Multiscale Computational Model of Traumatic Brain Injury
Traumatic brain injury (TBI) is a debilitating injury that is caused by mechanical loading of the head. One of the main pathological features of TBI is damage to the neural axons within the white matter of the brain. These neural axons serve as information highways, and when these pathways of communication are damaged, cognitive impairments can result.
Computational models can provide useful insight into the mechanisms that lead to TBI, aiding in the development of better methods for mitigation and treatment. We have developed a computational modeling approach that can be used to estimate the location and extent of axonal injury in the brain. The model is constructed from magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) of a single subject and maintains a high degree of biofidelity. A multi-scale material model is incorporated to accurately represent the deformation of the brain tissue, and damage is defined based on an axonal stretch injury criterion. Acceleration loading curves obtained from accident reconstruction data or accelerometer impact data are applied to the model, and the degree of axonal injury is estimated through a finite element analysis. The model can be applied to predict the total extent of axonal injury in the brain and to identify the fiber tracts with the greatest amount of axonal damage.
Video: Acceleration loading curves were obtained from an accident reconstruction of an impact that resulted in concussive injury. The impact reconstruction was conducted by the Neurotrauma Impact Science Laboratory, University of Ottawa. The accelerations were applied to our computational model, and the resulting axonal injury was estimated. The locations of axonal injury predicted from the computational analysis are shown in red for a axial section of the brain. The total area of injury was analyzed in 130 segmented regions of the brain.
One of the difficulties in studying mild traumatic brain injuries, such as concussions, is that the structural signature of injury often cannot be visualized with conventional medical imaging modalities (e.g. MRI, CT, etc.). This is mainly because the damage occurs at the microscale. As a result, clinicians often turn to neurocognitive assessments to diagnose concussive injury. Neurocognitive assessments are useful in identifying the cognitive impairments that result from a concussion; however, computational models could potentially be used as an additional tool to provide an objective measure of the locations of axonal injury and the extent of injury in the brain. This information can be applied in the management of the concussion (e.g. deciding how long a concussed player should wait before resuming participation in his/her sport), and it can be used to guide the treatment and rehabilitation of the injury.
Wright, R., K.T. Ramesh, A. Post, B. Hoshizaki, "A multiscale computational approach to estimating axonal damage under inertial loading of the head," J Neurotrauma, doi:10.1089/neu.2012.2418 (Sept 2012). pdf
Microfluidic Compression Platform to Study Single Axon Injury
We have demonstrated that the injury response of neural axons (primary rat embryonic CNS) is sensitive to the magnitude of the applied compressive load. At low levels of compression (< 55 kPa), ~73% of axons continued to grow, while at moderate levels of compression (55-95 kPa), the number of growing axons dramatically reduced to 8%. At high levels of compression (> 95 kPa), virtually all axons were instantaneously transected and nearly half (~46%) of these axons were able to regrow. This study suggests that there are cellular injury thresholds that govern the balance between axon degeneration and regrowth.
Videos: (Top) The degeneration of a neural axon following focal compression with the microfluidic injury device is shown. (Bottom) The transection and regrowth of a neural axon following severe focal compression is shown.
Determining the cellular thresholds for axonal injury is a critical step in developing an understanding of the mechanisms that govern axon degeneration and regrowth. Furthermore, these injury thresholds can be applied to computational models (such as the one described above) as a measure of injury, improving the predictive capabilities of the model. These models can then be used as a platform to develop methods for mitigating and treating injury.
Hosemane, S., A. Fournier, R. Wright, L. Rajbhandari, R. Siddique, I. Yang, K.T. Ramesh, A. Venkatesan, N. Thakor, "Valve-based microfluidic compression platform: single axon injury and regrowth," Lab on a Chip, doi: 10.1039/c11c20549h (Sept 2011). pdf