Subthalamic deep brain stimulation (DBS) is an established therapy to treat Parkinson’s disease (PD). To maximize therapeutic outcome, optimal DBS settings must be carefully selected for each patient. Unfortunately, this is not always achieved because of: (1) increased technological complexity of DBS devices, (2) time restraints, or lack of expertise, and (3) delayed therapeutic response of some symptoms. Biomarkers to accurately predict the most effective stimulation settings for each patient could streamline this process and improve DBS outcomes.
To investigate the use of evoked potentials (EPs) to predict clinical outcomes in PD patients with DBS.
In ten patients (12 hemispheres), a monopolar review was performed by systematically stimulating on each DBS contact and measuring the therapeutic window. Standard imaging data were collected. EEG-based EPs were then recorded in response to stimulation at 10 Hz for 50 s on each DBS-contact. Linear mixed models were used to assess how well both EPs and image-derived information predicted the clinical data.
Evoked potential peaks at 3 ms (P3) and at 10 ms (P10) were observed in nine and eleven hemispheres, respectively. Clinical data were well predicted using either P3 or P10. A separate model showed that the image-derived information also predicted clinical data with similar accuracy. Combining both EPs and image-derived information in one model yielded the highest predictive value.
Evoked potentials can accurately predict clinical DBS responses. Combining EPs with imaging data further improves this prediction. Future refinement of this approach may streamline DBS programming, thereby improving therapeutic outcomes.