EEG-based biomarkers for optimizing deep brain stimulation contact configuration in Parkinson's disease

Objective: Subthalamic deep brain stimulation (STN-DBS) is a neurosurgical therapy to treat Parkinson's disease (PD). Optimal therapeutic outcomes are not achieved in all patients due to increased DBS technological complexity; programming time constraints; and delayed clinical response of some symptoms. To streamline the programming process, biomarkers could be used to accurately predict the most effective stimulation configuration. Therefore, we investigated if DBS-evoked potentials (EPs) combined with imaging to perform prediction analyses could predict the best contact configuration.Methods: In 10 patients, EPs were recorded in response to stimulation at 10 Hz for 50 s on each DBS-contact. In two patients, we recorded from both hemispheres, resulting in recordings from a total of 12 hemispheres. A monopolar review was performed by stimulating on each contact and measuring the therapeutic window. CT and MRI data were collected. Prediction models were created to assess how well the EPs and imaging could predict the best contact configuration.Results: EPs at 3 ms and at 10 ms were recorded. The prediction models showed that EPs can be combined with imaging data to predict the best contact configuration and hence, significantly outperformed random contact selection during a monopolar review.Conclusion: EPs can predict the best contact configuration. Ultimately, these prediction tools could be implemented into daily practice to ease the DBS programming of PD patients.

Automated summary

This study aims to investigate whether combining DBS-evoked potentials (EPs) with imaging can predict the optimal contact configuration for deep brain stimulation (DBS) in patients with Parkinson's disease (PD). The results showed that EPs can accurately predict the best contact configuration, outperforming random contact selection. These prediction tools could potentially simplify the programming of DBS for PD patients.