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Neuropsychological Recovery Trajectories in Moderate to Severe Traumatic Brain Injury: Influence of Patient Characteristics and Diffuse Axonal Injury

Published online by Cambridge University Press:  16 October 2017

Amanda R. Rabinowitz ,
Tessa Hart ,
John Whyte  and
Junghoon Kim
Amanda R. Rabinowitz*
Affiliation:
Moss Rehabilitation Research Institute, Elkins Park, Pennsylvania CUNY School of Medicine, The City College of New York, New York, New York
Tessa Hart
Affiliation:
Moss Rehabilitation Research Institute, Elkins Park, Pennsylvania
John Whyte
Affiliation:
Moss Rehabilitation Research Institute, Elkins Park, Pennsylvania
Junghoon Kim
Affiliation:
Moss Rehabilitation Research Institute, Elkins Park, Pennsylvania
*
Correspondence and reprint requests to: Amanda Rabinowitz, Moss Rehabilitation Research Institute, 50 Township Line Road, Elkins Park, PA 19027. E-mail: [email protected]
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Abstract

Objectives: The goal of the present study was to elucidate the influence of demographic and neuropathological moderators on the longitudinal trajectory neuropsychological functions during the first year after moderate to severe traumatic brain injury (TBI). In addition to examining demographic moderators such as age and education, we included a measure of whole-brain diffuse axonal injury (DAI), and examined measures of processing speed (PS), executive function (EF), and verbal learning (VL) separately. Methods: Forty-six adults with moderate to severe TBI were examined at 3, 6, and 12 months post-injury. Participants underwent neuropsychological evaluation and neuroimaging including diffusion tensor imaging. Using linear mixed effects modeling, we examined longitudinal trajectories and moderating factors of cognitive outcomes separately for three domains: PS, VL, and EF. Results: VL and EF showed linear improvements, whereas PS exhibited a curvilinear trend characterized by initial improvements that plateaued or declined, depending on age. Age moderated the recovery trajectories of EF and PS. Education and DAI did not influence trajectory but were related to initial level of functioning for PS and EF in the case of DAI, and all three cognitive domains in the case of education. Conclusions: We found disparate recovery trajectories across cognitive domains. Younger age was associated with more favorable recovery of EF and PS. These findings have both clinical and theoretical implications. Future research with a larger sample followed over a longer time period is needed to further elucidate the factors that may influence cognitive change over the acute to chronic period after TBI. (JINS, 2018, 24, 237–246)

Keywords

Brain injuryNeuropsychological testsCognitionExecutive functionVerbal learningDiffusion tensor imaging
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 24 , Issue 3 , March 2018 , pp. 237 - 246
Copyright
Copyright © The International Neuropsychological Society 2017 

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References

REFERENCES

Baayen, R.H., Davidson, D.J., & Bates, D.M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59(4), 390–412.Google Scholar
Beglinger, L.J., Gaydos, B., Tangphao-Daniels, O., Duff, K., Kareken, D.A., Crawford, J., & Siemers, E.R. (2005). Practice effects and the use of alternate forms in serial neuropsychological testing. Archives of Clinical Neuropsychology, 20(4), 517–529.CrossRefGoogle ScholarPubMed
Benton, L., Hamsher, K., & Sivan, A. (1994). Controlled oral word association test. Multilingual aphasia examination. Iowa City, IA: AJA Associates.Google Scholar
Bigler, E.D., Johnson, S.C., Anderson, C.V., Blatter, D.D., Gale, S.D., Russo, A.A., &Hopkins, R.O. (1996). Traumatic brain injury and memory: The role of hippocampal atrophy. Neuropsychology, 10(3), 333.Google Scholar
Christensen, B.K., Colella, B., Inness, E., Hebert, D., Monette, G., Bayley, M., & Green, R.E. (2008). Recovery of cognitive function after traumatic brain injury: A multilevel modeling analysis of Canadian outcomes. Archives of Physical Medicine and Rehabilitation, 89(12), S3–S15.Google Scholar
Corrigan, J.D., & Hammond, F.M. (2013). Traumatic brain injury as a chronic health condition. Archives of Physical Medicine and Rehabilitation, 94(6), 1199–1201.Google Scholar
Dawson, D.R., & Chipman, M. (1995). The disablement experienced by traumatically brain-injured adults living in the community. Brain Injury, 9(4), 339–353.CrossRefGoogle ScholarPubMed
Delis, D.C., Kaplan, E., & Kramer, J.H. (2001). Delis-Kaplan executive function system (D-KEFS). San Antonio, TX: Psychological Corporation.Google Scholar
Dikmen, S.S., Corrigan, J.D., Levin, H.S., Machamer, J., Stiers, W., & Weisskopf, M.G. (2009). Cognitive outcome following traumatic brain injury. The Journal of Head Trauma Rehabilitation, 24(6), 430–438.CrossRefGoogle ScholarPubMed
Farbota, K.D., Bendlin, B.B., Alexander, A.L., Rowley, H.A., Dempsey, R.J., & Johnson, S.C. (2012). Longitudinal diffusion tensor imaging and neuropsychological correlates in traumatic brain injury patients. Frontiers in Human Neuroscience, 6, 160.Google Scholar
Fitzmaurice, G.M., Laird, N.M., & Ware, J.H. (2012). Applied longitudinal analysis, (Vol. 998). New York: John Wiley & Sons.Google Scholar
Green, R.E., Colella, B., Christensen, B., Johns, K., Frasca, D., Bayley, M., & Monette, G. (2008). Examining moderators of cognitive recovery trajectories after moderate to severe traumatic brain injury. Archives of Physical Medicine and Rehabilitation, 89(12), S16–S24.Google Scholar
Håberg, A., Olsen, A., Moen, K., Schirmer‐Mikalsen, K., Visser, E., Finnanger, T., & Eikenes, L. (2015). White matter microstructure in chronic moderate‐to‐severe traumatic brain injury: Impact of acute‐phase injury‐related variables and associations with outcome measures. Journal of Neuroscience Research, 93(7), 1109–1126.Google Scholar
Hellawell, R., Taylor, B., & Pentland, D.J. (1999). Cognitive and psychosocial outcome following moderate or severe traumatic brain injury. Brain Injury, 13(7), 489–504.Google Scholar
Himanen, L., Portin, R., Isoniemi, H., Helenius, H., Kurki, T., & Tenovuo, O. (2006). Longitudinal cognitive changes in traumatic brain injury: A 30-year follow-up study. Neurology, 66(2), 187–192.Google Scholar
Hoofien, D., Vakil, E., Gilboa, A., Donovick, P.J., & Barak, O. (2002). Comparison of the predictive power of socio-economic variables, severity of injury and age on long-term outcome of traumatic brain injury: Sample-specific variables versus factors as predictors. Brain Injury, 16(1), 9–27.Google Scholar
Hurvich, C.M., & Tsai, C.-L. (1989). Regression and time series model selection in small samples. Biometrika, 297–307.Google Scholar
Jackson, W.T., Novack, T.A., & Dowler, R.N. (1998). Effective serial measurement of cognitive orientation in rehabilitation: the Orientation Log. Archives of Physical Medicine and Rehabilitation, 79(6), 718–721.Google Scholar
Kesler, S.R., Adams, H.F., Blasey, C.M., & Bigler, E.D. (2003). Premorbid intellectual functioning, education, and brain size in traumatic brain injury: An investigation of the cognitive reserve hypothesis. Applied Neuropsychology, 10(3), 153–162.Google Scholar
Kraus, M.F., Susmaras, T., Caughlin, B.P., Walker, C.J., Sweeney, J.A., & Little, D.M. (2007). White matter integrity and cognition in chronic traumatic brain injury: A diffusion tensor imaging study. Brain, 130(10), 2508–2519.Google Scholar
Kumar, R., Gupta, R.K., Husain, M., Chaudhry, C., Srivastava, A., Saksena, S., & Rathore, R.K. (2009). Comparative evaluation of corpus callosum DTI metrics in acute mild and moderate traumatic brain injury: its correlation with neuropsychometric tests. Brain Injury, 23(7-8), 675–685.Google Scholar
Lannoo, E., Colardyn, F., Jannes, C., & De Soete, G. (2001). Course of neuropsychological recovery from moderate-to-severe head injury: A 2-year follow-up. Brain Injury, 15(1), 1–13.Google Scholar
Lezak, M., Howieson, D., Bigler, E., & Tranel, D. (2012). Neuropsychological assessment (5th ed.). New York, NY: Oxford University Press.Google Scholar
Lezak, M. (2004). Neuropsychological assessment. New York, NY: Oxford University Press.Google Scholar
Lipton, M.L., Kim, N., Park, Y.K., Hulkower, M.B., Gardin, T.M., Shifteh, K., & Branch, C.A. (2012). Robust detection of traumatic axonal injury in individual mild traumatic brain injury patients: intersubject variation, change over time and bidirectional changes in anisotropy. Brain Imaging and Behavior, 6(2), 329–342.Google Scholar
Mayer, A.R., Bedrick, E.J., Ling, J.M., Toulouse, T., & Dodd, A. (2014). Methods for identifying subject‐specific abnormalities in neuroimaging data. Human Brain Mapping, 35(11), 5457–5470.Google Scholar
Newcombe, V., Chatfield, D., Outtrim, J., Vowler, S., Manktelow, A., Cross, J., & Menon, D. (2011). Mapping traumatic axonal injury using diffusion tensor imaging: Correlations with functional outcome. PLoS One, 6(5), e19214.Google Scholar
Niogi, S.N., Mukherjee, P., Ghajar, J., Johnson, C.E., Kolster, R., Lee, H., & McCandliss, B.D. (2008). Structural dissociation of attentional control and memory in adults with and without mild traumatic brain injury. Brain, 131(12), 3209–3221.CrossRefGoogle ScholarPubMed
Novack, T.A., Bush, B.A., Meythaler, J.M., & Canupp, K. (2001). Outcome after traumatic brain injury: pathway analysis of contributions from premorbid, injury severity, and recovery variables. Archives of Physical Medicine and Rehabilitation, 82(3), 300–305.Google Scholar
Palacios, E.M., Sala-Llonch, R., Junque, C., Fernandez-Espejo, D., Roig, T., Tormos, J.M., & Vendrell, P. (2013). Long-term declarative memory deficits in diffuse TBI: correlations with cortical thickness, white matter integrity and hippocampal volume. Cortex, 49(3), 646–657.Google Scholar
Ponsford, J., Draper, K., & Schönberger, M. (2008). Functional outcome 10 years after traumatic brain injury: its relationship with demographic, injury severity, and cognitive and emotional status. Journal of the International Neuropsychological Society, 14(02), 233–242.Google Scholar
Ponsford, J.L., Downing, M.G., Olver, J., Ponsford, M., Acher, R., Carty, M., & Spitz, G. (2014). Longitudinal follow–up of patients with traumatic brain injury: Outcome at two, five, and ten years post-injury. Journal of Neurotrauma, 31(1), 64–77.Google Scholar
Rabinowitz, A.R., & Smith, D.H. (2016). Traumatic brain injury and rationale for a neuropsychological diagnosis of diffuse axonal injury. In G.T. Orly Lazarov (Ed.), Genes, environment and Alzheimer’s disease (pp. 267–293). Cambridge, MA: Elsevier.CrossRefGoogle Scholar
Reitan, R.M., & Wolfson, D. (1985). The Halstead-Reitan neuropsychological test battery: Theory and clinical interpretation (Vol. 4): Reitan neuropsychology. New York: Springer.Google Scholar
Ruff, R.M., Marshall, L.F., Crouch, J., Klauber, M.R., Levin, H.S., Barth, J., & Eisenberg, H.M. (1993). Predictors of outcome following severe head trauma: Follow-up data from the Traumatic Coma Data Bank. Brain Injury, 7(2), 101–111. doi: 10.3109/02699059309008164 Google Scholar
Salthouse, T.A. (2010). Selective review of cognitive aging. Journal of the International Neuropsychological Society, 16(5), 754.Google Scholar
Schretlen, D.J., & Shapiro, A.M. (2003). A quantitative review of the effects of traumatic brain injury on cognitive functioning. International Review of Psychiatry, 15(4), 341–349.Google Scholar
Schultz, R., & Tate, R.L. (2013). Methodological issues in longitudinal research on cognitive recovery after traumatic brain injury: Evidence from a systematic review. Brain Impairment, 14(3).Google Scholar
Senathi-Raja, D., Ponsford, J., & Schönberger, M. (2010). Impact of age on long-term cognitive function after traumatic brain injury. Neuropsychology, 24(3), 336.Google Scholar
Sherer, M., Stouter, J., Hart, T., Nakase-Richardson, R., Olivier, J., Manning, E., &Yablon, S.A. (2006). Computed tomography findings and early cognitive outcome after traumatic brain injury. Brain Injury, 20(10), 997–1005.Google Scholar
Spitz, G., Maller, J.J., O’Sullivan, R., & Ponsford, J.L. (2013). White matter integrity following traumatic brain injury: the association with severity of injury and cognitive functioning. Brain Topography, 26(4), 648–660.Google Scholar
Stern, Y. (2002). What is cognitive reserve? Theory and research application of the reserve concept. Journal of the International Neuropsychological Society, 8(03), 448–460.Google Scholar
Ware, J.B., Hart, T., Whyte, J., Rabinowitz, A., Detre, J.A., & Kim, J. (2017). Inter-subject variability of axonal injury in diffuse traumatic brain injury. Journal of Neurotrauma, 34(14), 2243–2253.Google Scholar
Watts, R., Thomas, A., Filippi, C.G., Nickerson, J.P., & Freeman, K. (2014). Potholes and molehills: Bias in the diagnostic performance of diffusion-tensor imaging in concussion. Radiology, 272(1), 217–223.Google Scholar
Wechsler, D. (2014). Wechsler adult intelligence scale–fourth edition (WAIS–IV). San Antonio, TX: Pearson.Google Scholar
White, T., Magnotta, V.A., Bockholt, H.J., Williams, S., Wallace, S., Ehrlich, S., & Lim, K.O. (2009). Global white matter abnormalities in schizophrenia: A multisite diffusion tensor imaging study. Schizophrenia Bulletin, 37, 222–232.Google Scholar
Whiteneck, G., Brooks, C., Mellick, D., Harrison-Felix, C., Terrill, M.S., & Noble, K. (2004). Population-based estimates of outcomes after hospitalization for traumatic brain injury in Colorado. Archives of Physical Medicine and Rehabilitation, 85, 73–81.Google Scholar
Wilde, E.A., Whiteneck, G.G., Bogner, J., Bushnik, T., Cifu, D.X., Dikmen, S., & von Steinbuechel, N. (2010). Recommendations for the use of common outcome measures in traumatic brain injury research. Archives of Physical Medicine and Rehabilitation, 91(11), 1650–1660. e1617.Google Scholar
Wood, R.L., & Rutterford, N.A. (2006). Demographic and cognitive predictors of long-term psychosocial outcome following traumatic brain injury. Journal of the International Neuropsychological Society, 12(03), 350–358.Google Scholar
Yuh, E.L., Cooper, S.R., Mukherjee, P., Yue, J.K., Lingsma, H.F., & Gordon, W.A., . . . TRACK-TBI INVESTIGATORS. (2014). Diffusion tensor imaging for outcome prediction in mild traumatic brain injury: A TRACK-TBI study. Journal of Neurotrauma, 31(17), 1457–1477.Google Scholar