In order to restore lost function or to aid the regenerative capacity in the brain after traumatic brain injury (TBI) or neural degenerative disease, micro-tissue engineered neural networks (micro-TENNs) are a possible replacement to existing, less effective methods. Micro-TENNs consist of a bundle of neurons grown cylindrically in vitro to mimic axonal tracts in the brain so they can later be transplanted into patients. The Computational Biomechanics Group at Penn State University has begun the process of creating computational models of these micro-TENNs to gather data on how micro-TENNs will transmit neuron signals once implanted. By identifying key geometric characteristics of the living micro-TENNs, statistical metrics summarize the morphology. MATLAB programs can identify the same statistical metrics in the computational model and can compare them to the experimental data. Understanding how these metrics vary between experimental data and the model can aid in describing the accuracy and usability of the computational model.
