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This proposal is a novel investigation of the role of astrocytes and myelin in regulating the functionality of injured neurons that combines experimental data from Atomic Force Microscopy (AFM) measurements with in-silico computational (finite element and machine learning) studies. Injury to axons in white matter (WM) remains a primary cause of many of the functional deficits that follow traumatic brain injury (TBI). Understanding how axons are injured mechanically will pave the way for designing measures to prevent their injury.WM is, however, complex. Composed of oriented, wavy, fibrous axons that are embedded within a glial matrix. To date, much of the research concerned with modeling TBI considers the brain to be a homogenous bulk material even though it is injury to individual axons that collectively lead to loss of function. Furthermore, very little is known about the role of astrocytes in protecting and repairing the axons after a TBI event.
Based on published AFM measurements of neurons and astrocytes, we will develop a finite element (FE) model of an axon surrounded by myelin and attached to a neuron and astrocyte. The axon will be modeled using Bézier curves which allow generating fiber paths with nonlinear angle variation. The material properties for the axon, astrocyte and neuron will be calibrated using force-displacement data from published AFM data. The material model comprises a hyperelastic network accounting for the instantaneous cell response and viscoelastic components capturing the strain rate effects at both short- and long-time scales. Using an optimization algorithm, the parameters of the material model will be tuned to match the measured force.
Within the validated model, we will introduce variations in the diameter of the axon, modify its path by altering the Bezier curves, introduce and vary the amount of myelin, vary the astrocyte volume, among others. We will run FE simulations for each variation in the model and extract the output force. The measured output force should reflect the changes in the about parameters. The simulations will serve as training data for a deep learning algorithm that ultimately replaces the FE models. The objective is to explore the use of Recurrent Neural Networks (RNN), a type of artificial neural network to process time series data of the measured force mapped to the FE geometry. The RNN will serve as a surrogate model in predicting the mechanical response of the axons, neuron and astrocyte.
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