Modeling and optimizing deep brain stimulation to enhance gait in Parkinson’s disease: personalized treatment with neurophysiological insights

Abstract

The effects of deep brain stimulation (DBS) on gait in Parkinson’s disease (PD) are variable due to challenges in gait assessment and limited understanding of stimulation parameters’ impacts on neural activity. We developed a data-driven approach to identify optimal DBS parameters to improve gait and uncover neurophysiological signatures of gait enhancement. Field potentials from the globus pallidus (GP) and motor cortex were recorded in three patients with PD (PwP) using implanted bidirectional neural stimulators during overground walking. We developed a Walking Performance Index (WPI) to assess gait metrics. DBS parameters were systematically varied to study their impacts on gait and neural dynamics. We were able to predict and identify personalized DBS settings that improved the WPI using a Gaussian Process Regressor. Improved walking correlated with reduced pallidal beta power during key gait phases. These findings, along with identified person-specific neural spectral biomarkers, underscore the importance of personalized, data-driven interventions for gait enhancement in PwP. ClinicalTrials.gov registration: NCT-03582891.