Biomechanics and Modeling in Mechanobiology

, Volume 16, Issue 5, pp 1709–1727 | Cite as

Injury prediction and vulnerability assessment using strain and susceptibility measures of the deep white matter

Original Paper


Reliable prediction and diagnosis of concussion is important for its effective clinical management. Previous model-based studies largely employ peak responses from a single element in a pre-selected anatomical region of interest (ROI) and utilize a single training dataset for injury prediction. A more systematic and rigorous approach is necessary to scrutinize the entire white matter (WM) ROIs as well as ROI-constrained neural tracts. To this end, we evaluated injury prediction performances of the 50 deep WM regions using predictor variables based on strains obtained from simulating the 58 reconstructed American National Football League head impacts. To objectively evaluate performance, repeated random subsampling was employed to split the impacts into independent training and testing datasets (39 and 19 cases, respectively, with 100 trials). Univariate logistic regressions were conducted based on training datasets to compute the area under the receiver operating characteristic curve (AUC), while accuracy, sensitivity, and specificity were reported based on testing datasets. Two tract-wise injury susceptibilities were identified as the best overall via pair-wise permutation test. They had comparable AUC, accuracy, and sensitivity, with the highest values occurring in superior longitudinal fasciculus (SLF; 0.867–0.879, 84.4–85.2, and 84.1–84.6%, respectively). Using metrics based on WM fiber strain, the most vulnerable ROIs included genu of corpus callosum, cerebral peduncle, and uncinate fasciculus, while genu and main body of corpus callosum, and SLF were among the most vulnerable tracts. Even for one un-concussed athlete, injury susceptibility of the cingulum (hippocampus) right was elevated. These findings highlight the unique injury discriminatory potentials of computational models and may provide important insight into how best to incorporate WM structural anisotropy for investigation of brain injury.


Concussion Deep white matter Fiber strain Injury susceptibility Tractography 



Funding is provided by the NIH Grants R01 NS092853 and R21 NS088781. The authors are grateful to the National Football League (NFL) Committee on Mild Traumatic Brain Injury (MTBI) and Biokinetics and Associates Ltd. for providing the reconstructed head impact kinematics. The authors thank Dr. Thomas W. McAllister at Indiana University for providing neuroimaging data, Dr. James C. Ford and Dr. Laura A. Flashman at Dartmouth College, and Dr. Richard M. Greenwald and Mr. Jonathan Beckwith at Simbex, LLC, for their help. In addition, they thank Dr. Qiang Liu at Dartmouth College, and Ms. Chiara Giordano at Royal Institute of Technology (KTH) for helpful discussions.

Conflict of interest

The authors declare that they have no competing interests.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Wei Zhao
    • 1
  • Yunliang Cai
    • 1
  • Zhigang Li
    • 2
  • Songbai Ji
    • 1
    • 3
    • 4
  1. 1.Department of Biomedical EngineeringWorcester Polytechnic InstituteWorcesterUSA
  2. 2.Department of Biomedical Data Science, Geisel School of MedicineDartmouth CollegeLebanonUSA
  3. 3.Department of Mechanical EngineeringWorcester Polytechnic InstituteWorcesterUSA
  4. 4.Thayer School of EngineeringDartmouth CollegeHanoverUSA

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