Probabilistic Subthalamic Nucleus Stimulation Sweet Spot Integration Into a Commercial Deep Brain Stimulation Programming Software Can Predict Effective Stimulation Parameters.

OBJECTIVES: Subthalamic nucleus (STN) deep brain stimulation (DBS) programming in patients with Parkinson disease (PD) may be challenging, especially when using segmented leads. In this study, we integrated a previously validated probabilistic STN sweet spot into a commercially available software to evaluate its predictive value for clinically effective DBS programming. MATERIALS AND METHODS: A total of 14 patients with PD undergoing bilateral STN DBS with segmented leads were included. A nonlinear co-registration of a previously defined probabilistic sweet spot onto the manually segmented STN was performed together with lead reconstruction and tractography of the corticospinal tract (CST) in each patient. Contacts were ranked (level and direction), and corresponding effect and side-effect thresholds were predicted based on the overlap of the volume of activated tissue (VTA) with the sweet spot and CST. Image-based findings were correlated with postoperative clinical testing results during monopolar contact review and chronic stimulation parameter settings used after 12 months. RESULTS: Image-based contact prediction showed high interrater reliability (Cohen kappa 0.851-0.91). Image-based and clinical ranking of the most efficient ring level and direction of stimulation were matched in 72% (95% CI 57.0-83.3) and 65% (95% CI 44.9-81.2), respectively, across the whole cohort. The mean difference between the predicted and clinically observed effect thresholds was 0.79 ± 0.69 mA (p = 0.72). The median difference between the predicted and clinically observed side-effect thresholds was -0.5 mA (p < 0.001, Wilcoxon paired signed rank test). CONCLUSIONS: Integration of a probabilistic STN functional sweet spot into a surgical programming software shows a promising capability to predict the best level and directional contact(s) as well as stimulation settings in DBS for PD and could be used to optimize programming with segmented lead technology. This integrated image-based programming approach still needs to be evaluated on a bigger data set and in a future prospective multicenter cohort.

Combining Multimodal Biomarkers to Guide Deep Brain Stimulation Programming in Parkinson Disease.

BACKGROUND: Deep brain stimulation (DBS) programming of multicontact DBS leads relies on a very time-consuming manual screening procedure, and strategies to speed up this process are needed. Beta activity in subthalamic nucleus (STN) local field potentials (LFP) has been suggested as a promising marker to index optimal stimulation contacts in patients with Parkinson disease. OBJECTIVE: In this study, we investigate the advantage of algorithmic selection and combination of multiple resting and movement state features from STN LFPs and imaging markers to predict three relevant clinical DBS parameters (clinical efficacy, therapeutic window, side-effect threshold). MATERIALS AND METHODS: STN LFPs were recorded at rest and during voluntary movements from multicontact DBS leads in 27 hemispheres. Resting- and movement-state features from multiple frequency bands (alpha, low beta, high beta, gamma, fast gamma, high frequency oscillations [HFO]) were used to predict the clinical outcome parameters. Subanalyses included an anatomical stimulation sweet spot as an additional feature. RESULTS: Both resting- and movement-state features contributed to the prediction, with resting (fast) gamma activity, resting/movement-modulated beta activity, and movement-modulated HFO being most predictive. With the proposed algorithm, the best stimulation contact for the three clinical outcome parameters can be identified with a probability of almost 90% after considering half of the DBS lead contacts, and it outperforms the use of beta activity as single marker. The combination of electrophysiological and imaging markers can further improve the prediction. CONCLUSION: LFP-guided DBS programming based on algorithmic selection and combination of multiple electrophysiological and imaging markers can be an efficient approach to improve the clinical routine and outcome of DBS patients.

Deep Brain Stimulation for Chronic Cluster Headache: Meta-Analysis of Individual Patient Data.

OBJECTIVE: Deep brain stimulation (DBS) is a treatment option for refractory chronic cluster headache (CCH). Despite several recent prospective case series reporting a good outcome, the effectiveness and the optimal stimulation target of DBS for CCH remain unclear. We aimed to obtain precise estimates and predictors of long-term pain relief in an individual patient data meta-analysis. Furthermore, we aimed to construct a probabilistic stimulation map of effective DBS. METHODS: We invited investigators of published cohorts of patients undergoing DBS for CCH, identified by a systematic review of MEDLINE from inception to Febuary 15, 2019, to provide individual patient data on baseline covariates, pre- and postoperative headache scores at median (12-month) and long-term follow-up, in addition to individual imaging data to obtain individual electrode positions. We calculated a stimulation map using voxel-wise statistical analysis. We used multiple regression analysis to estimate predictors of pain relief. RESULTS: Among 40 patients from four different cohorts representing ~50% of all previously published cases, we found a significant 77% mean reduction in headache attack frequency over a mean follow-up of 44 months, with an overall response rate of 75%. Positive outcome was not associated with baseline covariates. We identified 2 hotspots of stimulation covering the midbrain ventral and retrorubral tegmentum. INTERPRETATION: This study supports the hypothesis that DBS provides long-term pain relief for the majority of CCH patients. Our stimulation map of the region of influence of therapeutic DBS identified an optimal anatomical target site that can help surgeons to guide their surgical planning in the future. ANN NEUROL 2020;88:956-969.

Deep Brain Stimulation: When to Test Directional?

Background: Directional deep brain stimulation (DBS) allows for steering of the stimulation field, but extensive and time-consuming testing of all segmented contacts is necessary to identify the possible benefit of steering. It is therefore important to determine under which circumstances directional current steering is advantageous. Methods: Fifty two Parkinson's disease patients implanted in the STN with a directional DBS system underwent a standardized monopolar programming session 5 to 9 months after implantation. Individual contacts were tested for a potential advantage of directional stimulation. Results were used to build a prediction model for the selection of ring levels that would benefit from directional stimulation. Results: On average, there was no significant difference in therapeutic window between ring-level contact and best directional contact. However, according to our standardized protocol, 35% of the contacts and 66% of patients had a larger therapeutic window under directional stimulation compared to ring-mode. The segmented contacts warranting directional current steering could be predicted with a sensitivity of 79% and a specificity of 57%. Conclusion: To reduce time required for DBS programming, we recommend additional directional contact testing initially only on ring-level contacts with a therapeutic window of less than 2.0 mA.