Multiphysics modeling of neuronal mechanobiology - an integrated computational framework for investigating mechanics and microstructure underlying neuronal injury
Abstract
Approved for Public ReleaseTraumatic brain injury (TBI) is a disruption of the normal function of the brain that can be caused due t o external forces under conditions of impact, blast or penetrating head injury. TBI is one of the leading causes of death and disabi lity, and each year more than 1.5 million Americans sustain a TBI. A 2008 RAND corporation report indicates that as many as 19 perce nt of returning veterans suffer from probable TBI, and unless identified and treated, this can have long-term, cascading consequence s on their health. TBI often results in significant physical and cognitive degradation, and also leads to emotional, behavioral, and social traumas. While severe TBI detrimentally effect consciousness, memory and functioning, cases of mild TBI (mTBI) or concussion are known to result in persistent and longtime effects that result from delayed brain degradation. This is especially the case with concussions occurring in contact sports and war zones. This delayed brain degradation is often in the form of injury to the microsc opic nerve fibers in the brain and is very difficult to diagnose using current radiology techniques like CT and MRI. The primary rea son behind the limitation of diagnoses is the inherent complex multiscale organization of the brain matter that spans many orders of length-scales: from the tissue scale (10-1m), to the axon, dendritic spines and synapses (10-6m), to the sub-neuronal microstructur e of cytoskeleton and cell organelles (10-9m). The challenges with diagnoses of TBI, the need to predict longtime effects, and the l imitations on conducting brain experiments have resulted in a widespread interest in predictive modeling of TBI. However, current st ate of the art in TBI modeling is significantly limited in its predictive ability due to the use of coarse-scale brain material mode to model and characterize the mechanical deformation and damage response of a single neuron by considering the neuronal microstruct ure and microenvironment that are rel- evant to its structural integrity and its ionic homeostasis through a first-of-its-kind compu tational multiphysics framework. This framework models the coupling of the neuron-scale mechanics with the underlying chemical trans port and signaling in the intra-cellular and inter-cellular load bear- ing neuronal microstructure (cytoskeleton, cell membrane, tra nsmembrane receptors and ECM). Modeling this coupling will enable a systematic approach to understand the effect of loads on the neu ronal microstructure, to identify the mechanotransduction pathways that are triggered, and to relate the mechanotransduction pathway s to neuronal damage. The development of this mul- tiphysics framework relies on solving coupled parti ling mechanics, reactions and transport using advanced numerical modeling (FEM) techniques. Thekey steps in the development of this framework for modeling neuronal injury are: (1) developing a continuum- scale multiphysics model of single neuron mechanobiology, (2 ) simulating mechanical loading of a single neuron using this framework to characterize the mechano-chemical response of the neurona l load bearing constituents, and (3) integrating this neuron-scale multiphysics framework within the multiscale hierarchy of the ONR PANTHER program to enable predictive modeling capability for neuronal injury.The completion of the project aims will enable high fi delity modeling of neuronal and sub- neuronal injuries, and permit the linking of neuronal injury with diffuse pathological damage o bserved in TBI. This will provide fundamental insights into the evolution of TBI and potentially help identify neuron-scale therapeu
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 03, 2021
- Source ID
- N000142112918
Entities
People
- Shiva Rudraraju
Organizations
- Office of Naval Research
- United States Navy
- University of Wisconsin System