IntroductionPrevious studies have shown that subthalamic nucleus (STN) and unilateral globus pallidus interna (GPi) are similarly effective in the deep brain stimulation (DBS) treatment of motor symptoms. However, the counterintuitively more common clinical application of STN DBS makes us hypothesize that STN is superior to GPi in the treatment of motor symptoms.MethodsIn this prospective, double-blind, randomized crossover study, idiopathic PD patients treated with combined unilateral STN and contralateral GPi DBS (STN in one brain hemisphere and GPi in the other) for 2 to 3 years were enrolled. The MDS UPDRS-III total score and subscale scores for axial and bilateral limb symptoms were assessed preoperatively and at 2- to 3-year follow-up in four randomized, double-blinded conditions: (1) Med–STN+GPi–, (2) Med–STN–GPi+, (3) Med+STN+GPi–, and (4) Med+STN–GPi+.ResultsEight patients had completed 30 trials of assessment. Compared with the preoperative Med– state, in the Med–STN+GPi– condition, the cardinal symptoms in both sides of the body were all improved. In the Med–STN–GPi+ condition, symptoms of the GPi-stim limb were improved, while only tremor was improved on the ipsilateral side, although all axial symptoms showed aggravation. Compared with the preoperative Med+ state, in the Med+STN+GPi– state, cardinal symptoms were improved on both sides, except that tremor was worsened on the STN-stim side. In the Med+STN–GPi+ state, the overall motor symptoms were aggravated compared with the preoperative Med+ state. Most axial symptoms worsened at acute unilateral STN or GPi DBS onset, compared to both preoperative Med– and Med+ states. No side effects associated with this study were seen.ConclusionsImprovement in motor symptoms was greater in all sub-scores favoring STN. The effects of STN+ were seen on both sides of the body, while GPi+ mainly acted on the contralateral side.

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ObjectiveWe aimed to compare the motor effect of bilateral globus pallidus interna (GPi) deep brain stimulation (DBS) on motor subtypes of Parkinson’s disease (PD) patients and identify preoperative predictive factors of short-term motor outcome.MethodsWe retrospectively investigated bilateral GPi DBS clinical outcomes in 55 PD patients in 1 year follow up. Motor outcome was measured by the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) part III before and 1 year after surgery. Clinical outcomes were compared among different motor subtypes. Preoperative predictors of motor outcome were assessed by performing univariate and multivariate linear regression and logistic regression analyses.ResultsAt 1 year following implantation, GPi DBS significantly improved the off-medication MDS-UPDRS III scores in all motor subtype cohorts, with prominent improvement in tremor. No significant difference of postoperative motor symptoms changes was found except greater tremor improvement achieved in both the tremor-dominant (TD) and indeterminate (IND) patients compared to the postural instability and gait difficulty (PIGD) patients. High percentage of PIGD patients were weak responders to DBS. Better levodopa responsiveness and more severe tremor predicted greater overall improvement of motor function in the entire cohort. Similarly, both levodopa responsiveness and tremor improvement were confirmed as predictors for motor improvement in PIGD patients.ConclusionBilateral GPi DBS could effectively improve motor outcomes in PD patients regardless of motor subtypes. Both TD and IND patients obtained larger tremor improvement. The intensity of levodopa responsiveness and the severity of tremor could serve as predictors of motor improvement 1 year after GPi DBS.

Background and objectivesThe Movement Disorder Society’s Unified Parkinson’s Disease Rating Scale Part III (MDS-UPDRS III) is mostly common used for assessing the motor symptoms of Parkinson’s disease (PD). In remote circumstances, vision-based techniques have many strengths over wearable sensors. However, rigidity (item 3.3) and postural stability (item 3.12) in the MDS-UPDRS III cannot be assessed remotely since participants need to be touched by a trained examiner during testing. We developed the four scoring models of rigidity of the neck, rigidity of the lower extremities, rigidity of the upper extremities, and postural stability based on features extracted from other available and touchless motions.MethodsThe red, green, and blue (RGB) computer vision algorithm and machine learning were combined with other available motions from the MDS-UPDRS III evaluation. A total of 104 patients with PD were split into a train set (89 individuals) and a test set (15 individuals). The light gradient boosting machine (LightGBM) multiclassification model was trained. Weighted kappa (k), absolute accuracy (ACC ± 0), and Spearman’s correlation coefficient (rho) were used to evaluate the performance of model.ResultsFor model of rigidity of the upper extremities, k = 0.58 (moderate), ACC ± 0 = 0.73, and rho = 0.64 (moderate). For model of rigidity of the lower extremities, k = 0.66 (substantial), ACC ± 0 = 0.70, and rho = 0.76 (strong). For model of rigidity of the neck, k = 0.60 (moderate), ACC ± 0 = 0.73, and rho = 0.60 (moderate). For model of postural stability, k = 0.66 (substantial), ACC ± 0 = 0.73, and rho = 0.68 (moderate).ConclusionOur study can be meaningful for remote assessments, especially when people have to maintain social distance, e.g., in situations such as the coronavirus disease-2019 (COVID-19) pandemic.