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A network-based response feature matrix as a brain injury metric

  • Shaoju Wu
  • Wei Zhao
  • Bethany Rowson
  • Steven Rowson
  • Songbai JiEmail author
Original Paper

Abstract

Conventional brain injury metrics are scalars that treat the whole head/brain as a single unit but do not characterize the distribution of brain responses. Here, we establish a network-based “response feature matrix” to characterize the magnitude and distribution of impact-induced brain strains. The network nodes and edges encode injury risks to the gray matter regions and their white matter interconnections, respectively. The utility of the metric is illustrated in injury prediction using three independent, real-world datasets: two reconstructed impact datasets from the National Football League (NFL) and Virginia Tech, respectively, and measured concussive and non-injury impacts from Stanford University. Injury predictions with leave-one-out cross-validation are conducted using the two reconstructed datasets separately, and then by combining all datasets into one. Using support vector machine, the network-based injury predictor consistently outperforms four baseline scalar metrics including peak maximum principal strain of the whole brain (MPS), peak linear/rotational acceleration, and peak rotational velocity across all five selected performance measures (e.g., maximized accuracy of 0.887 vs. 0.774 and 0.849 for MPS and rotational acceleration with corresponding positive predictive values of 0.938, 0.772, and 0.800, respectively, using the reconstructed NFL dataset). With sufficient training data, real-world injury prediction is similar to leave-one-out in-sample evaluation, suggesting the potential advantage of the network-based injury metric over conventional scalar metrics. The network-based response feature matrix significantly extends scalar metrics by sampling the brain strains more completely, which may serve as a useful framework potentially allowing for other applications such as characterizing injury patterns or facilitating targeted multi-scale modeling in the future.

Keywords

Concussion Brain structural network Traumatic brain injury Support vector machine Worcester head injury model 

Notes

Acknowledgements

Funding is provided by the NIH Grant R01 NS092853. The authors are grateful to Dr. David B. Camarillo at Stanford University for data sharing. They also thank Dr. Zheyang Wu at Worcester Polytechnic Institute for help on statistical analysis.

Compliance with ethical standards

Conflict of interest

We have no competing interests.

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© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Biomedical EngineeringWorcester Polytechnic InstituteWorcesterUSA
  2. 2.Department of Biomedical Engineering and MechanicsVirginia TechBlacksburgUSA
  3. 3.Department of Mechanical EngineeringWorcester Polytechnic InstituteWorcesterUSA

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