Parkinson’s disease (PD) is a common neurodegenerative disorder resulting in a range of mobility deficits affecting gait, balance and turning. In this paper, we present: (i) the development and validation of an algorithm to detect turns during gait; (ii) a method to extract turn characteristics; and (iii) the classification of PD using turn characteristics. Thirty-seven people with PD and 56 controls performed 180-degree turns during an intermittent walking task. Inertial measurement units were attached to the head, neck, lower back and ankles. A turning detection algorithm was developed and validated by two raters using video data. Spatiotemporal and signal-based characteristics were extracted and used for PD classification. There was excellent absolute agreement between the rater and the algorithm for identifying turn start and end (ICC ≥ 0.99). Classification modeling (partial least square discriminant analysis (PLS-DA)) gave the best accuracy of 97.85% when trained on upper body and ankle data. Balanced sensitivity (97%) and specificity (96.43%) were achieved using turning characteristics from the neck, lower back and ankles. Turning characteristics, in particular angular velocity, duration, number of steps, jerk and root mean square distinguished mild-moderate PD from controls accurately and warrant future examination as a marker of mobility impairment and fall risk in PD.

Next-generation neurostimulators capable of running closed-loop adaptive deep brain stimulation (aDBS) are about to enter the clinical landscape for the treatment of Parkinson’s disease. Already promising results using aDBS have been achieved for symptoms such as bradykinesia, rigidity and motor fluctuations. However, the heterogeneity of freezing of gait (FoG) with its wide range of clinical presentations and its exacerbation with cognitive and emotional load make it more difficult to predict and treat. Currently, a successful aDBS strategy to ameliorate FoG lacks a robust oscillatory biomarker. Furthermore, the technical implementation of suppressing an upcoming FoG episode in real-time represents a significant technical challenge. This review describes the neurophysiological signals underpinning FoG and explains how aDBS is currently being implemented. Furthermore, we offer a discussion addressing both theoretical and practical areas that will need to be resolved if we are going to be able to unlock the full potential of aDBS to treat FoG.

Background: Real‐world clinical evidence is missing to understand the resorption characteristics of levodopa through duodenal and jejunal parts of the small intestine.Objective: To characterize how different application sites of intestinal levodopa gel would impact on levodopa dosing and clinical outcomes.Methods: This multicentre retrospective analysis investigated Parkinson's disease patients (n = 111) and their change in levodopa equivalent dosage when switching from oral treatment to intestinal continuous infusion therapy while stratifying for differences in percutaneous gastrojejunostomy (PEG‐J) tube localizations. We analyzed data from patients treated with both levodopa–carbidopa (LCIG) and levodopa–carbidopa–entacapone (LECIG) intestinal gel.Results: In dichotomic analysis, duodenal and jejunal tube positions showed similar levodopa equivalent dosages changes from baseline (P = 0.143). This was similar when subdividing patients for LCIG and LECIG treatment. In duodenal PEG‐J positions, 44.4% of patients showed persistent motor fluctuations compared to 21.9% in jejunal placements (P = 0.026). In duodenal positions, fluctuations most often persisted when the PEG‐J tube was placed proximally into the duodenum. In jejunal localizations, several patients displayed a satisfactory outcome from the primary intervention but experienced dislocation of the PEG‐J tube to a duodenal position. This was associated with re‐emergence of motor fluctuations in a majority of them.Conclusions: Our real‐world data suggest that LCIG and LECIG are absorbed similarly in both duodenal and jejunal portions of the small intestine. However, clinical data suggest, that jejunal positioning is critical to the stabilization of dopaminergic motor fluctuations.

Keywords The first case of a patient presenting with Balínt Syndrome caused by a platelet-derived growth factor receptor A mutation (PDGFRA)-induced Hypereosinophilic Syndrome (HES).

Turning is the most common trigger for freezing of gait in people with Parkinson’s disease, posing a substantial threat due to falls and the risk of traumatic injury. Within the basal ganglia, the subthalamic nucleus plays a central role in subthalamo-spinal motor integration, yet the neuronal processes underlying regular turning and freezing while turning have sparsely been investigated, hampering both mechanistic understanding and therapeutic conceptualization for freezing of gait. We recorded subthalamic local field potentials synchronized to gait kinematics in the medication-OFF and stimulation-Off state in 13 Parkinson’s disease patients with freezing of gait. We analysed spectral modulation and beta burst characteristics across locomotor states including regular turning, freezing while turning, unimpaired walking, as well as the transition periods before initiating turning. We found specific subthalamic activation-deactivation abnormalities both before and during freezing while turning, confined to the low beta range (12–20 Hz), which were associated with kinematic abnormalities (“sequence effect”) preceding freezing while turning. Our burst analyses revealed significant state-dependent differences with shorter bursts occurring in quasi-automatic movement during unimpaired walking or the transition period of regular turns, whereas freezing while turning was characterized by prolonged bursts. Guided by these neuronal and kinematic insights, we trained a generalized linear mixed-effects logistic regression model based on neuronal oscillatory (burst duration) and kinematic features (mean angular velocity, stride length) extracted from both the pre-turn transition and subsequent turning phase. Classification performance was quantified using leave-one-subject-out cross-validation, showing good discriminatory performance (accuracy 70 ± 19%, AU-ROC: 0.84 ± 0.14) in differentiating between regular turning from freezing while turning. Our findings suggest that defective supraspinal-motor integration precedes and further drives freezing while turning. Beyond these pathophysiological insights, our results may inform the conceptualization of future adaptive neuromodulation therapies.