Gait Parameters in Parkinson’s Patient Reflect Changes in Subthalamic LFPs
M.A. Greuter (TU Delft - Mechanical Engineering)
M.L. Van De Ruit – Mentor (TU Delft - Biomechatronics & Human-Machine Control)
Martijn Beudel – Mentor (Amsterdam UMC)
Deborah Hubers – Mentor (Amsterdam UMC)
F. C.T. van der Helm – Graduation committee member (TU Delft - Biomechatronics & Human-Machine Control)
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Abstract
Purpose: Parkinson's Disease (PD) is the second most common neurodegenerative disease with a still increasing incidence. The implementation of new medical technology also increases yearly, to achieve better and a more efficient healthcare. One implementation of such a corresponding medical technology is Medtronic's sensing technology, which allows for reading of Local Field Potentials (LFPs). Furthermore, new assessment options are also investigated, with Markerless Motion Tracking (MMT) programs as interesting option for assessment of the Unified Parkinson's Disease Rate Scale (UDPRS). This study aims to investigate correlations between these LFP signals and parameters extracted from MMT programs during gait.
Methods: Pose estimation in this study was performed using MMT programs, while simultaneously recording LFP data in PD patients implanted with a Deep Brain Stimulation (DBS) device. LFP activity was filtered to only include beta activity, while this is primarily correlated with motor impairment. Normalisation methods were then applied on pose estimation data for allowance of distance calculation and extraction of the arm-swing parameters: velocity, acceleration and jerk.
Results: Results indicate a negative trend between LFP data and among almost all examined parameters. This applies for both trends observed: beta power analysis, as well as the UPDRS analysis. Left hemisphere shows significant correlation for the velocity (rho = -0.356, p = 0.046), acceleration (rho = -0.456, p = 0.01) and jerk (rho = -0.465, p = 0.01). While right hemisphere does not show this significance. Whereas, amplitude calculations even show contrary outcomes.
Conclusion: This study shows multiple connections between LFP data and gait parameters. Furthermore, it confirms the importance of arm-swing as indication for gait abnormalities. Finally, these findings suggest the need for more research on other parameters originated from different UPDRS tasks.