Network analysis of the whole body kinematics in Parkinson diseases following levodopa intake

Background Three-dimensional motion analysis represents a quantitative and objective approach to assess spatio-temporal and kinematic changes in Parkinson’s disease (PD). However, these parameters, focusing on a specific body segment, provide only segmental information, discarding the complex whole-body patterns underlying the motor impairment. We aimed to assess how levodopa intake affects the whole body, large-scale kinematic network in PD. Methods Borrowing from network theory, we used the kinectome framework by calculating the Pearson’s correlation coefficients between the time series acceleration of 21 bone markers. Then, we performed a topological analysis to evaluate the large-scale interactions between body elements. Finally, we performed a multilinear regression analysis in order to verify whether the kinectome’s topological features could predict the clinical variation before and after levodopa intake. Results PD patients showed lower nodal strength (i.e., lower synchronization) in the upper body in the medio-lateral acceleration while in on-state with respect to the off state (p-head=0.048; p-C7=0.032; p-T10=0.006). On the contrary, PD patients in on state displayed higher nodal strength (i.e., higher synchronization) of both elbows (right, p=0.002; left, p=0.005), wrists (right, p=0.003; left, p=0.002) and knees (right, p=0.003; left, p=0.039) in the antero-posterior acceleration. Furthermore, the predictive analysis revealed that the nodal strength variations of the arms, following levodopa intake, significantly predicted the clinical variations assessed through the UPDRS-III (R2=0.65; p=0.025). Conclusions PD patients in the on-state showed less rigidity during walking, proportional to the UPDRS-III variation. More importantly, we showed that levodopa induces an improvement of the whole body, large-scale kinematic pattern.