Age and Gender-Dependent Group-Average Brain Biomechanics Models for Traumatic Brain Injury

Abstract

Experimental studies involving mechanical loading of the human head and brain in vivo are necessarily limited, making computational modeling essential for advancing our understanding of brain biomechanics. Demographic factors such as age and gender are known to influence brain anatomical structures, material properties, and potentially vulnerability to injurious loading. To address this, we construct six group-average brain models stratified by age and gender from a total of 135 subjects, and investigate the mechanical responses of these "group-average brains" using computational simulations. We use Pearson correlations to assess to what degree the group-average models represent individuals within each demographic category, showing strong correlations. Further, our p-value hypothesis test of the first principal strain across the six groups shows significant differences. This study demonstrates that age- and gender-stratified group-average models can effectively represent biomechanical responses of the individuals within the groups, and can reveal meaningful demographic differences that may influence susceptibility to traumatic brain injury. We show that the age-dependent change in material properties plays a greater role than anatomical changes in driving differences in the deformations. We hope to see increased utilization of these group-average models in both research and clinical applications.