Empirical quantification and computational modeling of spine stability and neuromuscular function during dynamic movements.

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The global objective of my research program is to utilize novel techniques to better understand which factors contribute mechanistically to spine injury and impairment. The goal of this grant cycle is to focus specifically on stability, since stability is a fundamental concept that can be used to characterize and evaluate the functioning of a system. A key feature to stability and appropriate neuromuscular function is the ability to effectively respond to internal and external mechanical perturbations, in order to restore an equilibrium posture or movement trajectory during motion. Spine stability is the result of a complex interaction between the osteoligamentous spine, the trunk musculature, and the neural control system; with impairment to any one subsystem, small perturbations can result in the unsuccessful transmission of compressive and shear forces and tissue strain and/or injury. However, despite the knowledge that spine stability is important, its quantification is difficult using presently available imaging, manual testing, and biomechanical modeling techniques. Moreover, to date no empirical method allows measuring stability in static and all the more in dynamic conditions.One promising method assessing spine stability and neuromuscular function during dynamic movements is to calculate local dynamic spine stability from trunk motion data using a nonlinear dynamical systems approach. During repetitive trunk movements it is reasonable to assume that each movement cycle would be similar to every other cycle and the target kinematic trajectory or attractor. Naturally-occurring variance observed in empirical data is thus attributable to mechanical disturbances or control errors that are attenuated in time by the musculoskeletal and nervous systems. Thus, it is logical to calculate stability from the time-dependent growth or attenuation of kinematic variability in state space using the maximum Lyapunov exponent. The proposed research program will build on my previous work and has two overarching objectives: 1) to continue to improve the ability to empirically quantify and model spine stability and neuromuscular function in humans, and 2) to apply these techniques to a variety of movement scenarios to better understand how instability and impaired functioning may act as a biological or biomechanical mechanism of tissue failure and injury. As part of objective 1, we will carry out a series of modeling-based studies that are aimed at: i) further elucidating the relationship between local dynamic spine stability and other stability measures, ii) understanding the relationship between local spine stability and global trunk stability, and iii) beginning the process of modeling the entire Lyapunov spectrum from empirical data. With the knowledge gained, objective 2 will involve applying these advanced stability assessment techniques to various movement tasks to gain a greater understanding of how mechanical loading and tissue altering properties affect (in) stability and neuromuscular function, and vice versa. Lastly, we will assess the relationship between stability and other measures of neuromuscular function (e.g. coordination), in order to fully understand its contributions to healthy movement.As a whole, this combination of novel basic science and computational modeling has the potential to greatly benefit the Canadian natural sciences and engineering fields, as the empirical quantification of spine stability and neuromuscular function during dynamic movement will now be possible; providing us a better biological and mechanical understanding of how many anatomical, physiological, and biomechanical factors contribute mechanistically to tissue strain and/or injury during a variety of movement tasks and conditions.