Programming of subthalamic nucleus deep brain stimulation for Parkinson’s disease with sweet spot-guided parameter suggestions

Abstract

Deep Brain Stimulation (DBS) is an effective treatment for advanced Parkinson’s disease. However, identifying stimulation parameters, such as contact and current amplitudes, is time-consuming based on trial and error. Directional leads add more stimulation options and render this process more challenging with a higher workload for neurologists and more discomfort for patients. In this study, a sweet spot-guided algorithm was developed that automatically suggested stimulation parameters. These suggestions were retrospectively compared to clinical monopolar reviews. A cohort of 24 Parkinson’s disease patients underwent bilateral DBS implantation in the subthalamic nucleus at our center. First, the DBS’ leads were reconstructed with the open-source toolbox Lead-DBS. Second, a sweet spot for rigidity reduction was set as the desired stimulation target for programming. This sweet spot and estimations of the volume of tissue activated were used to suggest (i) the best lead level, (ii) the best contact, and (iii) the effect thresholds for full therapeutic effect for each contact. To assess these sweet spot-guided suggestions, the clinical monopolar reviews were considered as ground truth. In addition, the sweet spot-guided suggestions for best lead level and best contact were compared against reconstruction-guided suggestions, which considered the lead location with respect to the subthalamic nucleus. Finally, a graphical user interface was developed as an add-on to Lead-DBS and is publicly available. With the interface, suggestions for all contacts of a lead can be generated in a few seconds. The accuracy for suggesting the best out of four lead levels was 56%. These sweet spot-guided suggestions were not significantly better than reconstruction-guided suggestions (p = 0.3). The accuracy for suggesting the best out of eight contacts was 41%. These sweet spot-guided suggestions were significantly better than reconstruction-guided suggestions (p < 0.001). The sweet spot-guided suggestions of each contact’s effect threshold had a mean error of 1.2 mA. On an individual lead level, the suggestions can vary more with mean errors ranging from 0.3 to 4.8 mA. Further analysis is warranted to improve the sweet spot-guided suggestions and to account for more symptoms and stimulation-induced side effects.

A Retrospective Comparison of Multiple Approaches to Anatomically Informed Contact Selection in Subthalamic Deep Brain Stimulation for Parkinson’s Disease

“…Correspondingly, we found that even though the dorsal border of the STN limited our VISUAL-A approach based on individual T2-contrast in subject space, the core region of the n-map in MNI space widely surpassed the dorsal border of the atlas STN with about 30% located in the extranuclear compartment. Previous studies comparing the results of monopolar reviews and visual contact selection already reported higher levels of agreement for a visual approach compared to automatic sweet spot targeting, which might implicate a corrective impact of human interpretation during visual assessment [46,47]. We conclude that further development beyond the applied methodology is necessary to translate insights from probabilistic sweet spots to the individual patient.…”

“…Local voltage gradients are expected to be continuous and heterogeneous affecting neuron populations differently depending on myelinization and fiber orientation [48,49]. Correspondingly, Pavese et al reported frequent subsequent adjustments of amplitude suggestions based on VTA modeling, and Nordenström et al reported that clinical effect thresholds did not correlate well with predictions based on VTA modelling [46,47]. While probabilistic sweet spot mapping allows for the deduction of the desired position of the electric field, it does not provide sufficient information regarding the optimal extent of the electrical field, respectively, the necessary current in individual patients.…”

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

“…Imaging is also used to visualize the spread of electrical stimulation induced by DBS, such as the electric field (EF), to guide programming ( Hemm et al, 2005 ; Åström et al, 2009 ; Nguyen et al, 2019 ; Lange et al, 2021 ). When pooling individual patient data, one can create so-called clinical ‘sweet spots’, which are probabilistic stimulation maps predictive of good motor outcomes ( Dembek et al, 2019 ) and are already being used to guide programming ( Phibbs et al, 2014 ; Nordenström et al, 2022 ).…”