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Fiber Tracking in Traumatic Brain Injury: Comparison of 9 Tractography Algorithms

  • Emily L. DennisEmail author
  • Gautam Prasad
  • Madelaine Daianu
  • Liang Zhan
  • Talin Babikian
  • Claudia Kernan
  • Richard Mink
  • Christopher Babbitt
  • Jeffrey Johnson
  • Christopher C. Giza
  • Robert F. Asarnow
  • Paul M. Thompson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9556)

Abstract

Traumatic brain injury (TBI) can cause widespread and long-lasting damage to white matter. Diffusion weighted imaging methods are uniquely sensitive to this disruption. Even so, traumatic injury often disrupts brain morphology as well, complicating the analysis of brain integrity and connectivity, which are typically evaluated with tractography methods optimized for analyzing normal healthy brains. To understand which fiber tracking methods show promise for analysis of TBI, we tested 9 different tractography algorithms for their classification accuracy and their ability to identify vulnerable areas as candidates for longitudinal follow-up in pediatric TBI participants and matched controls. Deterministic tractography models yielded the highest classification accuracies, but their limitations in areas of extensive fiber crossing suggested that they generated poor candidates for longitudinal follow-up. Probabilistic methods, including a method based on the Hough transform, yielded slightly lower accuracy, but generated follow-up candidate connections more coherent with the known neuropathology of TBI.

Keywords

Traumatic Brain Injury Fractional Anisotropy Traumatic Brain Injury Patient Diffuse Axonal Injury Connectivity Matrice 
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.

Notes

Acknowledgements

This study was supported by the NICHDS (R01 HD061504). ELD, YJ, 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 participant recruitment and study coordination. Finally, the authors thank the participants and their families for contributing their time to this study.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Emily L. Dennis
    • 1
    Email author
  • Gautam Prasad
    • 1
  • Madelaine Daianu
    • 1
  • Liang Zhan
    • 1
  • Talin Babikian
    • 2
  • Claudia Kernan
    • 2
  • Richard Mink
    • 3
  • Christopher Babbitt
    • 4
  • Jeffrey Johnson
    • 5
  • Christopher C. Giza
    • 6
  • Robert F. Asarnow
    • 2
    • 7
  • Paul M. Thompson
    • 1
    • 2
    • 8
  1. 1.Imaging Genetics CenterKeck USC School of MedicineMarina del ReyUSA
  2. 2.Department of Psychiatry and Biobehavioral SciencesSemel Institute for Neuroscience and Human Behavior, UCLALos 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.UCLA Brain Injury Research Center, Department of Neurosurgery and Division of Pediatric NeurologyMattel Children’s HospitalLos AngelesUSA
  7. 7.Department of PsychologyUCLALos AngelesUSA
  8. 8.Departments of Neurology, Pediatrics, Psychiatry, Radiology, Engineering, and OphthalmologyUSCLos AngelesUSA

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