Multi-modal Registration Improves Group Discrimination in Pediatric Traumatic Brain Injury

  • Emily L. DennisEmail author
  • Faisal Rashid
  • Julio Villalon-Reina
  • Gautam Prasad
  • Joshua Faskowitz
  • Talin Babikian
  • Richard Mink
  • Christopher Babbitt
  • Jeffrey Johnson
  • Christopher C. Giza
  • Robert F. Asarnow
  • Paul M. Thompson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10154)


Traumatic brain injury (TBI) can disrupt the white matter (WM) integrity in the brain, leading to functional and cognitive disruptions that may persist for years. There is considerable heterogeneity within the patient group, which complicates group analyses. Here we present improvements to a tract identification workflow, automated multi-atlas tract extraction (autoMATE), evaluating the effects of improved registration. Use of study-specific template improved group classification accuracy over the standard workflow. The addition of a multi-modal registration that includes information from diffusion weighted imaging (DWI), T1-weighted, and Fluid-Attenuated Inversion Recovery (FLAIR) further improved classification accuracy. We also examined whether particular tracts contribute more to group classification than others. Parts of the corpus callosum contributed most, and there were unexpected asymmetries between bilateral tracts.


Traumatic Brain Injury Fractional Anisotropy Diffusion Weighted Imaging Traumatic Brain Injury Patient White Matter Integrity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study was supported by the NICHDS (R01 HD061504). ELD is supported by a grant from the NINDS (K99 NS096116). ELD, FR, JV, GP, JF and PT are also supported by NIH grants to PT: U54 EB020403, R01 EB008432, R01 AG040060, and R01 NS080655. CCG is supported by the UCLA BIRC, NS027544, NS05489, Child Neurology Foundation, and the Jonathan Drown Foundation. Scanning was supported by the Staglin IMHRO Center for Cognitive Neuroscience. We gratefully acknowledge the contributions of Alma Martinez and Alma Ramirez in assisting with recruitment and study coordination. Finally, the authors thank the participants and their families for contributing their time.


  1. 1.
    Parizel, P., Özsarlak, Ö., Van Goethem, J., Van Den Hauwe, L., Dillen, C., Verlooy, J., Cosyns, P., De Schepper, A.: Imaging findings in diffuse axonal injury after closed head trauma. Eur. Radiol. 8, 960–965 (1998)CrossRefGoogle Scholar
  2. 2.
    Ashikaga, R., Araki, Y., Ishida, O.: MRI of head injury using FLAIR. Neuroradiology 39, 239–242 (1997)CrossRefGoogle Scholar
  3. 3.
    Ashwal, S., Holshouser, B.A., Tong, K.A.: Use of advanced neuroimaging techniques in the evaluation of pediatric traumatic brain injury. Dev. Neurosci. 28, 309–326 (2006)CrossRefGoogle Scholar
  4. 4.
    Xu, J., Rasmussen, I.-A., Lagopoulos, J., Håberg, A.: Diffuse axonal injury in severe traumatic brain injury visualized using high-resolution diffusion tensor imaging. J. Neuroatraum 24, 753–765 (2007)CrossRefGoogle Scholar
  5. 5.
    Dennis, E.L., Jin, Y., Villalon-Reina, J., Zhan, L., Kernan, C., Babikian, T., Mink, R., Babbitt, C., Johnson, J., Giza, C.C.: White matter disruption in moderate/severe pediatric traumatic brain injury: advanced tract-based analyses. NeuroImage Clin. 7, 493–505 (2015)CrossRefGoogle Scholar
  6. 6.
    Dennis, E.L., Ellis, M.U., Marion, S.D., Jin, Y., Moran, L., Olsen, A., Kernan, C., Babikian, T., Mink, R., Babbitt, C., Johnson, J., Giza, C.C., Thompson, P.M., Asarnow, R.F.: Callosal function in pediatric traumatic brain injury linked to disrupted white matter integrity. J. Neurosci. 35, 10202–10211 (2015)CrossRefGoogle Scholar
  7. 7.
    Dennis, E.L., Rashid, F., Ellis, M.U., Babikian, T., Villalon-Reina, J.E., Jin, Y., Olsen, A., Mink, R., Babbitt, C., Johnson, J., Giza, C.C., Thompson, P.M., Asarnow, R.F.: Diverging White Matter Trajectories in Children after Traumatic Brain Injury: The RAPBI Study Neurology. In Press, New York (2016)Google Scholar
  8. 8.
    Dennis, E., Jin, Y., Villalon-Reina, J., Kernan, C., Babikian, T., Mink, R., Babbitt, C., Johnson, J., Giza, C., Asarnow, R.F., Thompson, P.: Tract-based analysis of white matter integrity in pediatric TBI: mapping individual abnormalities. In: Organization for Human Brain Mapping. Honolulu, HI (2015)Google Scholar
  9. 9.
    Jin, Y., Shi, Y., Zhan, L., de Zubicaray, G.I., McMahon, K.L., Martin, N.G., Wright, M.J., Thompson, P.M.: Labeling white matter tracts in hardi by fusing multiple tract atlases with applications to genetics. In: Proceedings of 10th IEEE ISBI, pp. 512–515, San Francisco, CA (2013)Google Scholar
  10. 10.
    Jin, Y., Shi, Y., Zhan, L., Gutman, B., de Zubicaray, G.I., McMahon, K.L., Wright, M.J., Toga, A.W., Thompson, P.M.: Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics. NeuroImage 100, 75–90 (2014)CrossRefGoogle Scholar
  11. 11.
    Jin, Y., Shi, Y., Zhan, L., Li, J., Zubicaray, Greig, I., McMahon, Katie, L., Martin, Nicholas, G., Wright, Margaret, J., Thompson, Paul, M.: Automatic population hardi white matter tract clustering by label fusion of multiple tract atlases. In: Yap, P.-T., Liu, T., Shen, D., Westin, C.-F., Shen, L. (eds.) MBIA 2012. LNCS, vol. 7509, pp. 147–156. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33530-3_12 CrossRefGoogle Scholar
  12. 12.
    Zhang, Y., Zhang, J., Oishi, K., Faria, A.V., Jiang, H., Li, X., Akhter, K., Rosa-Neto, P., Pike, G.B., Evans, A., Toga, A.W., Woods, R., Mazziotta, J.C., Miller, M.I., van Zijl, P.C.M., Mori, S.: Atlas-guided tract reconstruction for automated and comprehensive examination of the white matter anatomy. NeuroImage 52, 1289–1301 (2010)CrossRefGoogle Scholar
  13. 13.
    Catani, M., Allin, M.P., Husain, M., Pugliese, L., Mesulam, M.M., Murray, R.M., Jones, D.K.: Symmetries in human brain language pathways correlate with verbal recall. Proc. Natl. Acad. Sci. 104, 17163–17168 (2007)CrossRefGoogle Scholar
  14. 14.
    Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage 54, 2033–2044 (2011)CrossRefGoogle Scholar
  15. 15.
    Kim, J., Avants, B., Patel, S., Whyte, J., Coslett, B.H., Pluta, J., Detre, J.A., Gee, J.C.: Structural consequences of diffuse traumatic brain injury: a large deformation tensor-based morphometry study. NeuroImage 39, 1014–1026 (2008)CrossRefGoogle Scholar
  16. 16.
    Jin, Y., Shi, Y., Jahanshad, N., Aganj, I., Sapiro, G., Toga, A.W., Thompson, P.M.: 3D elastic registration improves HARDI-derived fiber alignment and automated tract clustering. In: 8th Proceedings of IEEE International Symposium on Biomed Imaging, Chicago, IL, pp. 822–826. IEEE (2011)Google Scholar
  17. 17.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)zbMATHGoogle Scholar
  18. 18.
    Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI, pp. 1137–1145 (1995)Google Scholar
  19. 19.
    Weiss, N., Galanaud, D., Carpentier, A., Naccache, L., Puybasset, L.: Clinical review: prognostic value of magnetic resonance imaging in acute brain injury and coma. Crit. Care 11, 230 (2007)CrossRefGoogle Scholar
  20. 20.
    Irimia, A., Wang, B., Aylward, S.R., Prastawa, M.W., Pace, D.F., Gerig, G., Hovda, D.A., Kikinis, R., Vespa, P.M., Van Horn, J.D.: Neuroimaging of structural pathology and connectomics in traumatic brain injury: toward personalized outcome prediction. Neuroimage Clin. 1, 1–17 (2012)CrossRefGoogle Scholar
  21. 21.
    Wang, B., Prastawa, M., Irimia, A., Chambers, M.C., Sadeghi, N., Vespa, P.M., Van Horn, J.D., Gerig, G.: Analyzing imaging biomarkers for traumatic brain injury using 4D modeling of longitudinal MRI. In: 2013 IEEE 10th International Symposium on Biomedical Imaging, pp. 1392–1395 (2013)Google Scholar
  22. 22.
    Wang, B., Liu, W., Prastawa, M., Irimia, A., Vespa, P.M., van Horn, J.D., Fletcher, P.T. and Gerig, G.: 4D active cut: an interactive tool for pathological anatomy modeling. In: Proceedings/IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 529–532 (2014)Google Scholar
  23. 23.
    Hulkower, M.B., Poliak, D.B., Rosenbaum, S.B., Zimmerman, M.E., Lipton, M.L.: A decade of DTI in traumatic brain injury: 10 years and 100 articles later. AJNR. Am. J. Neuroradiol. 34(11), 2064–2074 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Emily L. Dennis
    • 1
    Email author
  • Faisal Rashid
    • 1
  • Julio Villalon-Reina
    • 1
  • Gautam Prasad
    • 1
  • Joshua Faskowitz
    • 1
  • Talin Babikian
    • 2
  • Richard Mink
    • 3
  • Christopher Babbitt
    • 4
  • Jeffrey Johnson
    • 5
  • Christopher C. Giza
    • 6
  • Robert F. Asarnow
    • 2
    • 7
    • 8
  • Paul M. Thompson
    • 1
    • 2
    • 9
  1. 1.Imaging Genetics Center, Keck USC School of MedicineMarina del ReyUSA
  2. 2.Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human BehaviorUCLALos AngelesUSA
  3. 3.Department of PediatricsHarbor-UCLA Medical Center and Los Angeles BioMedical Research InstituteTorranceUSA
  4. 4.Miller Children’s HospitalLong BeachUSA
  5. 5.Department of PediatricsLAC+USC Medical CenterLos AngelesUSA
  6. 6.Department of Neurosurgery and Division of Pediatric Neurology, UCLA Brain Injury Research CenterMattel Children’s HospitalLos AngelesUSA
  7. 7.Department of PsychologyUCLALos AngelesUSA
  8. 8.Brain Research InstituteUCLALos AngelesUSA
  9. 9.Departments of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and OphthalmologyUSCLos AngelesUSA

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