ArticleBiomechanics and modeling in mechanobiology2020
A network-based response feature matrix as a brain injury metric.
Article in Biomechanics and modeling in mechanobiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 37 citations in OpenAlex.
- Computational Modeling of Bridging Vein Rupture and Acute Subdural Hematoma Growth.Annals of biomedical engineering · 2025Article
- Quantitative video analysis of head acceleration events: a review.Frontiers in bioengineering and biotechnology · 2025Review
- An integrated finite element method and machine learning algorithm for brain morphology prediction.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Use of Brain Biomechanical Models for Monitoring Impact Exposure in Contact Sports.Annals of biomedical engineering · 2022Review
- American Football Helmet Effectiveness Against a Strain-Based Concussion Mechanism.Annals of biomedical engineering · 2022Article
- Finding the Spatial Co-Variation of Brain Deformation With Principal Component Analysis.IEEE transactions on bio-medical engineering · 2022Article
- Real-time dynamic simulation for highly accurate spatiotemporal brain deformation from impact.Computer methods in applied mechanics and engineering · 2022Article
- Cerebral vascular strains in dynamic head impact using an upgraded model with brain material property heterogeneity.Journal of the mechanical behavior of biomedical materials · 2022Article
- Quantification of Head Acceleration Events in Rugby League: An Instrumented Mouthguard and Video Analysis Pilot Study.Sensors (Basel, Switzerland) · 2022Article
- Displacement voxelization to resolve mesh-image mismatch: Application in deriving dense white matter fiber strains.Computer methods and programs in biomedicine · 2022Article
- The relationship between brain injury criteria and brain strain across different types of head impacts can be different.Journal of the Royal Society, Interface · 2021Article
- Instantaneous Whole-Brain Strain Estimation in Dynamic Head Impact.Journal of neurotrauma · 2021Article
- Ranking and Rating Bicycle Helmet Safety Performance in Oblique Impacts Using Eight Different Brain Injury Models.Annals of biomedical engineering · 2021Article
- A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion.Frontiers in bioengineering and biotechnology · 2021Article
- Multiscale Mechanobiology of Brain Injury: Axonal Strain Redistribution.Biophysical journal · 2020Article
- Displacement- and Strain-Based Discrimination of Head Injury Models across a Wide Range of Blunt Conditions.Annals of biomedical engineering · 2020Article
- Low-Rank Representation of Head Impact Kinematics: A Data-Driven Emulator.Frontiers in bioengineering and biotechnology · 2020Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.