Normative Segment-Specific Axial and Coronal Angulation Corridors of Subaxial Cervical Column in Axial Rotation

Study Design. In contrast to clinical studies wherein loading magnitudes are indeterminate, experiments permit controlled and quantifiable moment applications, record kinematics in multiple planes, and allow derivation of moment-angulation corridors. Axial and coronal moment-angulation corridors were determined at every level of the subaxial cervical spine, expressed as logarithmic functions, and level-specificity of range of motion and neutral zones were evaluated. Objective. Hypothesis: segmental primary axial and coupled coronal motions do not vary by level. Summary of Background Data. Although it is known that cervical spine responses are coupled, segment-specific corridors of axial and coronal kinematics under axial twisting moments from healthy normal spines are not reported. Methods. Ten human cadaver columns (23–44 years, mean: 34 ± 6.8) were fixed at the ends and targets were inserted to each vertebra to obtain kinematics in axial and coronal planes. The columns were subjected to pure axial twisting moments. Range of motion and neutral zone for primary-axial and coupled-coronal rotation components were determined at each spinal level. Data were analyzed using factorial analysis of variance. Moment-rotation angulations were expressed using logarithmic functions, and mean ±1 standard deviation corridors were derived at each level for both components. Results. Moment-angulations responses were nonlinear. Each segmental curve for both components was well represented by a logarithmic function (r2 > 0.95). Factorial analysis of variance indicated that the biomechanical metrics are spinal level-specific (P < 0.05). Conclusion. Axial and coronal angulations of cervical spinal columns show statistically different level-specific responses. The presentation of moment-angulation corridors for both metrics forms a dataset for the normal population. These segment-specific nonlinear corridors may help clinicians assess dysfunction or instability. These data will assist mathematical models of the spine in improved validation and lead to efficacious design of stabilizing systems.