Experimental and computational characterization of three-dimensional cervical spine flexibility

Area of Science:

• Biomechanics
• Spinal Mechanics
• Orthopedics

Background:

• Cervical spine behavior is modeled using 3D flexibility matrices, but experimental limitations and simplifications (omitted coupled motions, constant flexibility terms) hinder accuracy.
• Existing models lack validation for predicting vertebral motions and quantifying the impact of approximations in flexibility matrix representations.
• The flexibility matrix is foundational for multibody dynamics models of cervical spine motion.

Purpose of the Study:

• To fully quantify flexibility relationships in human cervical spine motion segments.
• To examine both primary and coupled motions within the flexibility matrix components.
• To determine if multivariable relationships enhance cervical spine motion prediction accuracy.

Main Methods:

• Performed flexibility tests on unembalmed human cervical spine motion segments (C3-C4, C5-C6) under neutral and pre-torqued conditions.
• Characterized matrix components using linear and piecewise nonlinear incremental constants, and multivariable incremental relationships.
• Predicted measured motions using structural flexibility methods and evaluated accuracy via RMS error.

Main Results:

• Established comprehensive flexibility relationships for C3-C4 and C5-C6 motion segments, detailing primary and coupled motions.
• Flexibility matrices employing incremental responses significantly improved motion prediction compared to linear methods (p<0.01).
• No significant improvement in prediction accuracy was observed with generalized nonlinear multivariable functional approaches (p<0.2).

Conclusions:

• Incremental response-based flexibility matrices enhance cervical spine motion prediction accuracy.
• Complex multivariable functional approaches for cervical spine flexibility are analytically and experimentally demanding without yielding superior predictive outcomes.
• Current modeling of cervical spine behavior can be improved using incremental, rather than purely linear or overly complex nonlinear, flexibility representations.