Access Type

Open Access Dissertation

Date of Award

January 2015

Degree Type


Degree Name



Biomedical Engineering

First Advisor

John Cavanaugh

Second Advisor

Liying Zhang


In the United States 1.4 million people sustain traumatic brain injury (TBI) each year, resulting in 235,000 hospitalizations and 50,000 fatalities annually. Traumatic axonal injury (TAI) is a serious outcome of TBI that accounts for 40-50% of hospitalizations due to head injury and one third of the mortality due to TBI, and it is difficult to diagnose and evaluate. The purpose of this dissertation is to determine mechanical injury predictors for TAI and identify potential biomarkers to evaluate TAI.

In this dissertation, a modified Marmarou impact acceleration injury model was developed to allow the monitoring of velocity of the impactor and characterization of head kinematics during impact. The rat head sustained linear acceleration and angular velocity of 918±281g and 116±45 rad/sec, respectively in 2.25m impacts, and 609±142g and 98±31 rad/sec, respectively in 1.25m impacts. The variability in head kinematics resulting from the same drop height suggested that monitoring of mechanical parameters are critical factors for illustration of the level of closed head injury with this model. Using this modified impact acceleration model, a series studies were performed to investigate correlation between impact mechanics and TAI, as well as correlation between biomarker levels and TAI.

In the first part of this dissertation, thirty-one anesthetized male Sprague-Dawley rats (392 ± 13 grams) were impacted using a modified impact acceleration injury device from 2.25 m and 1.25 m heights. Beta-amyloid precursor protein (β-APP) immunocytochemistry was used to assess and quantify axonal changes in CC and Py. Linear and angular responses of the rat head were monitored and measured in vivo with an attached accelerometer and angular rate sensor, and were correlated to TAI data. Logistic regression analysis suggested that the occurrence of severe TAI in CC was best predicted by average linear acceleration, followed by Power and time to surface righting. The combination of average linear acceleration and time to surface righting showed an improved predictive result. In Py, severe TAI was best predicted by time to surface righting, followed by peak and average angular velocity. When both CC and Py were combined, power was the best predictor, and the combined average linear acceleration and average angular velocity was also found to have good injury predictive ability.

In the second part of this dissertation, tweenty-four anesthetized male Sprague-Dawley rats were subjected to a closed head injury from 1.25, 1.75 and 2.25 m drop heights (n=8 for each group). 24 h after impact, cerebrospinal fluid (CSF) and serum were collected. CSF and serum levels of neurofilament H (NF-H), glial fibrillary acidic protein (GFAP), interleukin (IL)-6, and amyloid beta (Aβ) 1-42 were assessed by enzyme-linked immunosorbent assay (ELISA). Compared to controls, significantly higher CSF and serum pNF-H levels were observed in all impact groups, except between 1.25 m and control in serum. Furthermore, CSF and serum pNF-H levels were significantly different between the impact groups. For GFAP, both CSF and serum levels were significantly higher at 2.25 m compared to 1.75 m, 1.25 m and controls. There was no significant difference in CSF and serum GFAP levels between 1.75 m and 1.25 m, although both groups were significantly higher than control. TBI rats also showed significantly higher levels of IL-6 versus control in both CSF and serum, but no significant difference was observed between each impact group. Levels of Aβ were not significantly different between groups. Logistic regression analysis suggested that both pNF-H and GFAP levels in CSF and serum were good biomarkers for severe TBI. Pearson's correlation analysis showed pNF-H and GFAP levels in CSF and serum had positive correlation with power (rate of impact energy), followed by average linear acceleration and surface righting (p<0.01), which were good predictors for traumatic axonal injury (TAI) according to histologic assessment in first part study, suggesting that they are directly related to the injury mechanism.