Predictive Factors of Kinematics in Traumatic Brain Injury from Head Impacts Based on Statistical Interpretation

Overview

Journal Ann Biomed Eng

Specialty Biomedical Engineering

Date 2021 Jul 10

PMID 34244908

Citations 5

Authors

Xianghao Zhan

Yiheng Li

Yuzhe Liu

August G Domel

Hossein Vahid Alizadeh

Zhou Zhou

Nicholas J Cecchi

Samuel J Raymond

Stephen Tiernan

Jesse Ruan

Saeed Barbat

Olivier Gevaert

Michael M Zeineh

Gerald A Grant

David B Camarillo

Affiliations

Soon will be listed here.

Abstract

Brain tissue deformation resulting from head impacts is primarily caused by rotation and can lead to traumatic brain injury. To quantify brain injury risk based on measurements of kinematics on the head, finite element (FE) models and various brain injury criteria based on different factors of these kinematics have been developed, but the contribution of different kinematic factors has not been comprehensively analyzed across different types of head impacts in a data-driven manner. To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in (1) the derivative order (angular velocity, angular acceleration, angular jerk), (2) the direction and (3) the power (e.g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events. Ordinary least squares regressions were built from kinematics factors to the 95% maximum principal strain (MPS95), and we compared zero-order correlation coefficients, structure coefficients, commonality analysis, and dominance analysis. The angular acceleration, the magnitude and the first power factors showed the highest predictive power for the majority of impacts including laboratory impacts, American football impacts, with few exceptions (angular velocity for MMA and NASCAR impacts). The predictive power of rotational kinematics about three directions (x: posterior-to-anterior, y: left-to-right, z: superior-to-inferior) of kinematics varied with different sports and types of head impacts.

Citing Articles

Adaptive Machine Learning Head Model Across Different Head Impact Types Using Unsupervised Domain Adaptation and Generative Adversarial Networks.

Zhan X, Sun J, Liu Y, Cecchi N, Le Flao E, Gevaert O IEEE Sens J. 2025; 24(5):7097-7106.

PMID: 39897708 PMC: 11781752. DOI: 10.1109/jsen.2023.3349213.

Position-based assessment of head impact frequency, severity, type, and location in high school American football.

Bagherian A, Abbasi Ghiri A, Ramzanpour M, Wallace J, Elashy S, Seidi M Front Bioeng Biotechnol. 2025; 12:1500786.

PMID: 39877265 PMC: 11772367. DOI: 10.3389/fbioe.2024.1500786.

Machine-learning-based head impact subtyping based on the spectral densities of the measurable head kinematics.

Zhan X, Li Y, Liu Y, Cecchi N, Raymond S, Zhou Z J Sport Health Sci. 2023; 12(5):619-629.

PMID: 36921692 PMC: 10466194. DOI: 10.1016/j.jshs.2023.03.003.

Assessment of brain injury characterization and influence of modeling approaches.

Yang S, Tang J, Nie B, Zhou Q Sci Rep. 2022; 12(1):13597.

PMID: 35948588 PMC: 9365784. DOI: 10.1038/s41598-022-16713-2.

Piecewise Multivariate Linearity Between Kinematic Features and Cumulative Strain Damage Measure (CSDM) Across Different Types of Head Impacts.

Zhan X, Li Y, Liu Y, Cecchi N, Gevaert O, Zeineh M Ann Biomed Eng. 2022; 50(11):1596-1607.

PMID: 35922726 DOI: 10.1007/s10439-022-03020-0.

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