665–668. https://doi.org/10.1038/nature07246 Google Scholar
Hasan, K.M., Eluvathingal, T.J., Kramer, L.A., Ewing-Cobbs, L., Dennis, M., & Fletcher, J.M. (2008). White matter microstructural abnormalities in children with spina bifida myelomeningocele and hydrocephalus: A diffusion tensor tractography study of the association pathways. Journal of Magnetic Resonance Imaging, 27(4), 700–709. https://doi.org/10.1037/a0013262.Open Google Scholar
Hollingshead, A.B. (1975). Four factor index of social status. New Haven, CN: Yale University.Google Scholar
Jordan, N.C., Hanich, L.B., & Kaplan, D. (2003). A longitudinal study of mathematical competencies in children with specific mathematics difficulties versus children with comorbid mathematics and reading difficulties. Child Development, 74(3), 834–850. https://doi.org/10.1111/1467-8624.00571 Google Scholar
Jordan, N.C., Hansen, N., Fuchs, L.S., Siegler, R.S., Gersten, R., & Micklos, D. (2013). Developmental predictors of fraction concepts and procedures. Journal of Experimental Child Psychology, 116(1), 45–58. https://doi.org/10.1016/j.jecp.2013.02.001 Google Scholar
Juranek, J., & Salman, M.S. (2010). Anomalous development of brain structure and function in spina bifida myelomeningocele. Developmental Disabilities Research Reviews, 16(1), 23–30. https://doi.org/10.1002/ddrr.88 Google Scholar
Kim, Y.G. (2015). Developmental, component-based model of reading fluency: An investigation of predictors of word-reading fluency, text-reading fluency, and reading comprehension. Reading Research Quarterly, 50(4), 459–481. https://doi:10.1002/rrq.107Google Scholar
Korpipaa, H., Koponen, T., Aro, T., Tolvanen, A., Aunola, K., Poikkeus, A.-M., … Nurmi, J.-E. (2017). Covariation between reading and arithmetic skills from Grade 1 to Grade 7. Contemporary Educational Psychology, 51, 131–140. https://doi:10.1016/j.cedpsych.2017.06.005Google Scholar
Landerl, K., & Moll, K. (2010). Comorbidity of learning disorders: Prevalence and familial transmission. Journal of Child Psychology and Psychiatry and Allied Disciplines, 51(3), 287–294. https://doi.org/10.1111/j.1469-7610.2009.02164.x Google Scholar
Landerl, K., & Wimmer, H. (2008). Development of word reading fluency and spelling in a consistent orthography: An 8-year follow-up. Journal of Educational Psychology, 100(1), 150–161. https://doi.org/10.1037/0022-0663.100.1.150 Google Scholar
Manolitsis, G., Georgiou, G.K., & Tziraki, N. (2013). Examining the effects of home literacy and numeracy environment on early reading and math acquisition. Early Childhood Research Quarterly, 28(4), 692–703. https://doi.org/10.1016/j.ecresq.2013.05.004 Google Scholar
Martens, R., Hurks, P.P.M., Meijs, C., Wassenberg, R., & Jolles, J. (2011). Sex differences in arithmetical performance scores: Central tendency and variability. Learning and Individual Differences, 21(5), 549–554. https://doi.org/10.1016/j.lindif.2011.06.003 Google Scholar
Massetti, G.M., Lahey, B.B., Pelham, W.E., Loney, J., Ehrhardt, A., Lee, S.S., & Kipp, H. (2008). Academic achievement over eight years among children who met modified criteria for attention-deficit/hyperactivity disorder at 4-6 years of age. Journal of Abnormal Child Psychology, 36(3), 399–410. https://doi.org/10.1007/s10802-007-9186-4.Google Scholar
McGrath, L.M., Pennington, B.F., Shanahan, M.A., Santerre-Lemmon, L.E., Barnard, H.D., Willcutt, E.G., … Olson, R.K. (2011). A multiple deficit model of reading disability and attention-deficit/hyperactivity disorder: Searching for shared cognitive deficits. Journal of Child Psychology and Psychiatry, and Allied Disciplines, 52(5), 547–557. https://doi.org/10.1111/j.1469-7610.2010.02346.x Google Scholar
McGrew, K.S., LaForte, E.M., & Schrank, F.A. (2014). Woodcock-Johnson IV tecnhical manual. Rolling Meadows, IL: Riverside.Google Scholar
McGrew, K.S., & Woodcock, R.W. (2001). Technical Manual Woodcock-Johnson III. Itasca, IL: Riverside Publishing.Google Scholar
Nelson, J.R., Benner, G.J., Neill, S., & Stage, S.A. (2006). Interrelationships among language skills, externalizing behavior, and academic fluency and their impact on the academic skills of students with ED. Journal of Emotional and Behavioral Disorders, 14(4), 209–216. https://doi.org/10.1177/10634266060140040401 Google Scholar
Oldfield, R.C. (1971). The assessment and analysis of handedness: The Edinburgh Inventory. Neuropsychologia, 9, 97–113.Google Scholar
Peng, P., Namkung, J., Barnes, M., & Sun, C. (2016). A meta-analysis of mathematics and working memory: Moderating effects of working memory domain, type of mathematics skill, and sample characteristics. Journal of Educational Psychology, 108(4), 455–473. https://doi.org/10.1037/edu0000079 Google Scholar
Penner-Wilger, M., Fast, L., LeFevre, J.-A., Smith-Chant, B.L., Skwarchuk, S.-L., Kamawar, D., & Bisanz, J. (2007). The foundations of numeracy: Subitizing, finger gnosia, and fine motor ability. Proceedings of the 29th Annual Cognitive Science Society, 1385–1390.Google Scholar
Penner-Wilger, M., Fast, L., LeFevre, J.-A., Smith-Chant, B.L., Skwarchuk, S.-L., Kamawar, D., & Bisanz, J. (2009). Subitizing, finger gnosis, and the representation of number. Proceedings of the 31st Annual Cognitive Science Society, (1999), 520–525.Google Scholar
Pennington, B.F. (2006). From single to multiple deficit models of developmental disorders. Cognition, 101(2), 385–413. https://doi.org/10.1016/j.cognition.2006.04.008 Google Scholar
Perfetti, C. (2007). Reading ability: Lexical quality to comprehension. Scientific Studies of Reading, 11(4), 357–383.Google Scholar
Petrill, S., Logan, J., Hart, S., Vincent, P., Thompson, L., Kovas, Y., & Plomin, R. (2012). Math fluency is etiologically distinct from untimed math performance, decoding fluency, and untimed reading performance: Evidence from a twin study. Journal of Learning Disabilities, 45(4), 371–381. https://doi.org/10.1177/0022219411407926 Google Scholar
Pike, M., Swank, P., Taylor, H., Landry, S., & Barnes, M.A. (2013). Effect of preschool working memory, language, and narrative abilities on inferential comprehension at school-age in children with spina bifida myelomeningocele and typically developing children. Journal of the International Neuropsychological Society, 19, 390–399. https://doi.org/10.1017/S1355617712001579 Google Scholar
Pinnell, G.S., Pikulski, J.J., Wixson, K.K., Campbell, J.R., Gough, P.B., & Beatty, A.S. (1995). Listening to children read aloud: Data from NAEP’s integrated reading performance record (IRPR) at grade 4. Washington, DC: U.S. Department of Education.Google Scholar
Poletta, F.A., Rittler, M., Saleme, C., Campaña, H., Gili, J.A., Pawluk, M.S., … López-Camelo, J.S. (2018). Neural tube defects: Sex ratio changes after fortification with folic acid. PLoS One, 13(3), e0193127. https://doi.org/10.1371/journal.pone.0193127 Google Scholar
Rabiner, D., & Coie, J.D. (2000). Early attention problems and children’s reading achievement: A longitudinal investigation. Journal of the American Academy of Child and Adolescent Psychiatry, 39(7), 859–867.Google Scholar
Raghubar, K.P., Barnes, M.A., Dennis, M., Cirino, P.T., Taylor, H., & Landry, S. (2015). Neurocognitive predictors of mathematical processing in school-aged children with spina bifida and their typically developing peers: Attention, working memory, and fine motor skills. Neuropsychology, 29(6), 861–873. https://doi.org/10.1037/neu0000196 Google Scholar
Raimondi, A.J. (1994). A unifying theory for the definition and classification of hydrocephalus. Child’s Nervous System, 10(1), 2–12. https://doi.org/10.1007/BF00313578 Google Scholar
Inc, S.A.S. (2015). SAS 9.4 SQL procedure user’s guide. Cary, NC: SAS Institute, Inc.Google Scholar
Schatschneider, C., Fletcher, J.M., Francis, D.J., Carlson, C.D., & Foorman, B.R. (2004). Kindergarten prediction of reading skills: A longitudinal comparative analysis. Journal of Educational Psychology, 96(2), 265–282. https://doi.org/10.1037/0022-0663.96.2.265 Google Scholar
Shanahan, M.A., Pennington, B.F., & Willcutt, E.W. (2008). Do motivational incentives reduce the inhibition deficit in ADHD? Developmental Neuropsychology, 33(2), 137–159. https://doi.org/10.1080/87565640701884238 Google Scholar
Shanahan, M.A., Pennington, B.F., Yerys, B.E., Scott, A., Boada, R., Willcutt, E.G., … DeFries, J.C. (2006). Processing speed deficits in attention deficit/hyperactivity disorder and reading disability. Journal of Abnormal Child Psychology, 34(5), 585–602. https://doi.org/10.1007/s10802-006-9037-8 Google Scholar
Shin, M., Besser, L.M., Siffel, C., Kucik, J.E., Shaw, G.M., Lu, C., & Correa, A. (2010). Prevalence of spina bifida among children and adolescents in 10 regions in the United States. Pediatrics, 126(2), 274–279. https://doi.org/10.1542/peds.2009-2084 Google Scholar
Slot, E.M., van Viersen, S., de Bree, E.H., & Kroesbergen, E.H. (2016). Shared and unique risk factors underlying mathematical disability and reading and spelling disability. Frontiers in Psychology, 7(JUN). https://doi.org/10.3389/fpsyg.2016.00803 Google Scholar
Swanson, H.L., & Berninger, V.W. (1996). Individual differences in children’s working memory and writing skills. Journal of Experimental Child Psychology, 63, 358–385. https://doi.org/http://www.ingentaconnect.com/content/ap/ch/1996/00000063/00000002/art00054 Google Scholar
Swanson, J., Nolan, W., & Pelham, W.E. (1992). The SNAP-IV Rating Scale. Irvine, CA: University of California at Irvine.Google Scholar
Thorndike, R.L., Hagen, E.P., & Sattler, J.M. (1986). Stanford-Binet Intelligence Scale: Fourth Edition (SB: FE). Itasca, IL: Riverside Publishing Company.Google Scholar
Tiffin, J. (1968). Purdue pegboard examiner’s manual. Rosemont, IL: London House.Google Scholar
Tiffin, J., & Asher, E.J. (1948). The Purdue pegboard; norms and studies of reliability and validity. The Journal of Applied Psychology, 32(3), 234–247. https://doi.org/10.1037/h0061266 Google Scholar
Tobia, V., & Marzocchi, G.M. (2014). Predictors of reading fluency in Italian orthography: Evidence from a cross-sectional study of primary school students. Child Neuropsychology, 20(4), 449–469. http://dx.doi.org/10.1080/09297049.2013.814768 Google Scholar
Van Allen, M.I., Kalousek, D.K., Chernoff, G.F., Juriloff, D., Harris, M., McGillivray, B.C., … Hall, J.G. (1993). Evidence for multi-site closure of the neural tube in humans. American Journal of Medical Genetics, 47(5), 723–743. https://doi.org/10.1002/ajmg.1320470528 Google Scholar
Wasner, M., Nuerk, H.C., Martignon, L., Roesch, S., & Moeller, K. (2016). Finger gnosis predicts a unique but small part of variance in initial arithmetic performance. Journal of Experimental Child Psychology, 146, 1–16. https://doi.org/10.1016/j.jecp.2016.01.006 Google Scholar
Wasserman, R.M., & Holmbeck, G.N. (2016). Profiles of neuropsychological functioning in children and adolescents with spina bifida: Associations with biopsychosocial predictors and functional outcomes. Journal of the International Neuropsychological Society, 22(8), 804–815. https://doi.org/10.1017/S1355617716000680 Google Scholar
Wasserman, R.M., Stoner, A.M., Stern, A., & Holmbeck, G.N. (2016). ADHD and attention problems in children with and without spina bifida. Topics in Spinal Cord Injury Rehabilitation, 22(4), 253–259. https://doi.org/10.1310/sci2204-253 Google Scholar
Willcutt, E.G., Petrill, S.A., Wu, S., Boada, R., Defries, J.C., Olson, R.K., & Pennington, B.F. (2013). Comorbidity between reading disability and math disability: Concurrent psychopathology, functional impairment, and neuropsychological functioning. Journal of Learning Disabilities, 46(6), 500–516. https://doi.org/10.1177/0022219413477476 Google Scholar
Williams, L.J., Rasmussen, S.A., Flores, A., Kirby, R.S., & Edmonds, L.D. (2005). Decline in the prevalence of spina bifida and anencephaly by race/ethnicity: 1995 - 2002. Pediatrics, 116, 580–586. https://doi.org/116/3/580 Google Scholar
Wise, J.C., Pae, H.K., Wolfe, C.B., Sevcik, R.A., Morris, R.D., Lovett, M., & Wolf, M. (2008). Phonological awareness and rapid naming skills of children with reading disabilities and children with reading disabilities who are at risk for mathematics difficulties. Learning Disabilities Research & Practice, 23(3), 125–136. https://doi.org/10.1111/j.1540-5826.2008.00270.x Google Scholar
Wolf, M., & Bowers, P.G. (1999). The double-deficit hypothesis for the developmental dyslexias. Journal of Educational Psychology, 91(3), 415–438.Google Scholar
Wolf, M., & Katzir-Cohen, T. (2001). Reading fluency and its intervention. Scientific Studies of Reading, 5(3), 211–239. https://doi.org/10.1207/S1532799XSSR0503_2 Google Scholar
Woodcock, R., McGrew, K., & Mather, N. (2001). Woodcock-Johnson – III. Itasca, IL: Riverside Publishing.Google Scholar