A Mechanistic End-to-End Concussion Model That Translates Head Kinematics to Neurologic Injury

Area of Science:

• Biomechanics
• Neuroscience
• Computational Modeling

Background:

• Concussion research traditionally focuses on isolated length scales, hindering a holistic understanding of injury mechanisms.
• A multi-scale approach is crucial for connecting macroscopic head motion to cellular-level neurological outcomes.

Purpose of the Study:

• To develop the first quantitative end-to-end (E2E) multi-scale model of concussion.
• To integrate tissue and cellular mechanics with axonal dysfunction to predict neurological injury.

Main Methods:

• Developed a quantitative E2E multi-scale model linking head motion to neurological injury.
• Quantified axonal stretch and parameterized axonal functionality by axonal signaling.
• Calculated a neurological injury measure based on axonal signal amplitude reduction in the corpus callosum.

Main Results:

• The E2E model successfully quantified axonal stretch injury in the corpus callosum.
• Neurological injury was parameterized by the average reduction in axonal signal amplitude.
• The model demonstrated greater sensitivity and specificity in unifying concussion data across species and conditions compared to external measures.

Conclusions:

• The developed E2E model provides a mechanistic framework for understanding concussion.
• It quantitatively links corpus callosum injury to neurobehavioral outcomes relevant to mild traumatic brain injury.
• This framework can improve concussion risk assessment, protective equipment design, and safety standards.