Regression Analysis of Brain Biomechanics Under Uniaxial Deformation

Abstract

Traumatic brain injury is one of the most prevalent health conditions in the United States. However, despite its significance and frequency there is not that much understanding of the mechanism that controls the brain response during injurious loading. Because brain testing conditions are different between several assessment methods, this is considered as a confounding problem as brain biomechanics cannot be analyzed and understood completely. Multivariate linear regression has been applied in this article as a statistical method to expound the correlations between brain biomechanical response and in vitro brain testing conditions under uniaxial deformation. Neighborhood component analysis has been used to extract ten relevant continuous parameters, namely, age, strain rate, diameter, thickness, length, width, height, storage temperature, testing temperature, and post-mortem preservation time, five different categorical parameters, namely, stress condition, species, specimen location, brain matter composition, and geometry. In addition, multivariate regression model has been estimated with the storage, loss, and complex moduli as the responses. Intercept, strain rate, gray brain matter, and white brain matter have been discovered to be the most consistently significant parameters across the three response variables.