Advertisement

Journal of Neuro-Oncology

, Volume 141, Issue 2, pp 403–411 | Cite as

Computerized assessment of cognitive impairment among children undergoing radiation therapy for medulloblastoma

  • Andrew M. Heitzer
  • Jason M. Ashford
  • Brian T. Harel
  • Adrian Schembri
  • Michelle A. Swain
  • Joanna Wallace
  • Kirsten K. Ness
  • Fang Wang
  • Hui Zhang
  • Thomas E. Merchant
  • Giles W. Robinson
  • Amar Gajjar
  • Heather M. ConklinEmail author
Clinical Study

Abstract

Purpose

Advantages to computerized cognitive assessment include increased precision of response time measurement and greater availability of alternate forms. Cogstate is a computerized cognitive battery developed to monitor attention, memory, and processing speed. Although the literature suggests the domains assessed by Cogstate are areas of deficit in children undergoing treatment for medulloblastoma, the validity of Cogstate in this population has not been previously investigated.

Methods

Children participating in an ongoing prospective trial of risk-adapted therapy for newly diagnosed medulloblastoma (n = 73; mean age at baseline = 12.1 years) were administered Cogstate at baseline (after surgery, prior to adjuvant therapy) and 3 months later (6 weeks after completion of radiation therapy). Gold-standard neuropsychological measures of similar functions were administered at baseline.

Results

Linear mixed models revealed performance within age expectations at baseline across Cogstate tasks. Following radiation therapy, there was a decline in performance on Cogstate measures of reaction time (Identification and One Back). Females exhibited slower reaction time on One Back and Detection tasks at baseline. Higher-dose radiation therapy and younger age were associated with greater declines in performance. Pearson correlations revealed small-to-moderate correlations between Cogstate reaction time and working memory tasks with well-validated neuropsychological measures.

Conclusions

Cogstate is sensitive to acute cognitive effects experienced by some children with medulloblastoma and demonstrates associations with clinical predictors established in the literature. Correlations with neuropsychological measures of similar constructs offer additional evidence of validity. The findings provide support for the utility of Cogstate in monitoring acute cognitive effects in pediatric cancer.

Keywords

Medulloblastoma Brain tumor Cogstate Pediatric Neuropsychology 

Notes

Funding

This work was supported, in part, by the National Cancer Institute (St. Jude Cancer Center Support [CORE] Grant No.: [P30-CA21765]) and the American Lebanese Syrian Associated Charities (ALSAC).

Compliance with ethical standards

Conflict of interest

At the time of this study, Brian Harel, PhD, JD and Adrian Schembri, PhD were employees of Cogstate Limited, which is the supplier of the computerized battery used in the study. The remaining authors have no conflicts of interest to disclose.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Rickert CH, Paulus W (2001) Epidemiology of central nervous system tumors in childhood and adolescence based on the new WHO classification. Child’s Nerv Syst 17:503–511.  https://doi.org/10.1007/s003810100496 CrossRefGoogle Scholar
  2. 2.
    Ullrich NJ, Embry L (2012) Neurocognitive dysfunction in survivors of childhood brain tumors. Semin Pediatr Neurol 19:35–42.  https://doi.org/10.1016/j.spen.2012.02.014 CrossRefGoogle Scholar
  3. 3.
    Spiegler BJ, Bouffet E, Greenberg ML et al (2004) Change in neurocognitive functioning after treatment with cranial radiation in childhood. J Clin Oncol 22:706–713.  https://doi.org/10.1200/JCO.2004.05.186 CrossRefGoogle Scholar
  4. 4.
    Packer RJ, Gajjar A, Vezina G et al (2006) Phase III study of craniospinal radiation therapy followed by adjuvant chemotherapy for newly diagnosed average-risk medulloblastoma. J Clin Oncol 24:4202–4208.  https://doi.org/10.1200/JCO.2006.06.4980 CrossRefGoogle Scholar
  5. 5.
    Gajjar A, Chintagumpala M, Ashley D et al (2006) Risk-adapted craniospinal radiotherapy followed by high-dose chemotherapy and stem-cell rescue in children with newly diagnosed medulloblastoma (St Jude Medulloblastoma-96): long-term results from a prospective, multicentre trial. Lancet Oncol Lond 7:813–820CrossRefGoogle Scholar
  6. 6.
    Byer L, Kline C, Mueller S (2016) Clinical trials in pediatric neuro-oncology: what is missing and how we can improve. CNS Oncol 5:233–239.  https://doi.org/10.2217/cns-2016-0016 CrossRefGoogle Scholar
  7. 7.
    Mulhern RK, Merchant TE, Gajjar A et al (2004) Late neurocognitive sequelae in survivors of brain tumours in childhood. Lancet Oncol 5:399–408.  https://doi.org/10.1016/S1470-2045(04)01507-4 CrossRefGoogle Scholar
  8. 8.
    Reeves CB, Palmer SL, Reddick WE et al (2006) Attention and memory functioning among pediatric patients with medulloblastoma. J Pediatr Psychol 31:272–280.  https://doi.org/10.1093/jpepsy/jsj019 CrossRefGoogle Scholar
  9. 9.
    Knight SJ, Conklin HM, Palmer SL et al (2014) Working memory abilities among children treated for medulloblastoma: parent report and child performance. J Pediatr Psychol 39:501–511.  https://doi.org/10.1093/jpepsy/jsu009 CrossRefGoogle Scholar
  10. 10.
    Aukema EJ, Caan MWA, Oudhuis N et al (2009) White matter fractional anisotropy correlates with speed of processing and motor speed in young childhood cancer survivors. Int J Radiat Oncol 74:837–843.  https://doi.org/10.1016/j.ijrobp.2008.08.060 CrossRefGoogle Scholar
  11. 11.
    Palmer SL (2008) Neurodevelopmental impact on children treated for medulloblastoma: a review and proposed conceptual model. Dev Disabil Res Rev 14:203–210CrossRefGoogle Scholar
  12. 12.
    Ris MD, Walsh K, Wallace D et al (2013) Intellectual and academic outcome following two chemotherapy regimens and radiotherapy for average-risk medulloblastoma: COG A9961. Pediatr Blood Cancer 60:1350–1357.  https://doi.org/10.1002/pbc.24496 CrossRefGoogle Scholar
  13. 13.
    Hardy KK, Bonner MJ, Willard VW et al (2008) Hydrocephalus as a possible additional contributor to cognitive outcome in survivors of pediatric medulloblastoma. Psychooncology 17:1157–1161.  https://doi.org/10.1002/pon.1349 CrossRefGoogle Scholar
  14. 14.
    Mulhern RK, Palmer SL, Merchant TE et al (2005) Neurocognitive consequences of risk-adapted therapy for childhood medulloblastoma. J Clin Oncol 23:5511–5519.  https://doi.org/10.1200/JCO.2005.00.703 CrossRefGoogle Scholar
  15. 15.
    Walsh KS, Noll RB, Annett RD et al (2016) Standard of care for neuropsychological monitoring in pediatric neuro-oncology: lessons from the Children’s Oncology Group (COG). Pediatr Blood Cancer 63:191–195.  https://doi.org/10.1002/pbc.25759 CrossRefGoogle Scholar
  16. 16.
    Lim YY, Ellis KA, Harrington K et al (2012) Use of the CogState brief battery in the assessment of Alzheimer’s disease related cognitive impairment in the Australian Imaging, Biomarkers and Lifestyle (AIBL) study. J Clin Exp Neuropsychol 34:345–358.  https://doi.org/10.1080/13803395.2011.643227 CrossRefGoogle Scholar
  17. 17.
    Maruff P, Thomas E, Cysique L et al (2009) Validity of the CogState brief battery: relationship to standardized tests and sensitivity to cognitive impairment in mild traumatic brain injury, schizophrenia, and AIDS dementia complex. Arch Clin Neuropsychol 24:165–178.  https://doi.org/10.1093/arclin/acp010 CrossRefGoogle Scholar
  18. 18.
    Collie A, Maruff P, Makdissi M et al (2003) CogSport: reliability and correlation with conventional cognitive tests used in postconcussion medical evaluations. Clin J Sport Med 13:28–32CrossRefGoogle Scholar
  19. 19.
    Pietrzak RH, Olver J, Norman T et al (2009) A comparison of the CogState Schizophrenia Battery and the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Battery in assessing cognitive impairment in chronic schizophrenia. J Clin Exp Neuropsychol 31:848–859.  https://doi.org/10.1080/13803390802592458 CrossRefGoogle Scholar
  20. 20.
    Ichimura S, Ohira T, Kobayashi M et al (2010) Assessment of cognitive function before and after surgery for posterior cranial fossa lesions using computerized and conventional tests. Neurol Med Chir (Tokyo) 50:441–448CrossRefGoogle Scholar
  21. 21.
    Caine C, Deshmukh S, Gondi V et al (2016) CogState computerized memory tests in patients with brain metastases: secondary endpoint results of NRG Oncology RTOG 0933. J Neuro Oncol N Y 126:327–336CrossRefGoogle Scholar
  22. 22.
    Dingwall KM, Cairney S (2010) Psychological and cognitive assessment of indigenous Australians. Aust N Z J Psychiatry 44:20–30.  https://doi.org/10.3109/00048670903393670 CrossRefGoogle Scholar
  23. 23.
    Lewis MS, Dingwall KM, Berkhout N et al (2010) Assessment of cognition in an adolescent Indigenous population. Aust Psychol 45:123–131.  https://doi.org/10.1080/00050060903352998 CrossRefGoogle Scholar
  24. 24.
    Falleti MG, Maruff P, Collie A, Darby DG (2006) Practice effects associated with the repeated assessment of cognitive function using the CogState battery at 10-minute, one week and one month test-retest intervals. J Clin Exp Neuropsychol 28:1095–1112.  https://doi.org/10.1080/13803390500205718 CrossRefGoogle Scholar
  25. 25.
    Boivin MJ, Busman RA, Parikh SM et al (2010) A pilot study of the neuropsychological benefits of computerized cognitive rehabilitation in Ugandan children with HIV. Neuropsychology 24:667–673.  https://doi.org/10.1037/a0019312 CrossRefGoogle Scholar
  26. 26.
    Bangirana P, Giordani B, John CC et al (2009) Immediate neuropsychological and behavioral benefits of computerized cognitive rehabilitation in ugandan pediatric cerebral malaria survivors. J Dev Behav Pediatr 30:310–318.  https://doi.org/10.1097/DBP.0b013e3181b0f01b CrossRefGoogle Scholar
  27. 27.
    Snyder AM, Maruff P, Pietrzak RH et al (2008) Effect of treatment with stimulant medication on nonverbal executive function and visuomotor speed in children with attention deficit/hyperactivity disorder (ADHD). Child Neuropsychol 14:211–226.  https://doi.org/10.1080/09297040701220005 CrossRefGoogle Scholar
  28. 28.
    Sands SA, Harel BT, Savone M et al (2017) Feasibility of baseline neurocognitive assessment using Cogstate during the first month of therapy for childhood leukemia. Support Care Cancer 25:449–457.  https://doi.org/10.1007/s00520-016-3422-9 CrossRefGoogle Scholar
  29. 29.
    Lai J, Zelko F, Krull KR et al (2014) Parent-reported cognition of children with cancer and its potential clinical usefulness. Qual Life Res 23:1049–1058CrossRefGoogle Scholar
  30. 30.
    Taylor MD, Northcott PA, Korshunov A et al (2012) Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathol (Berl) 123:465–472.  https://doi.org/10.1007/s00401-011-0922-z CrossRefGoogle Scholar
  31. 31.
    Barratt W (2006) The Barratt simplified measure of social status (BSMSS). Indiana State University, Terre HauteGoogle Scholar
  32. 32.
    Wechsler D (2003) Wechsler intelligence scale for children (WISC-IV). Psychological Corporation, San AntonioGoogle Scholar
  33. 33.
    Wechsler D (2014) Wechsler adult intelligence scale—Fourth Edition (WAIS–IV). Psychological Corporation, San AntonioGoogle Scholar
  34. 34.
    Woodcock RW, McGrew KS, Mather N (2001) Woodcock-Johnson III tests of cognitive ability. Riverside Publishing, ItascaGoogle Scholar
  35. 35.
    Conners KC (2006) Conners’ kiddie continuous performance test. Multi-Health Systems, North TonawandaGoogle Scholar
  36. 36.
    Conners KC (2004) Conners’ continuous performance test II. Pearson Corporation, San AntonioGoogle Scholar
  37. 37.
    Delis DC, Kramer JH, Kaplan E, Ober BA (2000) California verbal learning test, second edition (CVLT-II). Psychological Corporation, San AntonioGoogle Scholar
  38. 38.
    Delis DC, Kramer JH, Kaplan E, Ober BA (1994) California verbal learning test, children’s version. Psychological Corporation, San AntonioGoogle Scholar
  39. 39.
    Gioia G, Isquith P, Guy S, Kenworthy L (2000) behavior rating inventory of executive function. Psychological Assessment Resources, OdessaGoogle Scholar
  40. 40.
    Kamphaus RW, Reynolds CR (2007) BASC-2 behavioral and emotional screening system manual. Pearson, Circle PinesGoogle Scholar
  41. 41.
    Turken U, Whitfield-Gabrieli S, Bammer R et al (2008) Cognitive processing speed and the structure of white matter pathways: Convergent evidence from normal variation and lesion studies. NeuroImage 42:1032–1044.  https://doi.org/10.1016/j.neuroimage.2008.03.057 CrossRefGoogle Scholar
  42. 42.
    Moore BD (2005) Neurocognitive outcomes in survivors of childhood cancer. J Pediatr Psychol 30:51–63.  https://doi.org/10.1093/jpepsy/jsi016 CrossRefGoogle Scholar
  43. 43.
    Reddick WE, Glass JO, Palmer SL et al (2005) Atypical white matter volume development in children following craniospinal irradiation. Neuro-oncology 7:12–19.  https://doi.org/10.1215/S1152851704000079 CrossRefGoogle Scholar
  44. 44.
    Mulhern RK, Kepner JL, Thomas PR et al (1998) Neuropsychologic functioning of survivors of childhood medulloblastoma randomized to receive conventional or reduced-dose craniospinal irradiation: a Pediatric Oncology Group study. J Clin Oncol 16:1723–1728.  https://doi.org/10.1200/JCO.1998.16.5.1723 CrossRefGoogle Scholar
  45. 45.
    Grill J, Renaux VK, Bulteau C et al (1999) Long-term intellectual outcome in children with posterior fossa tumors according to radiation doses and volumes. Int J Radiat Oncol Biol Phys 45:137–145.  https://doi.org/10.1016/S0360-3016(99)00177-7 CrossRefGoogle Scholar
  46. 46.
    Netson KL, Conklin HM, Wu S et al (2012) A 5-year investigation of children’s adaptive functioning following conformal radiation therapy for localized ependymoma. Int J Radiat Oncol Biol Phys 84:217–223.e1.  https://doi.org/10.1016/j.ijrobp.2011.10.043 CrossRefGoogle Scholar
  47. 47.
    Schatz J, Kramer JH, Ablin A, Matthay KK (2000) Processing speed, working memory, and IQ: a developmental model of cognitive deficits following cranial radiation therapy. Neuropsychology 14:189–200.  https://doi.org/10.1037/0894-4105.14.2.189 CrossRefGoogle Scholar
  48. 48.
    Toplak ME, West RF, Stanovich KE (2013) Practitioner Review: Do performance-based measures and ratings of executive function assess the same construct? J Child Psychol Psychiatry 54:131–143.  https://doi.org/10.1111/jcpp.12001 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Andrew M. Heitzer
    • 1
  • Jason M. Ashford
    • 1
  • Brian T. Harel
    • 2
  • Adrian Schembri
    • 3
  • Michelle A. Swain
    • 4
  • Joanna Wallace
    • 5
  • Kirsten K. Ness
    • 6
  • Fang Wang
    • 7
  • Hui Zhang
    • 7
  • Thomas E. Merchant
    • 8
  • Giles W. Robinson
    • 9
  • Amar Gajjar
    • 9
  • Heather M. Conklin
    • 1
    Email author
  1. 1.Department of PsychologySt. Jude Children’s Research HospitalMemphisUSA
  2. 2.Takeda Pharmaceuticals International CoCambridgeUSA
  3. 3.Cogstate LimitedNew HavenUSA
  4. 4.Paediatric Rehabilitation ServiceLady Cilento Children’s HospitalBribaneAustralia
  5. 5.Division of Child NeurologyStanford University/Lucile Packard Children’s HospitalPalo AltoUSA
  6. 6.Department of Epidemiology and Cancer ControlSt. Jude Children’s Research HospitalMemphisUSA
  7. 7.Department of BiostatisticsSt. Jude Children’s Research HospitalMemphisUSA
  8. 8.Department of Radiation OncologySt. Jude Children’s Research HospitalMemphisUSA
  9. 9.Department of OncologySt. Jude Children’s Research HospitalMemphisUSA

Personalised recommendations