A computational model for recovery from traumatic brain injury

Abstract

A computational simulation model calculates estimated recovery trajectories following traumatic brain injury (TBI). Prior publications include a multi-scale conceptual framework for studying concussion, a systems-level causal loop diagram (CLD) and an analysis of key feedback processes. A set of first order ordinary differential equations and their associated parameters determines recovery trajectories. The model contains 15 state variables, 73 auxiliary variables, and 50 parameters describing TBI pathology in an aggregate fashion at the cellular, network, cognitive and social levels. There are 1200 feedback loops, which give rise to a variety of behavior modes, many of which are highly nonlinear. Exogenous parameters include patient and injury characteristics, treatments, and time constants for recovery processes. Model testing has focused on reviewing the causal diagram with subject matter experts and determining sensitivity of model results to injury severity and patient characteristics, especially the time constants associated with healing/recovery processes. The model produces outcome trajectories that represent quick or slow recovery with no deficits, partial recovery, and the patient remaining indefinitely in a pathological state. While highly speculative, the model serves to demonstrate the potential utility of computational models in this context and to further discussion about the complex dynamics involved in recovery from TBI. The model also generates counterintuitive results, as is characteristic of complex systems. Much more research will be needed to create a properly supported research model that could be used or for precision medicine or to aid clinical trial design.