Intra- and perioperatively recorded local field potential (LFP) activity of the nucleus subthalamicus (STN) has been suggested to guide contact selection in patients undergoing deep brain stimulation (DBS) for Parkinson’s disease (PD). Despite the invention of sensing capacities in chronically implanted devices, a comprehensible algorithm that enables contact selection using such recordings is still lacking. We evaluated a fully automated algorithm that uses the weighted average of bipolar recordings to determine effective monopolar contacts based on elevated activity in the beta band. LFPs from 14 hemispheres in seven PD patients with newly implanted directional DBS leads of the STN were recorded. First, the algorithm determined the stimulation level with the highest beta activity. Based on the prior determined level, the directional contact with the highest beta activity was chosen in the second step. The mean clinical efficacy of the contacts chosen using the algorithm did not statistically differ from the mean clinical efficacy of standard contact selection as performed in clinical routine. All recording sites were projected into MNI standard space to investigate the feasibility of the algorithm with respect to the anatomical boundaries of the STN. We conclude that the proposed algorithm is a first step towards LFP-based contact selection in STN-DBS for PD using chronically implanted devices.

Clinical rating scales for tremors have significant limitations due to low resolution, high rater dependency, and lack of applicability in outpatient settings. Reliable, quantitative approaches for assessing tremor severity are warranted, especially evaluating treatment effects, e.g., of deep brain stimulation (DBS). We aimed to investigate how different accelerometry metrics can objectively classify tremor amplitude of Essential Tremor (ET) and tremor in Parkinson’s Disease (PD). We assessed 860 resting and postural tremor trials in 16 patients with ET and 25 patients with PD under different DBS settings. Clinical ratings were compared to different metrics, based on either spectral components in the tremorband or pure acceleration, derived from simultaneous triaxial accelerometry captured at the index finger and wrist. Nonlinear regression was applied to a training dataset to determine the relationship between accelerometry and clinical ratings, which was then evaluated in a holdout dataset. All of the investigated accelerometry metrics could predict clinical tremor ratings with a high concordance (>70%) and substantial interrater reliability (Cohen’s weighted Kappa > 0.7) in out-of-sample data. Finger-worn accelerometry performed slightly better than wrist-worn accelerometry. We conclude that triaxial accelerometry reliably quantifies resting and postural tremor amplitude in ET and PD patients. A full release of our dataset and software allows for implementation, development, training, and validation of novel methods.

Background: Stimulation-induced dysarthria (SID) is a troublesome and potentially therapy-limiting side effect of deep brain stimulation of the subthalamic nucleus (STN-DBS) in patients with Parkinson’s disease (PD). To date, the origin of SID, and especially whether there is an involvement of cerebellar pathways as well as the pyramidal tract, remains a matter of debate. Therefore, this study aims to shed light on structural networks associated with SID and to derive a data-driven model to predict SID in patients with PD and STN-DBS.Methods: Randomised, double-blinded monopolar reviews determining SID thresholds were conducted in 25 patients with PD and STN-DBS. A fibre-based mapping approach, implementing the calculation of fibr-wise ORs for SID, was employed to identify the distributional pattern of SID in the STN’s vicinity. The ability of the data-driven model to classify stimulation volumes as ‘causing SID’ or ‘not causing SID’ was validated by calculating receiver operating characteristics (ROC) in an independent out-of-sample cohort comprising 14 patients with PD and STN-DBS.Results: Local fibre-based stimulation maps showed an involvement of fibres running lateral and posteromedial to the STN in the pathogenesis of SID, independent of the investigated hemisphere. ROC analysis in the independent out-of-sample cohort resulted in a good fit of the data-driven model for both hemispheres (area under the curve (AUC)_left=0.88, AUC_right=0.88).Conclusions: This study reveals an involvement of both, cerebello-thalamic fibres, as well as the pyramidal tract, in the pathogenesis of SID in STN-DBS. The results may impact future postoperative programming strategies to avoid SID in patients with PD and STN-DBSTrial registration number: DRKS00023221; German Clinical Trials Register (DRKS) Number.

Gait impairments such as freezing of gait (FOG) are among the most common and disabling symptoms of Parkinson’s disease (PD). While the efficacy of deep brain stimulation (DBS) of the subthalamic nucleus (STN) in patients with early or advanced PD has been proven in large randomised controlled trials, its effect on gait impairments is still insufficiently understood. Moreover, there is uncertainty about pathways that need to be modulated to improve gait impairments. In this bi-centric study, we investigated how STN-DBS alters FOG in 47 subjects with PD. We assessed freezing prevalence and severity using the Freezing of Gait Questionnaire and Item 14 of the Unified Parkinson’s Disease Rating Scale-II. Using a model of publicly available basal-ganglia pathways we determined a connectivity profile for postoperative changes in FOG. Compared to preoperative baseline, freezing of gait significantly improved six months postoperatively, marked by reduced frequency and duration of freezing episodes. We found that optimal stimulation sites for improving freezing of gait structurally connected to primary and supplementary motor areas, the dorsolateral prefrontal cortex and to the globus pallidus. Stimulation of the lenticular fasciculus was associated with worsening of freezing of gait. Our findings highlight the need for optimal identification and characterisation for network structures that can be implemented in stereotactic planning and can additionally pose a target for postoperative stimulation strategies.

Objective: This study aims to shed light on structural networks associated with stimulation-induced dysarthria (SID) and to derive a data-driven model to predict SID in patients with Parkinson’s Disease (PD) and deep brain stimulation (DBS) of the subthalamic nucleus (STN). Methods: Randomized, double-blinded monopolar reviews determining SID thresholds were conducted in 25 patients with PD and STN-DBS. A fiber-based mapping approach, based on the calculation of fiber-wise Odds Ratios for SID, was employed to identify the distributional pattern of SID in the STN’s vicinity. The ability of the data-driven model to classify stimulation volumes as “causing SID” or “not causing SID” was validated by calculating receiver operating characteristics (ROC) in an independent out-of-sample cohort comprising 14 patients with PD and STN-DBS. Results: Local fiber-based stimulation maps showed an involvement of fibers running lateral and postero-medial to the STN in the pathogenesis of SID, independent of the investigated hemisphere. ROC-analysis in the independent out-of-sample cohort resulted in a good fit of the data-driven model for both hemispheres (AUC _left = 0.88, AUC _right = 0.88). Interpretation: This study reveals an involvement of both, cerebello-thalamic fibers, as well as the pyramidal tract, in the pathogenesis of SID in STN-DBS. The results may impact future postoperative programming strategies to avoid SID in patients with PD and STN-DBS.