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Feedback learning and behavior problems after pediatric traumatic brain injury

Published online by Cambridge University Press:  08 March 2016

M. Königs ,
L. W. E. van Heurn ,
R. J. Vermeulen ,
J. C. Goslings ,
J. S. K. Luitse ,
B. T. Poll-Thé ,
A. Beelen ,
M. van der Wees ,
R. J. J. K. Kemps  and
C. E. Catsman-Berrevoets
...Show all authors
M. Königs*
Affiliation:
Department of Clinical Neuropsychology, VU University Amsterdam, Amsterdam, The Netherlands
L. W. E. van Heurn
Affiliation:
Pediatric Surgical Center of Amsterdam, Emma Children's Hospital Academic Medical Center and VU University Medical Center, Amsterdam, The Netherlands
R. J. Vermeulen
Affiliation:
Department of Pediatric Neurology, VU University Medical Center, Amsterdam, The Netherlands
J. C. Goslings
Affiliation:
Trauma Unit, Academic Medical Center, Amsterdam, The Netherlands
J. S. K. Luitse
Affiliation:
Department of Emergency Medicine, Academic Medical Center, Amsterdam, The Netherlands
B. T. Poll-Thé
Affiliation:
Department of Pediatric Neurology, Emma Children's Hospital Academic Medical Center, Amsterdam, The Netherlands
A. Beelen
Affiliation:
Department of Rehabilitation, Academic Medical Center, Amsterdam, The Netherlands Merem Rehabilitation Center ‘De Trappenberg’, Huizen, The Netherlands
M. van der Wees
Affiliation:
Libra Rehabilitation Center ‘Blixembosch’, Eindhoven, The Netherlands
R. J. J. K. Kemps
Affiliation:
Libra Rehabilitation Center ‘Leijpark’, Tilburg, The Netherlands
C. E. Catsman-Berrevoets
Affiliation:
Department of Pediatric Neurology, Erasmus Medical Center, Rotterdam, The Netherlands
M. Luman
Affiliation:
Department of Clinical Neuropsychology, VU University Amsterdam, Amsterdam, The Netherlands Department of Methods, VU University Amsterdam, Amsterdam, The Netherlands
J. Oosterlaan
Affiliation:
Department of Clinical Neuropsychology, VU University Amsterdam, Amsterdam, The Netherlands Emma Children's Hospital Academic Medical Center, Amsterdam, The Netherlands
*
*Address for correspondence: M. Königs, M.Sc., Department of Clinical Neuropsychology, VU University Amsterdam, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands. (Email: [email protected])
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Abstract

Background

Feedback learning is essential for behavioral development. We investigated feedback learning in relation to behavior problems after pediatric traumatic brain injury (TBI).

Method

Children aged 6–13 years diagnosed with TBI (n = 112; 1.7 years post-injury) were compared with children with traumatic control (TC) injury (n = 52). TBI severity was defined as mild TBI without risk factors for complicated TBI (mildRF− TBI, n = 24), mild TBI with ⩾1 risk factor for complicated TBI (mildRF+ TBI, n = 51) and moderate/severe TBI (n = 37). The Probabilistic Learning Test was used to measure feedback learning, assessing the effects of inconsistent feedback on learning and generalization of learning from the learning context to novel contexts. The relation between feedback learning and behavioral functioning rated by parents and teachers was explored.

Results

No evidence was found for an effect of TBI on learning from inconsistent feedback, while the moderate/severe TBI group showed impaired generalization of learning from the learning context to novel contexts (p = 0.03, d = −0.51). Furthermore, the mildRF+ TBI and moderate/severe TBI groups had higher parent and teacher ratings of internalizing problems (p's ⩽ 0.04, d's ⩾ 0.47) than the TC group, while the moderate/severe TBI group also had higher parent ratings of externalizing problems (p = 0.006, d = 0.58). Importantly, poorer generalization of learning predicted higher parent ratings of externalizing problems in children with TBI (p = 0.03, β = −0.21) and had diagnostic utility for the identification of children with TBI and clinically significant externalizing behavior problems (area under the curve = 0.77, p = 0.001).

Conclusions

Moderate/severe pediatric TBI has a negative impact on generalization of learning, which may contribute to post-injury externalizing problems.

Keywords

Behavioral functioningfeedback learninggeneralization of learningpediatricstraumatic brain injury
Type
Original Articles
Information
Psychological Medicine , Volume 46 , Issue 7 , May 2016 , pp. 1473 - 1484
Copyright
Copyright © Cambridge University Press 2016 

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References

American College of Surgeons Committee on Trauma (2004). Advanced Trauma Life Support Program for Doctors, 7th edn. American College of Surgeons Committee on Trauma: Chicago, IL.Google Scholar
Aoki, Y, Inokuchi, R, Gunshin, M, Yahagi, N, Suwa, H (2012). Diffusion tensor imaging studies of mild traumatic brain injury: a meta-analysis. Journal of Neurology, Neurosurgery, and Psychiatry 83, 870–876.Google Scholar
Babikian, T, Asarnow, R (2009). Neurocognitive outcomes and recovery after pediatric TBI: meta-analytic review of the literature. Neuropsychology 23, 283–296.CrossRefGoogle ScholarPubMed
Bongers, IL, Koot, HM, van der Ende, J, Verhulst, FC (2008). Predicting young adult social functioning from developmental trajectories of externalizing behaviour. Psychological Medicine 38, 989–999.CrossRefGoogle ScholarPubMed
Breslau, J, Miller, E, Breslau, N, Bohnert, K, Lucia, V, Schweitzer, J (2009). The impact of early behavior disturbances on academic achievement in high school. Pediatrics 123, 1472–1476.Google Scholar
Broidy, LM, Nagin, DS, Tremblay, RE, Bates, JE, Brame, B, Dodge, KA, Fergusson, D, Horwood, JL, Loeber, R, Laird, R, Lynam, DR, Moffitt, TE, Pettit, GS, Vitaro, F (2003). Developmental trajectories of childhood disruptive behaviors and adolescent delinquency: a six-site, cross-national study. Developmental Psychology 39, 222–245.Google Scholar
Brown, G, Chadwick, O, Shaffer, D, Rutter, M, Traud, M (1981). A prospective study of children with head injuries: III. Psychiatric sequelae. Psychological Medicine 11, 63–78.Google Scholar
Donders, J, Wildeboer, MA (2004). Validity of the WCST-64 after traumatic brain injury in children. Clinical Neuropsychologist 18, 521–577.Google Scholar
Doya, K (2008). Modulators of decision making. Nature Neuroscience 11, 410–416.CrossRefGoogle ScholarPubMed
Feigin, VL, Theadom, A, Barker-Collo, S, Starkey, NJ, McPherson, K, Kahan, M, Dowell, A, Brown, P, Parag, V, Kydd, R, Jones, K, Jones, A, Ameratunga, S (2013). Incidence of traumatic brain injury in New Zealand: a population-based study. Lancet Neurology 12, 53–64.Google Scholar
Field, A (2009). Discovering Statistics using SPSS. Sage Publications: London.Google Scholar
Frank, MJ, Seeberger, LC, O'Reilly, RC (2004). By carrot or by stick: cognitive reinforcement learning in Parkinsonism. Science 306, 1940–1943.CrossRefGoogle ScholarPubMed
Gershman, S, Niv, Y (2015). Novelty and inductive generalization in human reinforcement learning. Topics in Cognitive Science 7, 391–415.Google Scholar
Gläscher, J, Daw, N, Dayan, P, O'Doherty, JP (2010). States versus rewards: dissociable neural prediction error signals underlying model-based and model-free reinforcement learning. Neuron 66, 585–595.Google Scholar
Gogtay, N, Giedd, JN, Lusk, L, Hayashi, KM, Greenstein, D, Vaituzis, AC, Nugent, TF, Herman, DH, Clasen, LS, Toga, AW, Rapoport, JL, Thompson, PM (2004). Dynamic mapping of human cortical development during childhood through early adulthood. Proceedings of the National Academy of Sciences of the United States of America 101, 8174–8179.Google Scholar
Hämmerer, D, Eppinger, B (2012). Dopaminergic and prefrontal contributions to reward-based learning and outcome monitoring during child development and aging. Developmental Psychology 48, 862–874.CrossRefGoogle ScholarPubMed
Kaufman, AS, Kaufman, JC, Baijgopal, RMJ (1996). Comparison of three WISC-III short forms, weighing psychometric, clinical and practical factors. Journal of Clinical Child Psychology 25, 97–105.Google Scholar
Kizilbash, A, Donders, J (1999). Latent structure of the Wisconsin Card Sorting Test after pediatric traumatic head injury. Child Neuropsychology 5, 224–229.Google Scholar
Larson, MJ, Kelly, KG, Stigge-Kaufman, DA, Schmalfuss, IM, Perlstein, WM (2007). Reward context sensitivity impairment following severe TBI: an event-related potential investigation. Journal of the International Neuropsychological Society 13, 615–625.Google Scholar
Levin, HS, Song, J, Scheibel, RS, Fletcher, JM, Harward, H, Lilly, M, Goldstein, F (1997). Concept formation and problem-solving following closed head injury in children. Journal of the International Neuropsychological Society 3, 598–607.CrossRefGoogle ScholarPubMed
Li, L, Liu, J (2013). The effect of pediatric traumatic brain injury on behavioral outcomes: a systematic review. Developmental Medicine and Child Neurology 55, 37–45.Google Scholar
Maia, TV, Frank, MJ (2011). From reinforcement learning models to psychiatric and neurological disorders. Nature Neuroscience 14, 154–162.Google Scholar
Max, JE, Koele, SL, Smith, WL Jr, Sato, Y, Lindgren, SD, Robin, DA, Arndt, S (1998). Psychiatric disorders in children and adolescents after severe traumatic brain injury: a controlled study. Child and Adolescent Psychiatry 37, 832–840.Google Scholar
Max, JE, Wilde, EA, Bigler, ED, Thompson, WK, MacLeod, M, Vasquez, AC, Merkley, TL, Hunter, JV, Chu, ZD, Yallampalli, R, Hotz, G, Chapman, SB, Yang, TT, Levin, HS (2012). Neuroimaging correlates of novel psychiatric disorders after pediatric traumatic brain injury. Child and Adolescent Psychiatry 51, 1208–1217.CrossRefGoogle ScholarPubMed
Roberts, RM, Mathias, JL, Rose, SE (2014). Diffusion tensor imaging (DTI) findings following pediatric non-penetrating TBI: a meta-analysis. Developmental Neuropsychology 39, 600–637.Google Scholar
Rosema, S, Crowe, L, Anderson, V (2012). Social function in children and adolescents after traumatic brain injury: a systematic review 1989–2011. Journal of Neurotrauma 29, 1277–1291.Google Scholar
Rushworth, MF, Behrens, TE (2008). Choice, uncertainty and value in prefrontal and cingulate cortex. Nature Neuroscience 11, 389–397.Google Scholar
Schmidt, AT, Hanten, GR, Li, X, Vasquez, AC, Wilde, EA, Chapman, SB, Levin, HS (2012). Decision making after pediatric traumatic brain injury: trajectory of recovery and relationship to age and gender. International Journal of Developmental Neuroscience 30, 225–230.Google Scholar
Schwartz, L, Taylor, HG, Drotar, D, Yeates, KO, Wade, SL, Stancin, T (2003). Long-term behavior problems following pediatric traumatic brain injury: prevalence, predictors, and correlates. Journal of Pediatric Psychology 28, 251–263.Google Scholar
Slawik, H, Salmond, CH, Taylor-Tavares, JV, Williams, GB, Sahakian, BJ, Tasker, RC (2009). Frontal vulnerability and executive deficits from raised intracranial pressure in child traumatic brain injury. Journal of Neurotrauma 26, 1891–1903.CrossRefGoogle ScholarPubMed
Slomine, BS, Gerring, JP, Grados, MA, Vasa, R, Brady, KD, Christensen, JR, Denckla, MB (2002). Performance on measures of “executive function” following pediatric traumatic brain injury. Brain Injury 16, 759–772.Google Scholar
SPSS Inc. (2013). IBM SPSS Statistics for Windows, version 22.0. IBM Corporation: Armonk, NY.Google Scholar
Statistics Netherlands (2006). Standaard onderwijsindeling 2006 (Education Categorization Standard) (http://www.cbs.nl/nl-NL/menu/methoden/classificaties/overzicht/soi/2006/default.htm). Accessed January 2016.Google Scholar
Sterne, JAC, White, IR, Carlin, JB, Spratt, M, Royston, P, Kenward, MG, Wood, AM, Carpenter, JR (2009). Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ (Clinical Research ed.) 338, b2393.Google Scholar
Stokes, TF, Baer, DM (1977). An implicit technology of generalization. Journal of Applied Behavior Analysis 10, 349–367.Google Scholar
Tabachnick, BG, Fidell, LS (2012). Using Multivariate Statistics: International Edition. Pearson: Boston, MA.Google Scholar
Tamminen, J, Davis, MH, Rastle, K (2015). From specific examples to general knowledge in language learning. Cognitive Psychology 79, 1–39.Google Scholar
Teasdale, G, Jennett, B (1976). Assessment and prognosis of coma after head injury. Acta Neurochirurgica 34, 45–55.Google Scholar
Timonen, M, Miettunen, J, Hakko, H, Zitting, P, Veijola, J, von Wendt, L, Räsänen, P (2002). The association of preceding traumatic brain injury with mental disorders, alcoholism and criminality: the Northern Finland 1966 Birth Cohort Study. Psychiatry Research 113, 217–226.Google Scholar
Van den Bos, W, Cohen, MX, Kahnt, T, Crone, Ea (2012). Striatum–medial prefrontal cortex connectivity predicts developmental changes in reinforcement learning. Cerebral Cortex 22, 1247–1255.Google Scholar
Van Duijvenvoorde, AC, Jansen, BR, Griffionen, ES, van der Molen, MW, Huizenga, HM (2013). Decomposing developmental differences in probabilistic feedback learning: a combined performance and heart-rate analysis. Biological Psychology 93, 175–183.Google Scholar
Verhulst, FC, van der Ende, J (2013). Handleiding ASEBA Vragenlijsten voor leeftijden 6 t/m 18 jaar: CBCL/6-18, YSR en TRF (Manual of ASEBA Questionnaires for ages 6 to 18: CBCL/6-18, YSR and TRF). ASEBA Nederland: Rotterdam.Google Scholar
Vos, PE, Battistin, L, Birbamer, G, Gerstenbrand, F, Potapov, A, Prevec, T, Stepan, ChA, Traubner, P, Twijnstra, A, Vecsei, L, von Wild, K; European Federation of Neurological Societies (2002). EFNS guideline on mild traumatic brain injury: report of an EFNS task force. European Journal of Neurology 9, 207–219.Google Scholar
Yeates, KO, Taylor, HG (2006). Behavior problems in school and their educational correlates among children with traumatic brain injury. Exceptionality 14, 141–154.Google Scholar
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